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Influencers are people on social media that distinguish themselves by the high number of followers and the ability to influence other users. While influencers are a long-standing phenomenon in social media, virtual influencers have made their appearance on such platforms only recently: they are CGI characters that act like and resemble humans, even if they do not physically exist in the real world. This recent phenomenon has sparked interest in society, and several questions arise regarding their evolution, opinions, ethics, purpose in marketing, and future perspective. In this article, we conduct an exhaustive review of the virtual influencer phenomenon. Through an extensive study of the literature, press articles, social platforms data, blogs, and interviews, we give a comprehensive reflection on virtual influencers. Starting from their evolution, we analyze their opportunities and threats. We provide detailed information about the most popular ones and their marketing collaborations, with a comparative analysis of virtual and real (human) influencers. Moreover, we conducted an online survey to grasp people's perspectives. From the 360 participants' answers, we draw conclusions about virtual influencers' ethics, importance, overall feelings, and future. Results show controversial opinions on this recent phenomenon. [ABSTRACT FROM AUTHOR]

Nowadays, online services, like e-commerce or streaming services, provide a personalized user experience through recommender systems. Recommender systems are built upon a vast amount of data about users/items acquired by the services. Such knowledge represents an invaluable resource. However, commonly, part of this knowledge is public and can be easily accessed via the Internet. Unfortunately, that same knowledge can be leveraged by competitors or malicious users. The literature offers a large number of works concerning attacks on recommender systems, but most of them assume that the attacker can easily access the full rating matrix. In practice, this is never the case. The only way to access the rating matrix is by gathering the ratings (e.g., reviews) by crawling the service's website. Crawling a website has a cost in terms of time and resources. What is more, the targeted website can employ defensive measures to detect automatic scraping. In this paper, we assess the impact of a series of attacks on recommender systems. Our analysis aims to set up the most realistic scenarios considering both the possibilities and the potential attacker's limitations. In particular, we assess the impact of different crawling approaches when attacking a recommendation service. From the collected information, we mount various profile injection attacks. We measure the value of the collected knowledge through the identification of the most similar user/item. Our empirical results show that while crawling can indeed bring knowledge to the attacker (up to 65% of neighborhood reconstruction on a mid-size dataset and up to 90% on a small-size dataset), this will not be enough to mount a successful shilling attack in practice. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) employs a large number of sensors and actuators to collect and act upon data for its smart functionalities. These devices are considered as a part of the Low-power and Lossy Networks due to their use of low power embedded hardware and computationally constrained nature. For synchronization and utility, these devices are often clubbed together logically to form groups. To maintain data confidentiality within a group, a shared symmetric key called the Group Key (GK) is used by all the group members. The GK must be redistributed upon joining and leaving of a group member to maintain forward and backward secrecy. However, the key management (i.e., generation and distribution) process causes overhead which consumes the scarce network resources. In this paper, we propose TARE, a novel Topology Adaptive Re-kEying (TARE) scheme for lightweight and secure group communication. TARE integrates the principles of routing tree mapped logical key tree and local derivation of the key over the an IPv6 Routing Protocol for low-power and Lossy networks in an original way. TARE takes into consideration the current routing topology and makes maximum energy reduction as the premise for its choice of key derivation and distribution methods, thus, it reduces the network energy consumption while maintaining key secrecy and data confidentiality. In particular, TARE provides the following advantages: (1) lower network overhead and bandwidth utilization in key management and re-distribution operations, (2) effective against the network mobility in scalable IoT networks, (3) secure group communications in network against attacks such as man-in-the-middle and eavesdropping, and (4) data confidentiality by ensuring backward and forward secrecy in key distribution method. We evaluate the performance of TARE and compare it with existing schemes. Our results show the effectiveness of TARE regarding energy consumption, bandwidth utilization, and the number of encrypted message transmissions during the re-keying operations. [ABSTRACT FROM AUTHOR]

A distributed denial of service (DDoS) attack on any of the major components (e.g., controller, switches, and southbound channel) of software defined networking (SDN) architecture is a critical security threat. For example, the breakdown of controller could disrupt the data communication in the whole SDN network. A possible way to perform DoS is to generate a large number of new, but short length traffic flows. These flows will trigger malicious flooding requests to overload the controller and causes overflow in flow tables at SDN switches. In this paper, we propose two lightweight and practically feasible countermeasures against two different types of DDoS attacks called Route Spoofing and Resource Exhaustion in SDN networks. For Route Spoofing attack, we introduce a technique called "selective blocking", which stops an adversary node from maliciously using other users active communication routes. To countermeasure Resource Exhaustion attack, we propose a solution called "periodic monitoring", which detects adversary nodes based on the traffic analysis statistics that are gathered within a time window. We implement and perform result analysis of the attacks and their proposed countermeasures. When using our proposed countermeasures in the target SDN scenarios, the simulation results indicate an adequate reduction in bandwidth consumption and processing delay of new request, and it also depicts substantial gain in packet delivery rate. Additionally, we present the receiver operating characteristic curve, which shows the sensitivity and specificity of our countermeasures along with their detection accuracy. [ABSTRACT FROM AUTHOR]

Malware is often hidden in illegitimately cloned software. Android, with over two billions active devices, is one of the most affected platforms because code cloning is quite simple and there are several not controlled markets. Obfuscation is both a cause and a solution to this scenario: a cause because obfuscated malware is harder to detect, a solution because obfuscation of legitimate applications makes code cloning more difficult. A deeper understanding of the obfuscation techniques would lead to more effective and aware use. In the literature, there are few methods of obfuscation detection with limited accuracy. Manual reverse engineering is too time-consuming to achieve this purpose, we need faster and automated techniques. In this work, we propose several deep learning models that can detect and classify the presence of obfuscation in Android applications. In addition to classical ML methods, we leverage natural language processing or image recognition approaches, then with a hybrid model, we exploit the best of each approach. Tests over a large dataset, made using different obfuscation tools, showed improvements compared to previous obfuscation detection methods. We target four obfuscation classes: identifier renaming, string encryption, reflection and class encryption, achieving an average F-measure of 0.985. [ABSTRACT FROM AUTHOR]

Recent energy measurements on smartphones have shown that parallel communications (e.g., data transfer and voice call) require less energy than their stand-alone execution. Guided by these results, we investigate the possibility of scheduling communications in pairs for minimizing the energy consumption. We define two energy optimization problems to postpone delay-tolerant services and perform them in parallel with real-time services in order to save energy. The first problem, called single delay-tolerant assignment (SDA), allows at most one delay-tolerant service to be paired with each real-time service, whereas the second problem, called multiple delay-tolerant assignment (MDA), allows multiple delay-tolerant services to be paired (in different times) with the same real-time service. For the SDA problem, we propose an optimal algorithm. For the MDA problem, which is computationally intractable, we give an approximation algorithm. We evaluate the benefits of the energy-efficient pairing strategy via simulations on synthetic traces. The MDA algorithm can save up to the 60 percent of the energy consumption using 4G network assuming an intensive smartphone usage, while the SDA algorithm saves up to the 20 percent. [ABSTRACT FROM AUTHOR]

