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Traffic monitoring services collect traffic reports and respond to users’ traffic queries. However, the reports and queries may reveal the user’s identity and location. Although different anonymization techniques have been applied to protect user privacy, a new security threat arises, namely, n-by-1 jamming attack, in which an anonymous contributing driver impersonates $n$ drivers and uploads $n$ normal reports by using $n$ reporting devices. Such an attack will mislead the traffic monitoring service provider and further degrade the service quality. Existing traffic monitoring services do not support customized queries, and private information retrieval techniques cannot be applied directly in traffic monitoring. We formally define the new attack and propose a traffic monitoring scheme TraJ to defend the attack and achieve user-defined location privacy. Specifically, we bridge anonymous contributing drivers without disclosing their speed set by using private set intersection. Each RSU collects time traffic reports and structures a weighted proximity graph to filter out malicious colluding drivers. We design a user-defined privacy-preserving query method by encoding complex road network. We leverage the uploading phase from private aggregation to collect traffic conditions and allow requesting drivers to dynamically and privately query traffic conditions. We provide a formal analysis of TraJ to prove its privacy and security properties. We also construct a prototype based on a real-world dataset and Android smartphones to demonstrate its feasibility and efficiency. A formal analysis demonstrates the privacy and security properties. Extensive experiments illustrate the performance and defense efficacy. [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]

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]

The integration between blockchains, Internet-of-Thing (IoT), and smart contracts is an emerging and promising technology. The advantages of this technology have raised the importance of Industrial Internet-of-Thing (IIoT) and have paved the pathway for “Industry 4.0.” Surprisingly, access control has received less attention in IIoTs. Though there are some solutions coming forward to use blockchains for IIoT to enable secure and resilient access control management, the challenge is to satisfy the low-latency requirements of IIoTs for validating and adding the blocks to the chain. Besides, role-based and rule-based access controls in the existing systems can be forged without organizational access controls and compliance. Therefore, we address these problems in this article. In the present work, we propose DHACS, a Decentralized Hybrid Access Control for Smart contract, for IIoTs. DHACS aims to provide transparency, reliability, and robustness to the existing access control mechanism in IIoTs. The framework is based on blockchain feasibilities that contribute to an interconnected hybrid access control through smart contract provision. It is a novel idea in the domain of IIoTs. We use three access control strategies, role-based, rule-based, and organization-based, to develop a hybrid approach for smart contract in DHACS. The operational transactions along with their access controls are accounted and blocks are made by the transaction pooler and block creator. We use a private blockchain environment; however, it can be extended to a public blockchain or consortium blockchain for geographical distributed dependency. We compare DHACS with three existing approaches in recent time. We measure the performance in terms of computational costs, storage complexity, and energy consumption. DHACS outperforms the others approaches and is considered to be efficient for IIoT applications with more than 30% better efficiency in access control management. To the best of our knowledge, DHACS is the first attempt to use decentralized blockchains with smart contract for hybrid access control in IIoTs. [ABSTRACT FROM AUTHOR]

E-commerce platforms incorporate reputation systems that allow customers to rate suppliers following financial transactions. Existing reputation systems cannot defend the centralized server against arbitrarily tampering with the supplier’s reputation. Furthermore, they do not offer reputation access across platforms. Rates are faced with privacy leakages because rating activities are correlated with privacy (e.g., identity and rating). Meanwhile, raters could be malicious and initiate multiple rating attacks and abnormal rating attacks. Determining how to address these issues have both research and practical value. In this paper, we propose a blockchain-based privacy-preserving reputation system for e-commerce platforms named RepChain; our system allows cross-platform reputation access and anonymous and private ratings. Using RepChain, all e-commerce platforms collaborate and share users’ reputations by co-constructing a consortium blockchain and modeling the rating process as a finite state machine. In particular, we facilitate one-show anonymous credentials constructed from two-move blind signatures to protect customers’ identities and resist multiple rating attacks, leverage zero-knowledge range proof to verify the correctness of ratings and defend against abnormal rating attacks, design a secure sum computation protocol among nodes to update reputations, and verify ratings via batch processing and consensus hashes. Finally, we demonstrate the security and privacy of RepChain via a formal analysis and evaluate its performance based on Ethereum test network. [ABSTRACT FROM AUTHOR]

