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Public Acceptance is a prerequisite forgaining the intended benefits of Connected and Au-tonomous Vehicles (CAVs). This paper proposes anidea of digital labels for CAVs so as to increaseconsumers trust and awareness regarding the se-curity level of their vehicle. We come up with anarchitecture named Cybersecurity Box (CSBox) inwhich digital labels display and inform the CAVconsumers and passengers about its cybersecurityhealth status. We also highlight requirements andchallenges in developing digital security labels forCAVs

Security and efficiency are crucial considerations for Long Term Evolution for Railway (LTE-R) which bears real-time transmission of train control information for future high-speed railway. In this article, we first analyze the vulnerabilities of LTE-R access authentication protocol, and then propose a proxy signature based authentication scheme to enhance the security of LTE-R without sacrificing efficiency. The proposed scheme consists of three main security mechanisms: a novel Elliptic Curve Cryptosystem based Certificateless Proxy Signature (ECC-CLPS) designed for authentication security, a hash-based puzzle introduced to defend against denial of service (DoS) attack and a key pre-gxeneration mechanism used to improve the efficiency of fast handover authentication. The security analysis and performance simulation show that our scheme has advantages over existing LTE-based authentication schemes in terms of security, functionality, computation and communication costs. Moreover, the authentication requirements for security and efficiency in different LTE-R communication scenarios are fully considered, which makes our scheme more suitable for the access authentication in the future LTE-R based high-speed railway.

Volunteer Computing provision of seamless connectivity that enables convenient and rapid deployment of greener and cheaper computing infrastructure is extremely promising to complement next-generation distributed computing systems. Undoubtedly, without tactile Internet and secure VC ecosystems, harnessing its full potentials and making it an alternative viable and reliable computing infrastructure is next to impossible. Android-enabled smart devices, applications, and services are inevitable for Volunteer computing. Contrarily, the progressive developments of sophisticated Android malware may reduce its exponential growth. Besides, Android malwares are considered the most potential and persistent cyber threat to mobile VC systems. To secure Android-based mobile volunteer computing, the authors proposed MulDroid, an efficient and self-learning autonomous hybrid (Long-Short-Term Memory, Convolutional Neural Network, Deep Neural Network) multi-vector Android malware threat detection framework. The proposed mechanism is highly scalable with well-coordinated infrastructure and self-optimizing capabilities to proficiently tackle fast-growing dynamic variants of sophisticated malware threats and attacks with 99.01% detection accuracy. For a comprehensive evaluation, the authors employed current state-of-the-art malware datasets (Android Malware Dataset, Androzoo) with standard performance evaluation metrics. Moreover, MulDroid is compared with our constructed contemporary hybrid DL-driven architectures and benchmark algorithms. Our proposed mechanism outperforms in terms of detection accuracy with a trivial tradeoff speed efficiency. Additionally, a 10-fold cross-validation is performed to explicitly show unbiased results.

The continuous innovation and progression in hardware, software and communication technologies helped the expansion and accelerated growth in Internet of Things based drone networks (IoD), for the devices, applications and people to communicate and share data. IoD can enhance comfort in many applications including, daily life, commercial, and military/rescue operations in smart cities. However, this growth in infrastructure smartness is also subject to new security threats and the countermeasures require new customized solutions for IoD. Many schemes to secure IoD environments are proposed recently; however, some of those were proved as insecure and some degrades the efficiency. In this article, using elliptic curve cryptography, we proposed a new authentication scheme to secure the communication between a user and a drone flying in some specific flying zone. The security of the proposed scheme is solicited using formal Random oracle method along with a brief discussion on security aspects provided by proposed scheme. Finally, the comparisons with some related and latest schemes is illustrated.