Nowadays, the number of Distributed Denial of Service (DDoS) attacks is growing rapidly. The aim of these type of attacks is to make the prominent and critical services unavailable for legitimate users. HTTP flooding is one of the most common DDoS attacks and because of its implementation in application layer, it is difficult to detect and prevent by the current defense mechanisms. This attack not only makes the web servers unavailable, but consumes the computational resources of the network equipment and congests communication links. Recently, the advent of Software Defined Networking (SDN) paradigm has enabled the network providers to detect and mitigate application layer DDoS attacks such as HTTP flooding. In this paper, we propose a defense mechanism named HTTPScout which leverages the benefits of SDN together with Machine Learning (ML) techniques to detect and mitigate HTTP flooding attack. HTTPScout is implemented as a security module in RYU controller and monitors the behavior of HTTP traffic flows. Upon detecting a malicious flow, it blocks the source of the attack at the edge switch and preserves the network resources from the adversarial effects of the attack. Simulation results confirm that HTTPScout brings a significant improvement of 64% in bandwidth consumption and 80% in the number of forwarding rules compared to normal SDN. [ABSTRACT FROM AUTHOR]

Enforcing security often requires the two legitimate parties of a communication to determine if they share a secret, without disclosing information (e.g., the shared secret itself, or just the existence of such a secret) to third parties. In this paper, we propose CASSANDRA, a toolbox composed of three probabilistic protocols that allows two parties, each one having a subset of elements drawn by a pre-determined set, to compute information about the intersection of such two sets. In particular, C-void decides whether the two sets are disjoint; C-size allows to compute how many elements the intersection is composed of; and, C-set returns the identity of the elements of the intersection (if any). These protocols differ, other than in functionality, also in the degree of assurance they can provide and the degree of interactions required by the two parties. The communication cost also differs, but in any case, it is below the cost of competing solution representing the state of the art. These protocols also share some common features: that is, they are completely tunable and specifically suited for devices having constraints on energy, communication, storage, and bandwidth. Examples of these devices are portable devices (e.g., phones) handling satellite communications, or nodes of wireless sensor networks. Thorough analysis and extensive simulations support our findings. [ABSTRACT FROM AUTHOR]

Abstract: In-network data aggregation is favorable for wireless sensor networks (WSNs): It allows in-network data processing while reducing the network traffic and hence saving the sensors energy. However, due to the distributed and unattended nature of WSNs, several attacks aiming at compromising the authenticity of the collected data could be perpetrated. For example, an adversary could capture a node to create clones of the captured one. These clones disseminated through the network could provide malicious data to the aggregating node, thus poisoning/disrupting the aggregation process. In this paper we address the problem of detecting cloned nodes; a requirement to be fulfilled to provide authenticity of the data fusion process. First, we analyze the desirable properties a distributed clone detection protocol should meet. Specifically: It should avoid having a single point of failure; the load should be totally distributed across the nodes in the network; the position of the clones in the network should not influence the detection probability. We then show that current solutions do not meet the exposed requirements. Next, we propose the Information Fusion Based Clone Detection Protocol (ICD). ICD is a probabilistic, completely distributed protocol that efficiently detects clones. ICD combines two cryptographic mechanisms: The pseudo-random key pre-distribution, usually employed to secure node pairwise communications, with a sparing use of asymmetric crypto primitives. We show that ICD matches all the requirements above mentioned and compare its performance with current solutions in the literature; experimental results show that ICD has better performance than existing solutions for all the cost parameters considered: Number of messages sent, per sensor storage requirement, and signature verification. These savings allow to increase the network operating lifetime. Finally, note that ICD protocol could be used as an independent layer by any data aggregation mechanism. [Copyright &y& Elsevier]

The Android application framework has a pervasive presence. In early 2021, Android has over 70 % share of the operating system mobile market (according to GlobalStats). Components are the main building blocks of Android Applications. These blocks communicate via a rich Inter-Component Communication (ICC) model rather than the traditional inter-process communication model. Intents, Intent-filters, and their Intents resolution (matching) algorithm are main elements of the ICC. However, the Intent resolution algorithm is not robust enough and has flaws that can lead to security breaches. In this paper, we present DLAIR, as an enrichment of the Intent resolution algorithm to overcome its security issues. To this end, we start by presenting a formal model to express and validate the ICC semantics. This includes defining key properties guaranteeing consistent and realistic semantic states. We then demonstrate how the semantics can be used to formally validate ICC aspects and to express and check ICC system updates. We verified our proposed model and all its lemmas and theorems in the Coq Proof Assistant, a machine-assisted verification tool. We extend our semantics to develop DLAIR which is assisted by a heuristic, and lightweight tool, LekInt. This tool identifies suspicious execution paths responsible for intent based sensitive user-information leakage. On a dataset of 2000 real-world apps, we evaluated LekInt against Flowdroid, a state-of-the-art information leakage analysis tool. Experiments show that LekInt is more effective and efficient than Flowdroid which has a higher false-negative rate and lower false-positive rate than LekInt. Considering the dynamic context in which LekInt is designed to work, the advantage of efficiency overcomes the disadvantage of higher false-negative. [ABSTRACT FROM AUTHOR]

Industrial applications and automation controls are closely connected with heterogeneous Wireless Sensor Network (WSN); thus, Industrial Internet-of Thing (IIoT) enhances the productivity, scalability, and flexibility of the operations. However, in many cases such as tracking a device or service localization becomes very crucial to state back the operations at times. Existing literature show a various localization solutions, but they face the drawbacks of high energy consumption, and non-coherence in heterogeneity. Besides, in a WSN-based IIoT, non-cooperativeness exists among nodes due to various system and environmental parameters, which make the system resource-exhaustive. In this paper, we introduce a novel localization algorithm that addresses the above-mentioned problems. We call our proposed algorithm RAnge-free Power efficient COoperative Localization (RAPCOL). Specifically, the use of co-operative beacon nodes to broadcast the information to Base Station and low energy consumption highlights the novelty of the work. RAPCOL uses weight metrics for selecting optimal cooperative beacon nodes to prolong the network lifetime. We also introduce an Improvised Particle Swarm Optimization (IPSO) that credits in the contribution in RAPCOL. We run a overall nine sets of experiments to analyze the localization accuracy, effect of beacon nodes, sensor nodes, network connectivity, and sensing field, error frequency, residual energy, time, and network lifetime. A comparative study with the existing localization models shows that RAPCOL is 30 % better than the existing models in terms of accuracy and resource consumption. We observe stable performance of RAPCOL with a differentiated effect of beacon nodes and sensor nodes. We also observe that our proposed IPSO 20 % better in fast convergence to the optimal solution. Though RAPCOL localization time is 12 % higher than other existing protocols, RAPCOL's accuracy and energy saving mechanism make it efficient for IIoTs. [ABSTRACT FROM AUTHOR]

Large-scale online campaigns, malicious or otherwise, require a significant degree of coordination among participants, which sparked interest in the study of coordinated online behavior. State-of-the-art methods for detecting coordinated behavior perform static analyses, disregarding the temporal dynamics of coordination. Here, we carry out a dynamic analysis of coordinated behavior. To reach our goal, we build a multiplex temporal network and we perform dynamic community detection to identify groups of users that exhibited coordinated behaviors in time. We find that i) coordinated communities (CCs) feature variable degrees of temporal instability; ii) dynamic analyses are needed to account for such instability, and results of static analyses can be unreliable and scarcely representative of unstable communities; iii) some users exhibit distinct archetypal behaviors that have important practical implications; iv) content and network characteristics contribute to explaining why users leave and join CCs. Our results demonstrate the advantages of dynamic analyses and open up new directions of research on the unfolding of online debates, on the strategies of CCs, and on the patterns of online influence. [ABSTRACT FROM AUTHOR]