Software-Defined Network (SDN) segregates the control plane and the data plane to bring about a programmable network. The controller at the control plane runs network modules and sets rules for forwarding the packets in the switches that resides at the data plane. Though advantageous in several ways, SDN can fail when the controller is saturated by a flood of TCP SYN packets. SYN flood can be created using malicious spoofing of IP or MAC addresses or flash crowd. The existing solutions to mitigate SYN flood against the controller does not adequately handle MAC spoofing based SYN flood, and these are unable to distinguish between flash crowd and malicious traffic. To overcome some limitations in existing solutions, we propose a novel mechanism called AEGIS, which detect and mitigate SYN flood against the controller in SDN. AEGIS runs in the controller, and it regularly checks if there is a performance lag in the controller due to an ongoing SYN flood. If a performance degradation is detected, then AEGIS takes it an indication of SYN flood and it identifies whether it is due to spoofed addresses or flash crowd. Once the reason is found, the appropriate mitigation procedure is triggered. We evaluate AEGIS in testbed and emulator settings, and we compare the results of the evaluation with state-of-the-art solutions. The performance evaluation of AEGIS shows that it identifies the malicious SYN at an accuracy of 97.78%. Moreover, when there is no SYN flood, AEGIS takes 0.0637s to set up a successful TCP connection, which is 53.81% less than the time taken by the state-of-the-art solution, thus, it proves that AEGIS is lightweight. [ABSTRACT FROM AUTHOR]

Energy trading in Industrial Internet of Things (IIoT), a fundamental approach to realize Industry 4.0, plays a vital role in satisfying energy demands and optimizing system efficiency. Existing research works utilize a utility company to distribute energy to energy nodes with the help of energy brokers. Afterwards, they apply blockchain to provide transparency, immutability, and auditability of peer-to-peer (P2P) energy trading. However, their schemes are constructed on a weak security model and do not consider the cheating attack initiated by energy sellers. Such an attack refers to an energy seller refusing to transfer the negotiated energy to an energy purchaser who already paid money. In this article, we propose $\mathsf{FeneChain}$ , a blockchain-based energy trading scheme to supervise and manage the energy trading process toward building a secure energy trading system and improving energy quality for Industry 4.0. Specifically, we leverage anonymous authentication to protect user privacy, and we design a timed-commitments-based mechanism to guarantee the verifiable fairness during energy trading. Moreover, we utilize fine-grained access control for energy trading services. We also build a consortium blockchain among energy brokers to verify and record energy trading transactions. Finally, we formally analyze the security and privacy of $\mathsf{FeneChain}$ and evaluate its performance (i.e., computational costs and communication overhead) by implementing a prototype via a local Ethereum test network and Raspberry Pi. [ABSTRACT FROM AUTHOR]

The Android smartphones are highly prone to spreading the malware due to intrinsic feebleness that permits an application to access the internal resources when the user grants the permissions knowingly or unknowingly. Hence, the researchers have focused on identifying the conspicuous permissions that lead to malware detection. Most of these permissions, common to malware and normal applications present themselves in different patterns and contribute to attacks. Therefore, it is essential to find the significant combinations of the permissions that can be dangerous. Hence, this paper aims to identify the pairs of permissions that can be dangerous. To the best of our knowledge, none of the existing works have used the permission pairs to detect malware. In this paper, we proposed an innovative detection model, named PermPair, that constructs and compares the graphs for malware and normal samples by extracting the permission pairs from the manifest file of an application. The evaluation results indicate that the proposed scheme is successful in detecting malicious samples with an accuracy of 95.44% when compared to other similar approaches and favorite mobile anti-malware apps. Further, we also proposed an efficient edge elimination algorithm that removed 7% of the unnecessary edges from the malware graph and 41% from the normal graph. This lead to minimum space utility and also 28% decrease in the detection time. [ABSTRACT FROM AUTHOR]

We propose a methodology to leverage machine learning (ML) for the detection of web application vulnerabilities. We use it in the design of Mitch, the first ML solution for the black-box detection of cross-site request forgery vulnerabilities. Finally, we show the effectiveness of Mitch on real software. [ABSTRACT FROM AUTHOR]

The devices in smart grids (SG) transfer data to a utility center (UC) or to the remote control centers. Using these data, the energy balance is maintained between consumers and the grid. However, this flow of data may be tampered by the intruders, which may result in energy imbalance. Thus, a robust authentication protocol, which supports dynamic SG device validation and UC addition, both in the local and global domains, is an essential requirement. For this reason, ECCAuth: a novel elliptic curve cryptography-based authentication protocol is proposed in this paper for preserving demand response in SG. This protocol allows establishment of a secret session key between an SG device and a UC after mutual authentication. Using this key, they can securely communicate for exchanging the sensitive information. The formal security analysis, informal security analysis, and formal security verification show that ECCAuth can withstand several known attacks. [ABSTRACT FROM AUTHOR]