Recent technological evolution in the Internet of Things (IoT) age supports better solutions to magnify the management of the power quality and reliability concerns, and imposes the measures of a smart grid. In smart grid environment, a smart meter needs to securely access the services from a service provider via insecure channel. However, since the communication is via public channel, it imposes various security threats by an adversary. To deal with this, in this article we design a new anonymous signature-based authenticated key exchange scheme for IoT-enabled smart grid environment, called AAS-IoTSG. The dynamic smart meter addition phase is also permissible in AAS-IoTSG after initial deployment. The security of AAS-IoTSG has been tested rigorously using formal security analysis under the real-or-random (ROR) model which is one of the broadly-accepted standard random oracle models, formal security verification under the broadly-used automated validation of Internet security protocols and applications (AVISPA) tool and also using informal security analysis. Finally, an exhaustive comparative study unveils that AAS-IoTSG supports better security and functionality features and requires less communication and computation overheads as compared to the existing state-of-art authentication mechanisms in smart grid systems.

As one of the essential applications of health information technology, the eHealth system plays a significant role in enabling various internet medicine service scenes, most of which primarily rely on service recommendation or an evaluation mechanism. To avoid privacy leakage, some privacy-preserving mechanisms must be adopted to protect raters’ privacy and make evaluation trust reliable. To tackle this challenge, this article proposes an efficient service recommendation and evaluation scheme, called EPRT , which is based on a similarity calculation and trust discovery method. This scheme uses homomorphic encryption technology to encrypt the sensitive data and combines the threshold mechanism and double-trap mechanism to realize the secure computing on the encrypted data, so as to ensure that the plaintexts of the final calculation results (e.g., recommendation value and evaluation truth) are only obtained by the authorized subject. In addition, a detailed security analysis shows that the proposed EPRT scheme can achieve the expected security. In addition, performance comparison results are carried out, demonstrating its effectiveness and accuracy.

Autonomous and connected vehicles are a collection of smart components such as sensors, Radar/Lidar, Electronic Control Units (ECUs), and cameras programmed through voluminous lines of codes, which enable a seamless communication system between the user, vehicle, and infrastructure. The concept of autonomous vehicles is gaining significant traction in the global markets, generating higher revenues, and witnessing high market penetration, thus attracting entrepreneurs to make new and disruptive innovations. However, the underlying technology and communication system of autonomous and connected vehicles are prone to the cybersecurity threats, which circumvent any product that uses sensors, transmits data over the network, and can be controlled through a remote computer or a mobile application. In this article, we attempt to outline the concerns that an aspiring entrepreneur should be aware of before entering into the autonomous vehicle industry, followed by innovative recommendations that can guide addressing the article's issues. The presented recommendations and guidelines are based on the cybersecurity principles of confidentiality, integrity, and availability.

Searchable encryption performs satisfactorily in protecting the privacy of outsourced data in cloud storage scenarios because it encrypts data and provides a secure way of searching on the ciphertext. Dynamic searchable encryption is designed to support the insertion and deletion of outsourced data. However, insertion may cause information leakage of updated keywords. Thus, forward privacy is proposed to limit the leakage of insertion, and it has become a vital security attribute for dynamic schemes. A verifiable dynamic encryption with ranked search (VDERS) scheme helps users to update outsourced data and verify the search result’s accuracy. However, as demonstrated in this study, a VDERS scheme proposed recently fails to satisfy forward privacy because there are two links between the previous search token and the added document. Thus, we designed an improved scheme VDERSc to achieve forward privacy. In our work, we cut off the two links by adding counters and an update buffer. Finally, experiment results showed that our improved scheme supports verification at a finer granularity, leading to a significant proof generation reduction.

Recently, Thumbur et al. introduced a paring-free certifcateless signature scheme to resolve security and efficiency issues in resource-constrained devices and claimed it was secure. However, we find that their scheme is vulnerable to signature forgery attack. To solve the above security challenges, we present a new pairing-free certificateless signature scheme and then formally prove its security under the ECDLP assumption, and finally we conduct a performance analysis and comparison. Security analysis and performance evaluation demonstrate that our new proposal can enjoy a higher level of security with a lower computation cost and communication cost, it is more practical in resource-constrained scenarios.