Summary: In software‐defined networking (SDN), TCP SYN flooding attack is considered as one of the most effective attacks to perform control plane and target server saturation. In this attack, an attacker generates a large number of malicious SYN requests, and because of the absence of the forwarding rules, the data plane switches have to forward these SYN messages to the controller. This excessive forwarding causes congestion over the communication channel between a data plane and control plane, and it also exhausts computational resources at both the planes. In this paper, we propose a novel countermeasure called SYN‐Guard to detect and prevent SYN flooding in SDN networks. We fully implement SYN‐Guard on the SDN controller to validate the incoming TCP connection requests. The controller installs forwarding rules for the SYN requests that successfully clear the validation test of SYN‐Guard. The host of the fake SYN request is detected, and SYN‐Guard prevents it from sending any further SYN requests to the data plane switch. The performance evaluation done using the simulation results shows that SYN‐Guard exhibits low side effect for genuine TCP requests, and when compared with standard SDN and state‐of‐art proposals, it reduces the average response time up to 21% during an ongoing SYN flooding attack. [ABSTRACT FROM AUTHOR]

Video surveillance systems provide means to detect the presence of potentially malicious drones in the surroundings of critical infrastructures. In particular, these systems collect images and feed them to a deep-learning classifier able to detect the presence of a drone in the input image. However, current classifiers are not efficient in identifying drones that disguise themselves with the image background, e.g., hiding in front of a tree. Furthermore, video-based detection systems heavily rely on the image's brightness, where darkness imposes significant challenges in detecting drones. Both these phenomena increase the possibilities for attackers to get close to critical infrastructures without being spotted and hence be able to gather sensitive information or cause physical damages, possibly leading to safety threats. In this article, we propose RANGO, a drone detection arithmetic able to detect drones in challenging images where the target is difficult to distinguish from the background. RANGO is based on a deep learning architecture that exploits a Preconditioning Operation (PREP) that highlights the target by the difference between the target gradient and the background gradient. The idea is to highlight features that will be useful for classification. After PREP, RANGO uses multiple convolution kernels to make the final decision on the presence of the drone. We test RANGO on a drone image dataset composed of multiple already-existing datasets to which we add samples of birds and planes. We then compare RANGO with multiple currently existing approaches to show its superiority. When tested on images with disguising drones, RANGO attains an increase of 6.6% mean Average Precision (mAP) compared to YOLOv5 solution. When tested on the conventional dataset, RANGO improves the mAP by approximately 2.2%, thus confirming its effectiveness also in the general scenario. [ABSTRACT FROM AUTHOR]

Android is currently the most widespread operating system (OS) worldwide, but also the most prone to attacks. Despite the challenges faced by Industry and Academia to improve the Android OS security, it still has several vulnerabilities. Among those, the severity of the Next-Intent Vulnerability (NIV) can be immediately grasped. Android apps are made of components, which by default are private and cannot be targeted by other apps on the same phone. However, NIV allows any app to access the private components of a different app, eventually generating a crash or stealing sensitive data. NIV occurs when there is a chain of calls among different components based on the Intent messaging model and there is no control over the reliability of the first component triggering the call. NIV was first detected in 2013, but it is still an open issue. In this paper, we present Next-Intent Vulnerability Detector ( N I V D ), a novel approach to detect NIV in Android apps by relying on type systems. N I V D applies the inference rules of its type system to the app execution paths containing a sequence of calls to three NIV-related Android APIs. Compared to the state-of-the-art, N I V D is faster and more efficient, without losing precision in detecting NIV. Finally, through N I V D Google Photos was found to be vulnerable, and we disclosed the finding on the Google official bug report website (issue number 124342801). [ABSTRACT FROM AUTHOR]

In the past few years, unmanned aerial vehicles (UAVs) have significantly gained attention and popularity from industry, government, and academia. With their rapid development and deployment into the civilian airspace, UAVs play an important role in different applications, including goods delivery, search‐and‐rescue, and traffic monitoring. Therefore, providing secure communication through authentication models for UAVs is necessary for a successful and reliable flight mission. To satisfy such requirements, numerous authentication mechanisms have been proposed in the literature. However, the literature lacks a comprehensive study evaluating the security and performance of these solutions. In this article, we analyze the security and performance of 27 recent UAV authentication works by considering ten different key metrics. First, in the performance analysis, we show that the majority of UAV authentication schemes are lightweight in their communication cost. However, the storage overhead or the energy consumption is not reported by many authentication studies. Then, we reveal in the security analysis the widely employed formal models (i.e., abstract description of an authentication protocol through a mathematical model), while most of the studies lack coverage of many attacks that can target UAV systems. Afterwards, we highlight the challenges that need to be addressed in order to design and implement secure and reliable UAV authentication schemes. Finally, we summarize the lessons learned on the authentication strategies for UAVs to motivate promising direction for further research. [ABSTRACT FROM AUTHOR]

The idea of Internet of Things (IoT) is that many of the live objects (e.g., appliances) in the network are accessible, sensed, and interconnected. However, energy-constrained IoT nodes limit the performance of the IoT network. Hence, preserving energy in IoT network requires utmost attention. Unequal clustering is commonly considered as one of the efficient energy saving technique. Here, the traffic load is evenly distributed among the nodes using variable size clusters across the network. However, none of the existing solutions considered (1) realistic factors like fading model, routing protocol etc., or (2) optimization of cluster radius while devising clustering structure. The contribution of this paper is two-fold. First, we analyze the maximization of network lifetime by balancing the energy consumption among Cluster Heads (CHs). We found that cluster radius of each level has significant role in maximization of network lifetime. Second, to meet the requirement of maximization of network lifetime, this paper proposes a novel Lifetime Maximizing optimal Clustering Algorithm (LiMCA) for battery-powered IoT devices. Particularly, LiMCA includes a novel stochastic deployment scheme for Member Nodes (MNs) and CHs and a training protocol to train CHs and MNs about their coarse-grain location. Extensive simulation study shows that our algorithm improves the network lifetime by more than 30%, compared to other existing approaches. [ABSTRACT FROM AUTHOR]

In the current decade, doing the search on massive data to find "hidden" and valuable information within it is growing. This search can result in heavy processing on considerable data, leading to the development of solutions to process such huge information based on distributed and parallel processing. Among all the parallel programming models, one that gains a lot of popularity is MapReduce. The goal of this paper is to survey researches conducted on the MapReduce framework in the context of its open-source implementation, Hadoop, in order to summarize and report the wide topic area at the infrastructure level. We managed to do a systematic review based on the prevalent topics dealing with MapReduce in seven areas: (1) performance; (2) job/task scheduling; (3) load balancing; (4) resource provisioning; (5) fault tolerance in terms of availability and reliability; (6) security; and (7) energy efficiency. We run our study by doing a quantitative and qualitative evaluation of the research publications' trend which is published between January 1, 2014, and November 1, 2017. Since the MapReduce is a challenge-prone area for researchers who fall off to work and extend with, this work is a useful guideline for getting feedback and starting research. [ABSTRACT FROM AUTHOR]