Software defined networking (SDN) is an emerging network paradigm which emphasizes the separation of the control plane from the data plane. This decoupling provides several advantages such as flexibility, programmability, and centralized control. However, SDN also introduces new vulnerabilities due to the required communication between data plane and control plane. Examples of threats that leverage such vulnerabilities are the control plane saturation and switch buffer overflow attacks. These attacks can be launched by flooding the TCP SYN packets from data plane (i.e., switches) to the control plane. This paper presents SAFETY, a novel solution for the early detection and mitigation of TCP SYN flooding. SAFETY harnesses the programming and wide visibility approach of SDN with entropy method to determine the randomness of the flow data. The entropy information includes destination IP and few attributes of TCP flags. To show the feasibility and effectiveness of SAFETY, we implement it as an extension module in Floodlight controller and evaluate it under different conditional scenarios. We run a thorough evaluation of our implementation through extensive emulation via Mininet. The experimental results show that when compared to the state-of-the-art, SAFETY brings a significant improvement (13%) regarding processing delay experienced by a legitimate node. Other parameters such as CPU utilization at the controller and attack detection time are also examined and shows improvement in various scenarios. [ABSTRACT FROM AUTHOR]

The Internet protocol (IP) is the lifeblood of the modern Internet. Its simplicity and universality have fueled the unprecedented and lasting global success of the current Internet. Nonetheless, some limitations of IP have been emerging in recent years. Furthermore, starting in mid-1990s, the advent of mobility, wirelessness, and the Web substantially shifted Internet usage and communication paradigms. This accentuated long-term concerns about the current Internet architecture and prompted interest in alternative designs. The U.S. National Science Foundation (NSF) has been one of the key supporters of efforts to design a set of candidate next-generation Internet architectures. As a prominent design requirement, NSF emphasized “security and privacy by design” in order to avoid the long and unhappy history of incremental patching and retrofitting that characterizes the current Internet architecture. To this end, as a result of a competitive process, four prominent research projects were funded by the NSF in 2010: nebula, named-data networking, MobilityFirst, and expressive Internet architecture. This paper provides a comprehensive and neutral analysis of salient security and privacy features (and issues) in these NSF-funded future Internet architectures. Prior surveys on future Internet architectures provide a limited, or even no, comparison on security and privacy features. In addition, this paper also compares the four candidate designs with the current IP-based architecture and discusses similarities, differences, and possible improvements. [ABSTRACT FROM AUTHOR]

Cloning is a very serious threat in the Internet of Things (IoT), owing to the simplicity for an attacker to gather configuration and authentication credentials from a non-tamper-proof node, and replicate it in the network. In this paper, we propose MDSClone, a novel clone detection method based on multidimensional scaling (MDS). MDSClone appears to be very well suited to IoT scenarios, as it: 1) detects clones without the need to know the geographical positions of nodes; 2) unlike prior methods, it can be applied to hybrid networks that comprise both static and mobile nodes, for which no mobility pattern may be assumed a priori. Moreover, a further advantage of MDSClone is that 3) the core part of the detection algorithm can be parallelized, resulting in an acceleration of the whole detection mechanism. Our thorough analytical and experimental evaluations demonstrate that MDSClone can achieve a 100% clone detection probability. Moreover, we propose several modifications to the original MDS calculation, which lead to over a 75% speed up in large scale scenarios. The demonstrated efficiency of MDSClone proves that it is a promising method towards a practical clone detection design in IoT. [ABSTRACT FROM PUBLISHER]

Implantable medical devices (IMDs) are man-made devices, which can be implanted in the human body to improve the functioning of various organs. The IMDs monitor and treat physiological condition of the human being (for example, monitoring of blood glucose level by insulin pump). The advancement of information and communication technology enhances the communication capabilities of IMDs. In healthcare applications, after mutual authentication, a user (for example, doctor) can access the health data from the IMDs implanted in a patient's body. However, in this kind of communication environment, there are always security and privacy issues, such as leakage of health data and malfunctioning of IMDs by an unauthorized access. To mitigate these issues, in this paper, we propose a new secure remote user authentication scheme for IMDs communication environment to overcome security and privacy issues in existing schemes. We provide the formal security verification using the widely accepted Automated Validation of Internet Security Protocols and Applications tool. We also provide the informal security analysis of the proposed scheme. The formal security verification and informal security analysis prove that the proposed scheme is secure against known attacks. The practical demonstration of the proposed scheme is performed using the broadly accepted NS2 simulation tool. The computation and communication costs of the proposed scheme are also comparable with the existing schemes. Moreover, the scheme provides additional functionality features, such as anonymity, untraceability, and dynamic implantable medical device addition. [ABSTRACT FROM AUTHOR]