Wireless sensor networks (WSNs) accomplish a key aspect in the Industrial Internet of Things (IIoT) that has turned out to be the most flourishing perception for computerizations in an industrial maneuver. IIoT environment is a significant application of the Internet of Things (IoT) that facilitate the professionals related to industries for remote supervision of factory, warehouses, etc. In this environment, the data must constantly be analyzed to take important real-time decisions. It makes remote confirmation most essential to secure the data transmission through insure channel so that alteration, interception, modifications of data cannot occur. As a result, there remains an immense requirement of remote user authentication protocol to restrict any illicit access to the data. In this scenario, to eradicate all the security attacks, it is aimed to design a robust provable-secure privacy-preserving three-factor authentication protocol for IIoT in this research. Our scheme is analyzed using both informal and formal security reviews using broadly known random oracle models, BAN logic as well as simulated using a well-accepted ProVerif simulation tool that confirms that the protocol is well-secured against all existing security threats. It is exhibited in the performance estimation that the proposed scheme is more efficient as well as lightweight than other existing schemes. To conclude it can be said that the proposed scheme is easy to be realized in the resource-constrained IIoT environment.

Security is paramount in protecting critical infrastructures such as healthcare, power, water distribution, and transport system. Modern critical infrastructures are increasingly turning into distributed, complex Cyber-Physical systems that enable data monitoring in near real-time conditions. With the advancement in distributed IoT systems, the magnitude of the threats has increased. An innovative approach is to adopt Privacy-Preserving machine learning solutions to detect any possible anomalies in such infrastructures. This issue aims to contribute to the body of knowledge on enhancing critical infrastructure security in developing machine learning solutions and benefiting our future fighting against cyber threats in critical infrastructure.

The radical progress in web services has drained more attraction towards escalating the security of several applications that serve and interact with the Internet users. In order to get authenticated from servers, the users must disclose their secret information to the server such as password and username so that they can access distinct applications on the Web. Due to distinct security attacks, such secret credentials should be discouraged from being revealed. Moreover, it is vibrant to secure the systems from known attacks. In contrast to all known security attacks, the insider attack is considered devastating because the privileged insiders of a system can violate the secret credentials, which may lead towards irrecoverable damage to both the system and the user. Therefore, to ensure the security of the system from insider attacks, different protocols have been proposed. Very recently, Rajamanickam et al. “Insider attack protection: Lightweight password-based authentication techniques using ECC,” presented novel authentication scheme for insider attack protection. They claimed that their protocol not only prevents insider attack but it is also immune to several known security attacks. This comment discloses the non-trivial weaknesses in the authentication phase between client and server. We have identified that the adversary can successfully impersonate the entities communicating with each other through this protocol. Moreover, their protocol fails to offer forward and backward secrecy. Consequently, we suggest possible solution for attack resilience.

As an extension of traditional vehicular ad hoc networks, the Internet of Vehicles (IoV) enables information collection and dissemination, which brings a lot of convenience and benefits to the intelligent transportation systems. However, the booming IoV confronts a few challenges in the aspects of vehicle location privacy preservation and the authenticity of the transmitted information. In order to meet these challenges, we propose an efficient certificateless aggregate signature scheme with conditional privacy preservation in this article. Our scheme utilizes the technique of full aggregation to reduce the bandwidth resources and computing overhead. Besides, the conditional privacy preservation in IoV system is realized by using pseudonym mechanism. We demonstrate that the proposed scheme is secure against the Type-I and Type-II adversaries in the random oracle under the computational Diffie–Hellman assumption. In addition, the proposed scheme is compared with related works from aspects of computation cost, communication efficiency, and security requirements. The comparison results show that the proposed scheme is efficient, and it is suitable for resource-constrained environments.