This paper studies leakage of user passwords and PINs based on observations of typing feedback on screens or from projectors in the form of masked characters (∗ or ∙) that indicate keystrokes. To this end, we developed an attack called Password and Pin Information Leakage from Obfuscated Typing Videos (PILOT). Our attack extracts inter-keystroke timing information from videos of password masking characters displayed when users type their password on a computer, or their PIN at an ATM. We conducted several experiments in various attack scenarios. Results indicate that, while in some cases leakage is minor, it is quite substantial in others. By leveraging inter-keystroke timings, PILOT recovers 8-character alphanumeric passwords in as little as 19 attempts. When guessing PINs, PILOT significantly improved on both random guessing and the attack strategy adopted in our prior work (In European Symposium on Research in Computer Security (2018) 263–280 Springer). In particular, we were able to guess about 3% of the PINs within 10 attempts. This corresponds to a 26-fold improvement compared to random guessing. Our results strongly indicate that secure password masking GUIs must consider the information leakage identified in this paper. [ABSTRACT FROM AUTHOR]

Abstract Internet-of-Things (IoT) promises to provide services to the end users by connecting physical things around them through Internet. The conventional services build for web are primarily based on the pull technology, where the user actively engages with system to get the services. However, in IoT environment, the services are based on push-based, where information and value added services will be pushed towards the user. Unless, these push-based services are properly managed they would overwhelm the user with unnecessary information, thus, it will soon start annoying the user. In this paper, we propose a lightweight context-aware IoT service architecture namely LISA to support IoT push services in an efficient manner. In particular, LISA filter and forward the most important and relevant services to the users by understanding their context. To achieve its goals, LISA formulates a user model to resolve local decision making by using agents and available web services paradigm. The proposed user model describes the user in an abstract way by considering the context and profile information of the user. For evaluation, we simulate LISA by considering an IoT tourist guide system as a use case scenario, and we show the performance of the our user model concerning precision and recall metrics. The results of our preliminary experiments confirm that LISA successfully reduces the information provided to the user by selecting only the most relevant among those. The evaluation shows that LISA can extract services for a user by selecting from 15000 services with precision upto 0.3 and recall upto 0.8, and it can be further optimized by tuning the user-specific design settings. Additionally, our approach shows improvements in query processing time which also includes the query generation time. [ABSTRACT FROM AUTHOR]

In this paper, we present a behavioral biometric-based smartphone user authentication mechanism, namely, AnswerAuth , which relies on the very common users' behavior. Behavior, here, refers to the way a user slides the lock button on the screen, to unlock the phone, and brings the phone towards her ear. The authentication mechanism works with the biometric behavior based on the extracted features from the data recorded using the built-in smartphone sensors, i.e., accelerometer, gyroscope, gravity, magnetometer and touchscreen, while the user performed sliding and phone-lifting actions. We tested AnswerAuth on a dataset of 10,200 behavioral patterns collected from 85 users while they performed the unlocking actions, in sitting, standing , and walking postures, using six state-of-the-art conceptually different machine learning classifiers in two settings, i.e., with and without simultaneous feature selection and classification. Among all the chosen classifiers, Random Forest (RF) classifier proved to be the most consistent and accurate classifier on both full and reduced features and provided a True Acceptance Rate (TAR) as high as 99.35%. We prototype proof-of-the-concept Android app, based on our findings, and evaluate it in terms of security and usability. Security analysis of AnswerAuth confirms its robustness against the possible mimicry attacks. Similarly, the usability study based on Software Usability Scale (SUS) 1 1 https://www.usability.gov/how-to-and-tools/methods/system-usability-scale.html. questionnaire verifies the user-friendliness of the proposed scheme (SUS Score of 75.11). Experimental results prove AnswerAuth as a secure and usable authentication mechanism. [ABSTRACT FROM AUTHOR]

The IoT extends the idea of interconnecting computers to a plethora of different devices, collectively referred to as smart devices. These are physical items, that is, "things", such as wearable devices, home appliances, and vehicles, enriched with computational and networking capabilities. Due to the huge set of devices involved, and therefore its pervasiveness, IoT is a great platform to leverage for building new applications and services or extending existing ones. In this regard, expanding online advertising into the IoT realm is an under-investigated yet promising research direction, especially considering that the traditional Internet advertising market is already worth hundreds of billions of dollars. In this article, we first propose the architecture of an IoT advertising platform inspired by the well known business ecosystem, which the traditional Internet advertising is based on. Additionally, we discuss the key challenges to implement such a platform, with a special focus on issues related to architecture, advertisement content delivery, security, and privacy of the users. [ABSTRACT FROM AUTHOR]

Routing Protocol for Low-Power and Lossy Networks (RPL) is a standard routing protocol for the Low Power and Lossy Networks (LLNs). It is a part of the IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) protocol stack. Features such as energy-efficient mechanisms and availability of the secure modes of operations make RPL suitable for the constrained Internet of Things (IoT) devices. Hence, the majority of IoT applications rely on RPL for data communication. However, routing security in RPL-based IoT networks is a significant concern, motivating us to study and analyze routing attacks and suggested countermeasures against them. To this end, we provide a comprehensive survey on the state-of-the-art security threats and their corresponding countermeasures in RPL-based IoT networks. Based on our study, we propose a novel classification scheme that uses a mapping between RPL attacks and their countermeasure techniques to the RPL control messages used to develop these techniques. Furthermore, we provide an in-depth statistical analysis that includes analysis of routing attacks through the RPL control messages, distribution of various mitigation techniques as per the method used, RPL control messages involved in the mitigation techniques, and details of the tools used by multiple researchers. In the end, we highlight some open challenges and future research opportunities on this topic. We believe that this survey will be beneficial to researchers and practitioners working in the area of RPL security. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is emerging with the pace of technology evolution, connecting people and things through the Internet. IoT devices enable large-scale data collection and sharing for a wide range of applications. However, it is challenging to securely manage interconnected IoT devices because the collected data could contain sensitive personal information. The authors believe that attribute-based encryption (ABE) could be an effective cryptographic tool for secure management of IoT devices. However, little research has addressed ABE's actual feasibility in the IoT thus far. This article investigates such feasibility considering well-known IoT platforms--specifically, Intel Galileo Gen 2, Intel Edison, Raspberry Pi 1 Model B, and Raspberry Pi Zero. A thorough evaluation confirms that adopting ABE in the IoT is indeed feasible. [ABSTRACT FROM AUTHOR]

Malware remains a persistent threat to industrial operations, causing disruptions and financial losses. Traditional malware detection approaches struggle with the increasing complexity of false positives and negatives. However, existing Intrusion Detection Systems (IDSs) often lack the capability to assess the severity of detected malware, crucial for effective threat mitigation. This paper presents a novel model, MAlware DEtection and Severity Analysis for eNcrypted Traffic (MADESANT), designed to detect and analyze malware severity in encrypted traffic data. MADESANT combines Deep Learning (DL)-based intrusion detection with Machine Learning (ML)-based severity analysis, specifically customized for the minutiae of IoT systems and assets. Notably, MADESANT introduces a cascading model integrating a Cascading Forward Back Propagation Neural Network (CFBPNN) with the J48 tree to systematically assess risk factors in network traffic. Our assessment, conducted on diverse encrypted datasets including UNSW-NB15, IoT23, and XIIoTID, highlights the remarkable efficacy of MADESANT. Impressively, it achieves a flawless 0% false positive rate in detecting binary attack instances, surpassing benchmarks set by conventional models. Additionally, MADESANT excels in accurately estimate malware severity, providing invaluable insights into the factors contributing to the risk. To further validate its efficiency, we compared MADESANT against prevalent Neural Network models like FeedForward and Recurrent Neural Networks, with MADESANT emerging as the superior choice. The experimentation encompasses both the entire dataset and subsets generated through meticulous risk factor analysis. These results underscore MADESANT’s prowess in not only identifying malware but also in evaluating its potential impact, signifying a significant leap forward in industrial cybersecurity. [ABSTRACT FROM AUTHOR]