Underwater acoustic networks (UANs) have been recognized as an enabling technology for various applications in the maritime domain. The wireless nature of the acoustic medium makes UANs vulnerable to various malicious attacks, yet, limited consideration has been given to security challenges. In this paper, we outline a hybrid architecture that incorporates aspects of physical layer security, software defined networking, node cooperation, cross-layering, context-awareness, and cognition. The proposed architecture envisions strategies at the node as well as at the network level that adapt to environmental changes, the status of the network and the possible array of attacks. Several examples of attacks and countermeasures are discussed while deployment and functionality issues of the proposed architecture are taken into consideration. This work is not intended to represent a whatsoever proven solution but mainly to suggest future research directions to the scientific community working in the area of UANs. [ABSTRACT FROM AUTHOR]

Many dependable systems rely on the integrity of the position of their components. In such systems, two key problems are secure localization and secure location verification of the components. Researchers proposed several solutions, which generally require expensive infrastructures of several fixed stations (anchors) with trusted positions. In this paper, we explore the approach of replacing all the fixed anchors with a single drone that flies through a sequence of waypoints. At each waypoint, the drone acts as an anchor and securely determines the positions. This approach completely eliminates the need for many expensive anchors. The main challenge becomes how to find a convenient path for the drone to do this for all the devices. The problem presents novel aspects, which make existing path planning algorithms unsuitable. We propose LocalizerBee, VerifierBee, and PreciseVerifierBee: three path planning algorithms that allow a drone to respectively measure, verify, and verify with a guaranteed precision a set of positions in a secure manner. They are able to securely localize all the positions in a generic deployment area, even in the presence of drone control errors. Moreover, they produce short path lengths and they run in a reasonable processing time. [ABSTRACT FROM AUTHOR]

Software defined networking (SDN) is a novel networking paradigm which decouples control plane from data plane. This separation facilitates a high level of programmability and manageability. On the other hand, it makes the SDN controller a bottleneck and hence vulnerable to control plane saturation attack. One of the key mechanism to achieve control plane saturation is via TCP SYN flooding attack. This is one of the most effective and popular denial of service attack, in which the attacker produces many half-open TCP connections on the targeted server in order to degrade its availability. Furthermore, when applied to SDN, TCP SYN flooding attack also introduces control plane saturation attack. In particular, the attacker generates a significant number of TCP SYN packets and imposes data plane switches to forward them to the controller. As a result, the performance of the controller degrades and the controller will not be able to respond genuine requests in acceptable time. In this paper, we propose SLICOTS, an effective and efficient countermeasure to mitigate TCP SYN flooding attack in SDN. SLICOTS takes the advantage of dynamic programmability nature of SDN to detect and prevent attacks. SLICOTS is implemented in the controller, it surveils ongoing TCP connection requests, and blocks malicious hosts. We implemented SLICOTS as an extension module of OpenDayLight controller and evaluated it under different attack scenarios. The experimental results confirm that, compared to the state-of-art, SLICOTS reduces the response time overhead up to some 50%, while ensuring the same level of protection. [ABSTRACT FROM PUBLISHER]

The emergence of molecular communication has provided an avenue for developing biological nanonetworks. Synthetic biology is a platform that enables reprogramming cells, which we refer to as Bio-NanoThings, that can be assembled to create nanonetworks. In this paper, we focus on specific Bio-NanoThings, i.e, bacteria, where engineering their ability to emit or sense molecules can result in functionalities, such as cooperative target localization. Although this opens opportunities, e.g., for novel healthcare applications of the future, this can also lead to new problems, such as a new form of bioterrorism. In this paper, we investigate the disruptions that malicious Bio-NanoThings (M-BNTs) can create for molecular nanonetworks. In particular, we introduce two types of attacks: 1) blackhole and 2) sentry attacks. In blackhole attack M-BNTs emit attractant chemicals to draw-in the legitimate Bio-NanoThings (L-BNTs) from searching for their target, while in the sentry attack, the M-BNTs emit repellents to disperse the L-BNTs from reaching their target. We also present a countermeasure that L-BNTs can take to be resilient to the attacks, where we consider two forms of decision processes that includes Bayes’ rule as well as a simple threshold approach. We run a thorough set of simulations to assess the effectiveness of the proposed attacks as well as the proposed countermeasure. Our results show that the attacks can significantly hinder the regular behavior of Bio-NanoThings, while the countermeasures are effective for protecting against such attacks. [ABSTRACT FROM PUBLISHER]