Medical images with various modalities have become an integral part of the diagnosis and treatment of several diseases. The medical practitioners often use previous case studies to deal with the current medical condition of any particular patient. In such circumstance, they need to securely access medical images of various cases which are generally stored in a network and are vulnerable to malicious attacks. To address these sensitive inadequacies, we have proposed computational intelligence based secure healthcare Content based Image Retrieval (CBIR) for medical image retrieval scheme through which any medical practitioner can retrieve the image in an encrypted domain in cloud environment. In this regard, hamming distance-based similarity matching is the only available technique that effectively handles the comparison between encrypted features. This technique requires binary features to perform similarity matching, and the performance of such features in image retrieval is poor. In this concern, we have suggested a salient component-based binary feature extraction approach to enhance retrieval accuracy. Initially, we have re-arranged the input image using the saliency map, principal texture direction, and entropy to place the salient components at the starting blocks. Subsequently, we have employed a block-level majority voting scheme on the salient blocks of the image to obtain local binary features. As a result, the final feature vector carries more features from the salient part of the image, which propitiously improves the retrieval accuracy. Later, we have encrypted the binary feature vector and performed image retrieval on cloud environment which involve Data Owner, Database Service Provider and Client over encrypted domain to full fill the security aspect. Finally, we have used medical as well as Corel image datasets to validate the retrieval performance accuracy of the proposed scheme. The experimental results obtained from real life datasets exhibit that the proposed method is secure and provides comparable retrieval accuracy concerning other related schemes in the domain.

Recent advancements in internet technology and the infrastructure have attracted more people and organizations to do everything online. Internet technologies have provided amazing and smooth ease for electronic sales and purchases. However, many people have refused to use these internet technologies in electronic purchases because of unstable and insecure forms. New hacking techniques and new types of attacks have been tackled to make these internet technologies better and safer. Smartcard-based password authentication schemes have been the mainstream in recent years, featuring their highly lightweight, easy-to-use equipment and lowcost apps. Various secure and faster authentication schemes have been proposed in the literature. However, most of the existing authentication schemes have found vulnerable to recent attacks and have security flaws. This paper provides and efficient way for authentication using the partial discrete logarithm and sub-tree structure. The proposed scheme has seen effective and more useful in cloud computing environment. The analysis based on the security and the computational cost shows that the proposed authentication scheme proves to be more secure and efficient compared to other protocols that serve the same purposes.

Internet of Things (IoT) devices are becoming ubiquitous in our lives, with applications spanning from the consumer domain to commercial and industrial systems. The steep growth and vast adoption of IoT devices reinforce the importance of sound and robust cybersecurity practices during the device development life-cycles. IoT-related vulnerabilities, if successfully exploited can affect, not only the device itself, but also the application field in which the IoT device operates. Evidently, identifying and addressing every single vulnerability is an arduous, if not impossible, task. Attack taxonomies can assist in classifying attacks and their corresponding vulnerabilities. Security countermeasures and best practices can then be leveraged to mitigate threats and vulnerabilities before they emerge into catastrophic attacks and ensure overall secure IoT operation. Therefore, in this paper, we provide an attack taxonomy which takes into consideration the different layers of IoT stack, i.e., device, infrastructure, communication, and service, and each layer's designated characteristics which can be exploited by adversaries. Furthermore, using nine real-world cybersecurity incidents, that had targeted IoT devices deployed in the consumer, commercial, and industrial sectors, we describe the IoT-related vulnerabilities, exploitation procedures, attacks, impacts, and potential mitigation mechanisms and protection strategies. These (and many other) incidents highlight the underlying security concerns of IoT systems and demonstrate the potential attack impacts of such connected ecosystems, while the proposed taxonomy provides a systematic procedure to categorize attacks based on the affected layer and corresponding impact., Comment: IEEE Internet of Things Journal

Constraint Application Protocol (CoAP), an application layer based protocol, is a compressed version of HTTP protocol that is used for communication between lightweight resource constraint devices in Internet of Things (IoT) network. The CoAP protocol is generally associated with connectionless User Datagram Protocol (UDP) and works based on Representational State Transfer architecture. The CoAP is associated with Datagram Transport Layer Security (DTLS) protocol for establishing a secure session using the existing algorithms like Lightweight Establishment of Secure Session for communication between various IoT devices and remote server. However, several limitations regarding the key management, session establishment and multi-cast message communication within the DTLS layer are present in CoAP. Hence, development of an efficient protocol for secure session establishment of CoAP is required for IoT communication. Thus, to overcome the existing limitations related to key management and multicast security in CoAP, we have proposed an efficient and secure communication scheme to establish secure session key between IoT devices and remote server using lightweight elliptic curve cryptography (ECC). The proposed ECC-based CoAP is referred to as ECC-CoAP that provides a CoAP implementation for authentication in IoT network. A number of well-known cryptographic attacks are analyzed for validating the security strength of the ECC-CoAP and found that all these attacks are well defended. The performance analysis of the ECC-CoAP shows that our scheme is lightweight and secure.