In recent years, the explosive growth of the Internet has led to an increment in the number of Distributed Denial of Service (DDoS) attacks. HTTP Flooding is a critical DDoS attack that targets HTTP servers to prohibit users from receiving HTTP services. Moreover, it saturates the link bandwidth and consumes network resources. Because the attack is launched at the application layer, it is difficult to defend against it using current countermeasures such as firewall or Intrusion Prevention System (IPS). In this paper, we propose SHFD , which leverages the Software-Defined Networking (SDN) paradigm to mitigate HTTP flooding attacks. We implement SHFD as a defender module on the SDN controller to detect and mitigate the attack in the first place. Experimental results gathered from Mininet confirm that SHFD brings a significant improvement of 13% in detection time and 29% in the number of blocked malicious flows compared to the state-of-the-art approaches. [Display omitted] • SHFD is proposed for mitigating slow and high rate HTTP flooding attacks in SDN-based networks. • Shannon's Entropy and Hellinger distance methods are used in SHFD to provide more accurate results. • Both slow/high rate HTTP flooding attack together with their side effects are explained in details. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has been characterized by a social media "infodemic": an overabundance of information whose authenticity may not always be guaranteed. With the potential to lead individuals to harmful decisions for the society, this infodemic represents a severe threat to information security, public health and democracy. In this paper, we assess the interplay between the infodemic and specific aspects of the pandemic, such as the number of cases, the strictness of containment measures, and the news media coverage. We perform a comparative study on three countries that employed different managements of the COVID-19 pandemic in 2020—namely Italy, the United Kingdom, and New Zealand. We first analyze the three countries from an epidemiological perspective to characterize the impact of the pandemic and the strictness of the restrictions adopted. Then, we collect a total of 6 million posts from Facebook to describe user news consumption behaviors with respect to the reliability of such posts. Finally, we quantify the relationship between the number of posts published in each of the three countries and the number of confirmed cases, the strictness of the restrictions adopted, and the online news media coverage about the pandemic. Our results show that posts referring to reliable sources are consistently predominant in the news circulation, and that users engage more with reliable posts rather than with posts referring to questionable sources. Furthermore, our modelling results suggest that factors related to the epidemiological and informational ecosystems can serve as proxies to assess the evolution of the infodemic. [ABSTRACT FROM AUTHOR]

The new era of mobile health ushered in by the wide adoption of ubiquitous computing and mobile communications has brought opportunities for governments and companies to rethink their concept of healthcare. Simultaneously, the worldwide urbanization process represents a formidable challenge and attracts attention toward cities that are expected to gather higher populations and provide citizens with services in an efficient and human manner. These two trends have led to the appearance of mobile health and smart cities. In this article we introduce the new concept of smart health, which is the context-aware complement of mobile health within smart cities. We provide an overview of the main fields of knowledge that are involved in the process of building this new concept. Additionally, we discuss the main challenges and opportunities that s-Health would imply and provide a common ground for further research. [ABSTRACT FROM PUBLISHER]

The evolving behavior of the attacks may affect the decision boundaries of the trained machine learning models. The issue has not been well investigated, especially with hypervisor-based security solutions where virtual machine (VM)’s network artifacts are introspected and analyzed. In this paper, we proposed a sustainable and explainable flow-filtering-based concept drift-driven network intrusion detection approach, called ‘SFC-NIDS’ which introspects network activities by analyzing VM traffic profile. The VM traffic is captured and pre-processed at the hypervisor to extract important network artifacts. The redundant and trivial network flows have been filtered using the proposed gradient descent-based flow filtering mechanism and validated using explainability. SFC-NIDS employs auto-encoders to reconstruct the traffic features to capture additional patterns. Afterward, the 1D-convolution neural network has been employed to learn and detect malicious attack flows. The model’s sustainability is ensured by integrating the drift detection mechanism with the decision model to retrain it with evolving attack patterns. The approach has been validated with virtual network traffic artifacts collected at the hypervisor and provides 98.9% accuracy, 99.03%, and F1-Score. In addition, the approach has also been validated using the KDD99 dataset, showcasing an accuracy of 99.97% and an F1-Score of 99.98%. [ABSTRACT FROM AUTHOR]

Several ongoing research efforts aim to design potential Future Internet Architectures, among which Named-Data Networking (NDN) introduces a shift from the existing host-centric Internet Protocol-based Internet infrastructure towards a content-oriented one. However, researchers have identified some design limitations in NDN, among which some enable to build up a new type of Distributed Denial of Service attack, better known as Interest Flooding Attack (IFA). In IFA, an adversary issues not satisfiable requests in the network to saturate the Pending Interest Table (PIT) of NDN routers and prevent them from properly handling the legitimate traffic. Researchers have been trying to mitigate this problem by proposing several detection and reaction mechanisms, but all the mechanisms proposed so far are not highly effective and, on the contrary, heavily damage the legitimate traffic. In this paper, we propose a novel mechanism for IFA detection and mitigation, aimed at decreasing the memory consumption of the PIT by effectively reducing the malicious traffic that passes through each NDN router. In particular, our protocol exploits an effective management strategy on the PIT, through which the Malicious Interest (MIs) already stored in the PIT are removed and the new incoming MIs are dropped. In addition, the proposed countermeasure provides an additional security wall on the edges of the network to detect and mitigate the attack as early as possible and improve the network health, i.e., routers PIT occupancy during IFA. To evaluate the effectiveness of our work, we implemented the proposed countermeasure on the open-source ndnSIM simulator and compared its effectiveness with the state of the art. The results show that our proposed countermeasure effectively reduces the IFA damages both in terms of preserved legitimate traffic and availability of routers PIT. Considering the legitimate traffic, the amount of Benign Interests preserved by our approach increases from 5% to 40% with respect to the preservation guaranteed by the state-of-the-art solutions. Concerning the routers PIT availability, our approach guarantees that the 97% of the PIT size is left free for handling the legitimate traffic. [ABSTRACT FROM AUTHOR]

Wireless Multimedia Sensor Network (WMSN) is a network of interconnected sensor nodes equipped with multimedia devices capable to retrieve multimedia as well as scalar sensor data in smart city scenarios. With the progress of smart city development, WMSNs are becoming points of interest. The existing research works on the security of WMSNs show some significant centralized solutions; they are prone to denial of service attacks, central failure, and privacy breaches. Also, these solution are having limitations because of their centralized nature.Therefore, the the proposed scheme adopted the concept of decentralization to achieve more effective and ubiquitous results. As per our best knowledge, we introduce the first decentralized privacy-preserving authentication framework for WMSNs to address the energy and storage challenges. We call our solution PRIvacy-preserving Blockchain-based Authentication for multimeDIa networks (PRIBADI). PRIBADI uses a blockchain framework for authenticated communication between sensor nodes and base stations (as a trusted authority) by using certification and access authentication schemes. We elect trustworthy cluster heads based on weight metrics and these cluster heads forward the information to the Trusted Authority (TA). Subsequently, TA forward large data to the cloud for storage, and the information relevant to certificates and hashes are recorded on the distributed blockchain. Moreover, TA revokes the certificates of malicious sensor nodes and eliminates them from the blockchain. We run a set of experiments on PRIBADI and compare the results with the state-of-the-art models. We use the evaluation criteria of detection accuracy, operation, and energy consumption. We find that PRIBADI is 31.7% better than the existing models. [ABSTRACT FROM AUTHOR]