Intervehicular communication (IVC) is an important emerging research area that is expected to considerably contribute to traffic safety and efficiency. In this context, many possible IVC applications share the common need for fast multihop message propagation, including information such as position, direction, and speed. However, it is crucial for such a data exchange system to be resilient to security attacks. Conversely, a malicious vehicle might inject incorrect information into the intervehicle wireless links, leading to life and money losses or to any other sort of adversarial selfishness (e.g., traffic redirection for the adversarial benefit). In this paper, we analyze attacks to the state-of-the-art IVC-based safety applications. Furthermore, this analysis leads us to design a fast and secure multihop broadcast algorithm for vehicular communication, which is proved to be resilient to the aforementioned attacks. [ABSTRACT FROM PUBLISHER]

In a large sensor network, in-network data aggregation significantly reduces the amount of communication and energy consumption. Recently, the research community has proposed a robust aggregation framework called synopsis diffusion which combines multipath routing schemes with duplicate-insensitive algorithms to accurately compute aggregates (e.g., predicate Count, Sum) in spite of message losses resulting from node and transmission failures. However, this aggregation framework does not address the problem of false subaggregate values contributed by compromised nodes resulting in large errors in the aggregate computed at the base station, which is the root node in the aggregation hierarchy. This is an important problem since sensor networks are highly vulnerable to node compromises due to the unattended nature of sensor nodes and the lack of tamper-resistant hardware. [ABSTRACT FROM PUBLISHER]

Cybercrime in the past decade has experienced an all-time high due to the inclusion of so-called smart devices in our daily lives. These tiny devices with brittle security features are often dubbed as the Internet of Things (IoT). Their inclusion is not only limited to our daily lives but also in different fields, for example, healthcare, smart-industries, aviation, and smart-cities. Although IoT devices make our lives easy and perform our jobs in a smart way, but their fragile security mechanisms pose a severe challenge regarding safety and privacy of its users. Attacks like Stuxnet, and Mirai-botnet are the key examples of the damages that can be caused by maliciously controlling these devices. One effective tool to identify a malicious entity at a network device is to perform Remote Attestation (RA). However, performing RA over a large, heterogeneous IoT network is difficult tasks due to resource constrain nature of these networks. To this end, we propose a novel scheme called SARP , which is an attestation-assisted secure and scalable routing protocol for IoT networks. SARP performs attestation in large scale IoT networks by using Routing Protocol for Low Power and Lossy Networks (RPL) framework and exploiting the inbuilt features of RPL. In particular, SARP uses attestation technique that not only secures the network from internal attacks, but it also provides security to RPL's data communication process, which helps to improve the overall network performance. Moreover, SARP supports network mobility, device heterogeneity, and network scalability, while it does not sacrifice the key requirements of IoT networks such as low energy and memory consumption, and low network overhead. The simulation results obtained in different IoT scenarios in presence of various types of attacks show the effectiveness of SARP, concerning energy consumption, packet delivery ratio, network overhead, data integrity, and communication security. [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]

Summary: The cyber‐security threats to low‐cost end‐user devices could severely undermine the expected deployment of Internet of Thing (IoT) solutions in a range of real‐world applications such as environment monitoring, transportation, and manufacturing. Additionally, the huge amount of data generated by these devices posses new challenges concerning tasks such as efficient information acquisition and analysis, decision making, and action implementation. In this paper, we propose CENSOR, a novel cloud‐enabled secure IoT network architecture based on SDN paradigm. We discuss the significant benefits as well as challenges that are inherent while performing integration of SDN and IoT in CENSOR. We show that the emerging software‐based networking features combined with the cloud computing solutions can significantly improve the security and communication reliability in the target IoT scenarios. In particular, to provide the adequate security measures in the network, CENSOR uses a lightweight and scalable software remote attestation scheme, which ensures the integrity of the software that is being executed by the IoT devices to achieve the application specific goals in the network. We further discuss the improvements in data communication and data overhead that can be achieved in CENSOR due to its convergence with the cloud computing (at back‐end) and fog computing services (at edge routers or front‐end). A Smart City use‐case has been considered as a target IoT scenario to analyze the feasibility and effectiveness of CENSOR concerning the communication security and the network scalability parameters. Additionally, we provide future research directions along with the recent industry initiatives that include open issues in the integration and deployment of cloud‐enabled SDN‐based IoT networks. [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]