The Internet of Things (IoT) enables all objects to connect to the Internet and exchange data via different emerging technologies, which makes the intelligent identification and management a reality. Wireless sensor networks (WSNs), as a crucial basis of IoT, have been applied in many fields like smart health care and smart transportation. With the development of WSNs, data security has attracted more and more attention, and user authentication is a popular mechanism to ensure the information security of WSNs. Recently, many authentication mechanisms for wireless medical sensor networks (WMSNs) have been proposed, but most of the protocols cannot achieve the features of local password change and forward secrecy while resisting stolen smart card attack. To enhance the security based on previous work, an ECC-based secure three-factor authentication protocol with forward secrecy for WMSN is proposed in this paper. It utilizes a fuzzy commitment scheme to handle the biometric information. Meanwhile, fuzzy verifier and honey_list techniques are used to solve the contradiction of local password verification and mobile device lost attack. The security of our protocol is evaluated by provable security, Proverif tool, and information analysis. Besides, the comparisons with the relevant protocols are given, and the results indicate that our protocol is robust and secure for WMSN systems.

Object segmentation of monitor systems based on the Internet of drones plays an important role in the practical applications of wide-area smart-city intelligent monitoring systems. It is an important step for extracting objects from remote-sensing images, and provides a reliable theoretical basis for key property monitoring, environmental monitoring, disaster monitoring, and agricultural monitoring. To improve the accuracy of object segmentation and to solve the problem of inadequate edge recognition, a joint-learning segmentation scheme was designed that combines the conditional random field (CRF) model with an improved U-net model. It employs the improved U-net model as the front-end model of the joint-learning framework for feature fusion and the CRF model as the back-end of the joint-learning framework for transforming to gradient optimization-based recurrent neural networks. The joint-learning framework enables the front and back parts to interact with each other to obtain the location of the target and its classification information accurately. The joint-learning framework was realized on open datasets and compared with state-of-the-art remote-sensing image segmentation algorithms. The experiment results show that the accuracy of the ground object segmentation improved to 86.1%, which is an encouraging improvement.

Industrial Internet of Things (IIoT) refers to all kinds of equipment in industrial production, whether it is machine equipment in factories or engine in automobiles and airplanes, on which sensors are installed and connected to wireless network terminals to collect and share data. At present, because IIoT system is an open, distributed and heterogeneous system, it is a great challenge to implement trusted communication in IIoT. Blockchain technology has become a new way to solve the cooperative trust issues of industrial Internet of Things. Using blockchain, a tamper-proof system can be built, which can be used as an audit tool for hardware products of industrial Internet of Things from chip to whole equipment. Blockchain improve productivity and operational efficiency of IIoT through smart contract, allowing machines to manage themselves. To apply blockchain technology in IIoT, one of the main issues is to solve the security and efficiency problems of consensus protocols. In this paper, a reputation scheme is proposed to encourage normal nodes and abnormal nodes both to participate in network collaboration in a good way. In order to guide the behavior, a credit based incentive approach is proposed. The reward and punishment factor is designed in the revenue payment function of reputation. The effectiveness is that the cooperative behavior is rewarded, and the non cooperative behavior is punished. The main advantage of our scheme is that our reputation based incentive module can be implemented on state-of-the-art PoX protocols, which is called PoRX,and can make PoX protocols achieve better consensus states. Therefore, it would benefit the applications of IIoT with blockchain. A prototype has been implemented and the experimental results shows that our scheme can effectively encourage the cooperative behavior of the nodes in IIoT, that can benefit the network.