The widespread adoption of smartphones dramatically increases the risk of attacks and the spread of mobile malware, especially on the Android platform. Machine learning-based solutions have been already used as a tool to supersede signature-based anti-malware systems. However, malware authors leverage features from malicious and legitimate samples to estimate statistical difference in-order to create adversarial examples. Hence, to evaluate the vulnerability of machine learning algorithms in malware detection, we propose five different attack scenarios to perturb malicious applications (apps). By doing this, the classification algorithm inappropriately fits the discriminant function on the set of data points, eventually yielding a higher misclassification rate. Further, to distinguish the adversarial examples from benign samples, we propose two defense mechanisms to counter attacks. To validate our attacks and solutions, we test our model on three different benchmark datasets. We also test our methods using various classifier algorithms and compare them with the state-of-the-art data poisoning method using the Jacobian matrix. Promising results show that generated adversarial samples can evade detection with a very high probability. Additionally, evasive variants generated by our attack models when used to harden the developed anti-malware system improves the detection rate up to 50% when using the generative adversarial network (GAN) method. [ABSTRACT FROM AUTHOR]

The challenges of cloud forensics have been well-documented by both researchers and government agencies (e.g., U.S. National Institute of Standards and Technology), although many of the challenges remain unresolved. In this article, we perform a comprehensive survey of cloud forensic literature published between January 2007 and December 2018, categorized using a five-step forensic investigation process. We also present a taxonomy of existing cloud forensic solutions, with the aim of better informing both the research and practitioner communities, as well as an in-depth discussion of existing conventional digital forensic tools and cloud-specific forensic investigation tools. Based on the findings from the survey, we present a set of design guidelines to inform future cloud forensic investigation processes, and a summary of digital artifacts that can be obtained from different stakeholders in the cloud computing architecture/ecosystem. [ABSTRACT FROM AUTHOR]

Label manipulation attacks are a subclass of data poisoning attacks in adversarial machine learning used against different applications, such as malware detection. These types of attacks represent a serious threat to detection systems in environments having high noise rate or uncertainty, such as complex networks and Internet of Thing (IoT). Recent work in the literature has suggested using the K-nearest neighboring algorithm to defend against such attacks. However, such an approach can suffer from low to miss-classification rate accuracy. In this paper, we design an architecture to tackle the Android malware detection problem in IoT systems. We develop an attack mechanism based on silhouette clustering method, modified for mobile Android platforms. We proposed two convolutional neural network-type deep learning algorithms against this Silhouette Clustering-based Label Flipping Attack. We show the effectiveness of these two defense algorithms—label-based semi-supervised defense and clustering-based semi-supervised defense—in correcting labels being attacked. We evaluate the performance of the proposed algorithms by varying the various machine learning parameters on three Android datasets: Drebin, Contagio, and Genome and three types of features: API, intent, and permission. Our evaluation shows that using random forest feature selection and varying ratios of features can result in an improvement of up to 19% accuracy when compared with the state-of-the-art method in the literature. [ABSTRACT FROM AUTHOR]

Smart city vision brings emerging heterogeneous communication technologies such as Fog Computing (FC) together to substantially reduce the latency and energy consumption of Internet of Everything (IoE) devices running various applications. The key feature that distinguishes the FC paradigm for smart cities is that it spreads communication and computing resources over the wired/wireless access network (e.g., proximate access points and base stations) to provide resource augmentation (e.g., cyberforaging) for resource- and energy-limited wired/wireless (possibly mobile) things. Motivated by these considerations, this paper presents a Fog-supported smart city network architecture called Fog Computing Architecture Network (FOCAN), a multi-tier structure in which the applications are running on things thatjointly compute, route, and communicate with one another through the smart city environment. FOCAN decreases latency and improves energy provisioning and the efficiency of services among things with different capabilities. In particular, three types of communications are defined between FOCAN devices – interprimary , primary , and secondary communication –to manage applications in a way that meets the quality of service standards for the Internet of Everything. One of the main advantages of the proposed architecture is that the devices can provide the services with low energy usage and in an efficient manner. Simulation results for a selected case study demonstrate the tremendous impact of the FOCAN energy-efficient solution on the communication performance of various types of things in smart cities. • Present a generalized multi-tiered smart city architecture utilizes FC for devices. • Develop an FC-supported resource allocation model to cover FNs/device components. • Provide various types of communications between the components. • Evaluate the performance of the solution for an FC platform on real datasets. [ABSTRACT FROM AUTHOR]

Click Hijacking (clickjacking) is emerging as a web-based threat on the Internet. The prime objective of clickjacking is stealing user clicks. An attacker can carry out a clickjacking attack by tricking the victim into clicking an element that is barely visible or completely hidden. By stealing the victim's clicks, an attacker could entice the victim to perform an unintended action from which the attacker can benefit. These actions include online money transactions, sharing malicious website links, initiate social networking links, etc. This paper presents an anatomy of advanced clickjacking attacks not yet reported in the literature. In particular, we propose new class of clickjacking attacks that employ SVG filters and create various effects with SVG filters. We demonstrate that current defense techniques are ineffective to deal with these sophisticated clickjacking attacks. Furthermore, we develop a novel detection method for such attacks based on the behavior (response) of a website active content against the user clicks (request). In our experiments, we found that our method can detect advanced Scalable Vector Graphics (SVG)-based attacks where most of the contemporary tools fail. We explore and utilize various common and distinguishing characteristics of malicious and legitimate web pages to build a behavioral model based on Finite State Automaton. We evaluate our proposal with a sample set of 78,000 web pages from various sources, and 1000 web pages known to involve clickjacking. Our results demonstrate that the proposed solution enjoys good accuracy and a negligible percentage of false positives (i.e., 0.28%), and zero false negatives in distinguishing clickjacking and legitimate websites. [ABSTRACT FROM AUTHOR]

Legacy encryption systems depend on sharing a key (public or private) among the peers involved in exchanging an encrypted message. However, this approach poses privacy concerns. The users or service providers with the key have exclusive rights on the data. Especially with popular cloud services, control over the privacy of the sensitive data is lost. Even when the keys are not shared, the encrypted material is shared with a third party that does not necessarily need to access the content. Moreover, untrusted servers, providers, and cloud operators can keep identifying elements of users long after users end the relationship with the services. Indeed, Homomorphic Encryption (HE), a special kind of encryption scheme, can address these concerns as it allows any third party to operate on the encrypted data without decrypting it in advance. Although this extremely useful feature of the HE scheme has been known for over 30 years, the first plausible and achievable Fully Homomorphic Encryption (FHE) scheme, which allows any computable function to perform on the encrypted data, was introduced by Craig Gentry in 2009. Even though this was a major achievement, different implementations so far demonstrated that FHE still needs to be improved significantly to be practical on every platform. Therefore, this survey focuses on HE and FHE schemes. First, we present the basics of HE and the details of the well-known Partially Homomorphic Encryption (PHE) and Somewhat Homomorphic Encryption (SWHE), which are important pillars for achieving FHE. Then, the main FHE families, which have become the base for the other follow-up FHE schemes, are presented. Furthermore, the implementations and recent improvements in Gentry-type FHE schemes are also surveyed. Finally, further research directions are discussed. This survey is intended to give a clear knowledge and foundation to researchers and practitioners interested in knowing, applying, and extending the state-of-the-art HE, PHE, SWHE, and FHE systems. [ABSTRACT FROM AUTHOR]