© 2020 The Authors. Published by IEEE. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1109/ACCESS.2020.3011259 Cybercrimes are cases of indictable offences and misdemeanors that involve computers or communication tools as targets and commission instruments or are associated with the prevalence of computer technology. Common forms of cybercrimes are child pornography, cyberstalking, identity theft, cyber laundering, credit card theft, cyber terrorism, drug sale, data leakage, sexually explicit content, phishing, and other forms of cyber hacking. They mostly lead to a privacy breach, security violation, business loss, financial fraud, or damage in public and government properties. Thus, this study intensively reviews cybercrime detection and prevention techniques. It first explores the different types of cybercrimes and discusses their threats against privacy and security in computer systems. Then, it describes the strategies that cybercriminals may utilize in committing these crimes against individuals, organizations, and societies. It also reviews the existing techniques of cybercrime detection and prevention. It objectively discusses the strengths and critically analyzes the vulnerabilities of each technique. Finally, it provides recommendations for the development of a cybercrime detection model that can detect cybercrimes effectively compared with the existing techniques.

Blockchain, a distributed ledger technology, can potentially be deployed in a wide range of applications. Among these applications, decentralized payment systems (e.g. Bitcoin) have been one of the most mature blockchain applications with widespread adoption. While the early designs (e.g. Bitcoin) are often the currency of choice by cybercriminals (e.g., in ransomware incidents), they only provide pseudo-anonymity, in the sense that anyone can deanonymize Bitcoin transactions by using information in the blockchain. To strengthen the privacy protection of decentralized payment systems, a number of solutions such as Monero and Zerocash have been proposed. However, completely Decentralized Anonymous Payment (DAP) systems can be criminally exploited, for example in online extortion and money laundering activities. Recognizing the importance of regulation, we present a novel definition of Decentralized Conditional Anonymous Payment (DCAP) and describe the corresponding security requirements. In order to construct a concrete DCAP system, we first design a Condition Anonymous Payment (CAP) scheme (based on our proposed signature of knowledge), whose security can be demonstrated under the defined formal semantic and security models. To demonstrate utility, we compare the performance of our proposal with that of Zerocash under the same parameters and testing environment.

Cognitive radio (CR) provides a way to utilize the radio resources in a smart manner. In order to exploit the benefits of CR technology either in CR systems or in CR enabled wireless sensor networks, CR based smart grids, CR based wireless body area networks (WBANs) and CR based Internet of Things (IOTs), it is necessary to characterize the radio traffic in a realistic way. In perspective of CR operating models, the radio traffic can be modeled either using conditional or unconditional modeling approaches. In unconditional modeling approach, observed data traffic is solely considered as interference while in conditional modeling approach the observed data traffic is first classified as signal and noise. Prior knowledge about the statistics of interference in unconditional modeling approach while signal (primary user) and noise (secondary user) characterization in conditional modeling framework will help in efficient utilization of radio resources within CR-enabled radio environment. Furthermore, performance analysis is carried out for both the unconditional and conditional models based on devised information theoretic criterion. Multivariate Gaussian mixture (MGM) as a special case of hidden markov random field (HMRF) and Gaussian mixture (GM) are suitable candidate models for the observed data traffic in ISM (Industrial, Scientific and Medical) band based on devised criterion in unconditional and conditional models, respectively.

Evidently, criminal network activities have shown an increasing trend in terms of complexity and frequency, particularly with the advent of social media and modern telecommunication systems. In these circumstances, law enforcement agencies have to be armed with advance criminal network analysis (CNA) tools capable of uncovering with speed, probable key hidden relationships (links/edges) and players (nodes) in order to anticipate, undermine and cripple organised crime syndicates and activities. The development of link prediction models for network orientated domains is based on Social Network Analysis (SNA) methods and models. The key objective of this research is to develop a link prediction model that incorporates a fusion of metadata (i.e. environment data sources such as arrest warrants, judicial judgement, wiretap records and police station proximity) with a time-evolving criminal dataset in order to be aware of real-world situations to improve the quality of link prediction. Based on the review of related work, most of the models are constructed by leveraging on classical machine learning (ML) techniques such as support vector machine (SVM) without metadata fusion. The problem with the use of classical ML techniques is the lack of available domain dataset which is sufficiently large for training purpose. Compared to sociaI network, criminal network dataset by nature tends to relatively much smaller. In view of this, deep reinforcement learning (DRL) technique which could improve the training of models with the self-generated dataset is leveraged upon to construct the model. In this research, a purely time-evolving DRL model (TDRL-CNA) without metadata fusion is designed as a baseline for comparison with the metadata fusion model (FDRL-CNA). The experimental results show that the predictive accuracy of new and recurrent links by the FDRL-CNA model is higher than the baseline TDRL-CNA model that does not factor data fusion from different data sources.