Various sources and sophisticated tools are used to gather and process the comparatively large volume of data or big data that sometimes leads to privacy disclosure (at broader or finer level) for the data owner. Privacy preserving data publishing approaches such as k-anonymity, l-diversity, and t-closeness are very well used to de-identify data, however, chances of re-identification of attributes always exist as data is collected from multiple sources such as public web, social media, Internet whereabouts, and sensors that are highly prone to data linkages. In literature, k-anonymity stands out amongst the most popular mainstream data anonymization approaches that can also be used for large sized data. However, applying k-anonymization for variety of data (especially unstructured data) is difficult in the traditional way, due to the fact that it requires the given data to be classified into the personal data, the quasi identifiers, and the sensitive data. We identify existing approaches from the literature of Natural Language Processing(NLP) to convert the unstructured data to structured form in order to apply k-anonymization over the generated structured records. We adopt a two phase Conditional Random Field (CRF) based Named Entity Recognition (NER) approach to represent unstructured data into the structured form. Further, we propose an Improved Scalable k-Anonymization (ImSKA) to anonymize the well represented unstructured data that achieves privacy preserving unstructured big data publishing. We compare both of the propose approaches namely NER and ImSKA with existing approaches and the results show that our proposed solutions outperform the existing approaches in terms of F1 score and Normalized Cardinality Penalty (NCP), respectively. Since, NER approaches are widely used for bio-medical datasets, we have also used a well-known Bio-NER dataset called GENIA corpus for measuring the performance. [ABSTRACT FROM AUTHOR]

Abstract In remote locations, the best way to establish a communication infrastructure is to deploy Peer-to-Peer (P2P) network over Mobile Ad-hoc Network (MANET). However, the node mobility and efficient cross-layer communication between the overlay and MANET routing protocols remains a challenge for reliable data transmission. In this paper, we propose a novel Mobility Aware Cross-layer Routing approach for Peer-to-Peer Networks (MACARON) over MANET. MACARON provides reliable communication and high lifetime routes to support efficient data transmission in the network. For this purpose, it uses cross-layer communications to share routing updates between MANET and overlay routing protocols. MACARON provides guaranteed routes with low path stretch by maintaining O ˜ (n) routing entries per node, where n is the number of nodes in the network. It provides scalability without using any landmark directory to store routing state, and it can effectively handle moderate mobility by using Last Encounter Routing (LER) protocol. [ABSTRACT FROM AUTHOR]

The event prediction task learns the dynamic changes of past events and infers the upcoming ones. This task helps people to understand how events evolve in our real world and make rapid, accurate, and efficient reactions in emergencies. The complex relation of events and entities (events participants) changes dynamically over time. The current solutions of event prediction have not fully exploited such event temporal dependency and entity relation dependency. In addition, an event may relate to a considerable number of entities. Among such related entities, only a few of them are the key participants in driving the evolution of the event. Previous works fail to emphasize such key entities and limit the non-related entities. In this paper, we introduce a novel gating and attention mechanism and propose a novel T emporal sp A tial dy N amic G raph m O del (TANGO) that is composed of a graph model (based on Graph Convolutional Network with gated and attention mechanisms) and a sequential model (based on Temporal Convolutional Network). TANGO is able to model the event temporal dependency and entity relation dependency simultaneously; learn the representation of events relations; control the aggregation of relational information among entities with varying degrees of mutual influence; support long effective historical sizes. We demonstrate the validity and effectiveness of our approach on three different datasets (i.e., ICEWS18, GDELT, and ICEWS14). The result shows almost 6.2% MRR improvement and 3.9% Hits@10 improvement over the previous state-of-the-art. [ABSTRACT FROM AUTHOR]

Energy management systems are designed to monitor, optimize, and control the smart grid energy market. Demand-side management, considered as an essential part of the energy management system, can enable utility market operators to make better management decisions for energy trading between consumers and the operator. In this system, a priori knowledge about the energy load pattern can help reshape the load and cut the energy demand curve, thus allowing a better management and distribution of the energy in smart grid energy systems. Designing a computationally intelligent load forecasting (ILF) system is often a primary goal of energy demand management. This study explores the state of the art of computationally intelligent (i.e., machine learning) methods that are applied in load forecasting in terms of their classification and evaluation for sustainable operation of the overall energy management system. More than 50 research papers related to the subject identified in existing literature are classified into two categories: namely the single and the hybrid computational intelligence (CI)-based load forecasting technique. The advantages and disadvantages of each individual techniques also discussed to encapsulate them into the perspective into the energy management research. The identified methods have been further investigated by a qualitative analysis based on the accuracy of the prediction, which confirms the dominance of hybrid forecasting methods, which are often applied as metaheurstic algorithms considering the different optimization techniques over single model approaches. Based on extensive surveys, the review paper predicts a continuous future expansion of such literature on different CI approaches and their optimizations with both heuristic and metaheuristic methods used for energy load forecasting and their potential utilization in real-time smart energy management grids to address future challenges in energy demand management. [ABSTRACT FROM AUTHOR]

The hierarchical routing algorithm is categorized as a kind of routing method using node clustering to create a hierarchical structure in large-scale mobile ad hoc network (LMANET). In this paper, we proposed a new hierarchical clustering algorithm (HCAL) and a corresponded protocol for hierarchical routing in LMANET. The HCAL is designed based on a cost metric in the form of the link expiration time and node's relative degree. Correspondingly, the routing protocol for HCAL adopts a reactive protocol to control the existing cluster head (CH) nodes and handle proactive nodes to be considered as a cluster in LMANET. Hierarchical clustering algorithm jointly utilizes table-driven and on-demand routing by using a combined weight metric to search dominant set of nodes. This set is composed by link expiration time and node's relative degree to establish the intra/intercommunication paths in LMANET. The performance of the proposed algorithm and protocol is numerically evaluated in average end-to-end delay, number of CH per round, iteration count between the CHs, average CH keeping time, normalized routing overhead, and packet delivery ratio over a number of randomly generated benchmark scenarios. Furthermore, to corroborate the actual effectiveness of the HCAL algorithm, extensive performance comparisons are carried out with some state-of-the-art routing algorithms, namely, Dynamic Doppler Velocity Clustering, Signal Characteristic-Based Clustering, Dynamic Link Duration Clustering, and mobility-based clustering algorithms. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Multi-Constraint Quality-of-Experience (QoE) centric Routing (MCQR) technique for efficient real-time video streaming over Mobile Ad-hoc Networks (MANETs). The dynamic estimation of link quality metric during MCQR route discovery process is the key of enhancing QoE at end user. MCQR limits end-to-end delay and jitter of received data packets by selecting long lifetime routes with maximum permissible delay. This minimizes the distortion caused fluctuations in perceived video. To prove the effectiveness of MCQR for real-time video streaming, we have designed a hybrid MANET consisting of simulated nodes and physical machines. In our hybrid network, the source and destination nodes are video traffic generators and receivers running on physical devices. Emulation results are obtained using real-time video streaming over high fidelity virtual wireless network, and conforming that the MCQR protocol provides satisfactory QoE to end users as compared to existing similar techniques. The packet delivery ratio of MCQR is higher than 90 % and its mean opinion score is higher than three (i.e., satisfactory) even when mobility and shadowing effects are kept high in target network scenarios. These improvements are achieved along with still maintaining similar or better routing overhead and processing delays. [ABSTRACT FROM AUTHOR]