The traditional methods used for the identification of individuals such as personal identification numbers (PINs), identification tags, etc., are vulnerable as they are easily compromised by the hackers. In this paper, we aim to focus on the existing multibiometric systems that use hand based modalities for the identification of individuals. We cover the existing multibiometric systems in the context of various feature extraction schemes, along with an analysis of their performance using one of the performance measures used for biometric systems. Later, we cover the literature on template protection including various cancelable biometrics and biometric cryptosystems and provide a brief comment about the methods used for multibiometric template protection. Finally, we discuss various open issues and challenges faced by researchers and propose some future directions that can enhance the security of multibiometric templates.

In the advanced technological world, vehicular communications are used in numerous business and safety applications for smart traffic management. With the help of vehicle-to-vehicle (V2V) communication, users can transfer substantial information directly to the reachable vehicles. Generally, the movement of vehicles on the highway is faster than crossroads, thus, to communicate securely and efficiently, a robust vehicular communication system is required. Therefore, a number of message dissemination protocols have been designed by the researchers, but distinct interests (i.e, storage cost, high communication overhead, more execution time, and security threats) are sought in these systems, which lead toward unreliability for on-the-fly communication. Very recently, Limbasiya-Das ( Ieee System Journal , pp 1937–9234, 10.1109/JSYST.2019.2932807) introduced a hash-based secure and efficient V2V data dissemination protocol to transfer significant information at the receiver end. Limbasiya-Das claimed the security of their protocol against numerous security attacks. However, in this comment, we highlight the loopholes of Limbasiya-Das protocol and show that their protocol is vulnerable to impersonation attack. Moreover, it has irrelevant usage of timestamp and does not offer user's anonymity. In addition, it lacks message confidentiality because an adversary can determine and view the messages exchanged among the legitimate vehicles by intercepting the messages disseminated over public channel. Finally, we provide probable remedy to mitigate the deficiencies of Limbasiya-Das protocol.

Ciphertext-policy attribute-based encryption (CP-ABE) scheme can be deployed in a mobile cloud environment to ensure that data outsourced to the cloud will be protected from unauthorized access. Since mobile devices are generally resource-constrained, CP-ABE schemes designed for a mobile cloud deployment should have constant sizes for secret keys and ciphertexts. However, most existing CP-ABE schemes do not provide both constant size ciphertexts and secret keys. Thus, in this paper, we propose a new pairing-based CP-ABE scheme, which offers both constant size ciphertexts and secret keys (CSCTSK) with an expressive AND gate access structure. We then show that the proposed CP-ABE-CSCTSK scheme is secure against chosen-ciphertext adversary in the selective security model, and present a comparative summary to demonstrate the utility of the scheme. Since mobile devices are generally resource-constrained and cloud services are Internet-based and pay-by-use, a key feature in ciphertext-policyAttribute-based encryption (CP-ABE) should be constant sizes for secret keys and ciphertexts.In this paper, we propose a new pairing-based CP-ABE scheme, which offers both constant size ciphertexts and secret keys (CSCTSK) with an expressive AND gate access structure.We then show that the proposed CP-ABE-CSCTSK scheme is secure against chosen-ciphertext adversary in the selective security model, and demonstrate its utility.

The papers in this special section focus on cyber security threats and efforts to ensure secure communications. With rapid and ongoing advances in information and communications technology (ICT) and “expansion” of cyber space, cyber security is, and will continue to be, of crucial importance to the stability of our Internet-connected society. For example, how do we ensure secure communications between servers, network nodes, terminals and user applications across public and private networks, particularly in cyber-physical systems? Defending our cyber space is both a research challenge and an operational challenge. Designing effective security solutions is complicated by the need to carefully balance between security and usability, as well as the amount of efforts and resources required.