Researchers envisioned Storage as a Service (StaaS) as an effective solution to the distributed management of digital data. Cooperative storage cloud forensic is relatively new and is an under-explored area of research. Using Symform as a case study, we seek to determine the data remnants from the use of cooperative cloud storage services. In particular, we consider both mobile devices and personal computers running various popular operating systems, namely Windows 8.1, Mac OS X Mavericks 10.9.5, Ubuntu 14.04.1 LTS, iOS 7.1.2, and Android KitKat 4.4.4. Potential artefacts recovered during the research include data relating to the installation and uninstallation of the cloud applications, log-in to and log-out from Symform account using the client application, file synchronization as well as their time stamp information. This research contributes to an in-depth understanding of the types of terrestrial artifacts that are likely to remain after the use of cooperative storage cloud on client devices. [ABSTRACT FROM AUTHOR]

Recent Distributed Denial of Service (DDoS) attacks on cloud services demonstrate new attack effects, including collateral and economic losses. In this work, we show that DDoS mitigation methods may not provide the expected timely mitigation due to the heavy resource outage created by the attacks. We observe an important Operating System (OS) level “internal collateral damage”, in which the other critical services are also affected. We formulate the DDoS mitigation problem as an OS level resource management problem. We argue that providing extra resources to the victim’s server is only helpful if we can ensure the availability of other services. To achieve these goals, we propose a novel resource containment approach to enforce the victim’s resource limits. Our real-time experimental evaluations show that the proposed approach results in reduction in the attack reporting time and victim service downtime by providing isolated and timely resources to ensure availability of other critical services. [ABSTRACT FROM AUTHOR]

Nowadays, users are starting to develop a symbiotic bound with their smartphones. Users continuously carry these devices and use them for daily communication activities and social network interactions. On the other hand, a smartphone is equipped with sensors that can infer not only information about the context (e.g., location), but also about its owner. Since smartphones handle a huge amount of private information, recent techniques rely on those sensing capabilities to authenticate the user, measuring her biometric features. In this paper, we survey the most relevant biometric authentication methods on smartphones proposed in the literature. We categorize such methods by the nature of biometrics used, by their temporal features and by the sensing capabilities they rely on. Moreover, we draw some future directions in this promising research topic. [ABSTRACT FROM AUTHOR]

While encryption can prevent unauthorized access to a secret message, it does not provide undetectability of covert communications over the public network. Implementing a highly latent data exchange, especially with low eavesdropping/discovery probability, is challenging for practical scenarios, such as social and political movements in authoritarian regimes, military operations, and privacy preservation. Moreover, the current literature suffers from a low embedding capacity and monolingual applicability, limiting the amount of hiding secret data within short text messages using state-of-the-art algorithms, e.g., linguistic-based, structural-based, or coverless-based solutions. In this paper, we present a systematic covert communication technique called CovertSYS that enables a multilingual secure end-to-end conversation via messaging or social network platforms. The CovertSYS functions by encrypting a confidential message using a multi-factor authentication scheme and converting the encoded binary data into hidden Unicode symbols to be transmitted under cover of short text messages. We then conduct extensive experiments to confirm the security and validity of the proposed technique against state-of-the-art approaches. Our experimental results show that the CovertSYS provides a superior mean performance of 91.53% by improving the criteria scores: embedding capacity rate of 100%, imperceptibility rate of 76.4%, and distortion robustness rate of 98.2%. Finally, we discuss the practical implications of the proposed technique compared to the existing text steganography methods. [Display omitted] • Various evaluation criteria based on literature and practical scenario have been discussed. • Possible cyberattacks on covert communication systems have been described. • The CovertSYS has been implemented in an Android prototype software. • Experimental results show superior performance compared to existing related works. [ABSTRACT FROM AUTHOR]

In this paper, we show a deep learning model for bot detection, named as DeBot, for industrial network traffic. DeBot uses a novel Cascade Forward Back Propagation Neural Network (CFBPNN) model with a subset of features using the Correlation-based Feature Selection (CFS) technique. A time series-based Nonlinear Auto-regressive Network with eXogenous inputs (NARX) technique analyzes the factors having a higher impact on the target variable and predict the behavioral pattern. To the best of our knowledge, we pioneer the use of optimal feature selection and integration with the cascading model of deep learning in bot detection of IIoTs. We conduct a thorough set of experiments on five popular bot datasets: NF-UNSW-NB15, NF-ToN-IoT, NF-BoT-IoT, NF-CSE-CIC-IDS2018, and ToN-IoT-Windows. We compare CFBPNN with other existing neural network models. We observe that CFBPNN in DeBot shows 100% accuracy in all the datasets with subset evaluation and obtains optimum F1-score and zero precision. [Display omitted] • We address the multi-layered network model with IIoT features to enable bot attack detection. • Our DeBot model evaluates the subset with optimal features with high impact on attack class and predict the risk factor. • We pioneer the implementation of time series-based Nonlinear Auto-Regressive eXogenous (NARX) model to predict the risk in the specified time gap. • We introduce a new cascading model with recurrent feature to preserve the relationship between input and output variables. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging decentralized and distributed technology. Along with the beneficial features of decentralization, transparency, and security the consensus algorithms of blockchains form key building blocks for this technology. Consensus protocol/algorithm helps to provide a decentralized decision making process. An efficient consensus algorithm is inclusive that engages all the participants to make their decision based on the conflicts of the blockchain networks. These consensus decisions lead to better quality outcomes of the blockchains and help to obtain the finality. Rigorous research is in process to upgrade or optimize the existing consensus protocols. The optimized or enhanced consensus protocols objectify to be suitable for Internet-of-Thing (IoT) as the current versions of the protocols are not suitable for the resource-constrained environments due their complexity, hard configurations, mining techniques, high resource consumption, and explicit security loophole. In this paper, we present a survey of consensus protocols with a purpose to identify and discuss the existence of various consensus protocols available in literature. We emphasize on the genesis of the consensus protocols, particularly for Proof-of-X, byzantine fault tolerance, Paxos, and RAFT; we also include Directed Acyclic Graph (DAG) orientation of some contemporary algorithms. We discuss the variants of these genesis protocols. Our survey analyzes the advantages, disadvantages, and their applicability in IoTs. We enlist the categorical use of consensus algorithms in blockchains and other applications. Finally, we present several research trends and open issues emphasizing for consensus protocols emphasizing on IoTs. Compared to the other surveys in the field, our present survey objectifies to provide a more thorough summary of the most relevant protocols and application issues; this survey helps the researchers and the application developers to obtain an insight on the current status of the consensus protocols' suitability to deliver the desired functionalities in IoTs. The notified disadvantages of each of the protocol provide future scope for the industries and academia. To the best of our knowledge, such a comprehensive and summarized survey of consensus protocols including DAG-based protocols is unavailable in the literature and thus, our contribution claims are significant. [ABSTRACT FROM AUTHOR]