The rapid advancement of information and communication technologies (ICT) has drastically augmented the connectivity of actuators, computational elements and smart devices to the physical world. In this context, smart building (SB) serves as a significant domain to automatically handle and control the temperature, humidity, ventilation, safety, lighting, and other building's operations. Moreover, it plays a vital role in implementing the standards of enhanced living environments (ELE) and ambient assisted living (AAL). However, the extensive use of ICT and inflexible architecture of SB pose many challenges including heterogeneity of applications and Internet of things (IoT) devices, security, efficient networking architectures and protocols, energy efficiency, reliability and quality of service (QoS) provisioning. In this perspective, software defined networking (SDN) has gained great attention since it is being evolved as a programmable, and flexible networking framework. This paper is devoted to surveying the research work conducted on the integration of SDN to SB. Consequently, it provides a comprehensive review on SDN enabled SB, its architecture, taxonomy, communication protocols, and challenges for an immersive and interactive experience. It also presents a basic architecture of SDN over traditional networking. Moreover, SDN applications are also exploited to handle the issues of the wireless network. Furthermore, this paper demonstrates the use cases to explain the impact of SDN paradigm on the SBs.

In recent years immense growth of data i.e. big data is observed resulting in a brighter and more optimized future. Big Data demands large computational infrastructure with high–performance processing capabilities. Preparing big data for mining and analysis is a challenging task and requires data to be preprocessed to improve the quality of raw data. The data instance representation and quality are foremost. Data preprocessing is preliminary data mining practice in which raw data is transformed into a format suitable for another processing procedure. Data preprocessing improves the data quality by cleaning, normalizing, transforming and extracting relevant feature from raw data. Data preprocessing significantly improve the performance of machine learning algorithms which in turn leads to accurate data mining. Knowledge discovery from noisy, irrelevant and redundant data is a difficult task therefore precise identification of extreme values and outlier, filling up missing values poses challenges. This paper discusses various big data pre–processing techniques in order to prepare it for mining and analysis tasks.

User authentication is the process that is exercised millions of times around the globe by using different techniques and methods. The most prominent way of authentication is alphanumerical password forms that have been used for decades. Authorized access is becoming a challenging issue because of the introduction of modern technologies. In addition, traditional alphanumerical passwords have significant security issues, for example, humans forget the combination of keys due to the selection of a difficult key combination. Moreover, when they choose an easy key combination, this helps hackers to crack their passwords easily. Traditional passwords are also vulnerable to several types of attacks, for example, dictionary attack, brute force attack, and malware. To provide an easy and more secure authentication technique, a graphical password has been introduced in this paper for consumer electronic devices, which uses an image or a set of images for authentication. We have categorized the existing graphical password methods into recognition-based, cued-recall-based, pure-recall-based, and hybrid techniques. Due to the limitations of the existing graphical passwords, we have introduced a new technique, named graphical random authentication technique (gRAT), which generates a randomized set of images every time a user tries to authenticate him/herself by maintaining the security and usability at the same time. The gRAT technique is also tested by user-centric evaluation in terms of security, usability, usefulness, and utility, and the experimental results show that the proposed technique is more secure and useful in the real-life authentication applications. Deanship of Scientific Research, King Saud University Scopus

Patients with chronic diseases and the elderly will benefit greatly from a close-monitoring healthcare system. A wireless body area network (WBAN) has been widely deployed for this purpose. WBAN sensors collect vast amounts of biosignal data from a patient for long periods of time. These biosignal data need to be efficiently managed and stored for further processing and future reference. This article reviews and compares the frameworks and also evaluates the various methods used to manage WBAN data. We aim to identify the best method of managing WBAN data and present an enhanced architectural platform. It is intended to help researchers and practitioners to further study, modify, and develop quality-of-service (QoS)-based WBAN health-care systems.

The advancement of the cloud storage technology opens up a wide range of possibilities for adaptable data sharing. When sharing data to an extensive number of users with fuzzy identities, the data proprietor must use an appropriate identity-based encryption technique that satisfies both efficiency and security prerequisites. Identity-based encryption is a promising possibility to ensure fuzzy user data sharing while meeting the security essentials; however, it may encounter efficiency trouble in multi-receiver settings. Recently, identity-based encryption has received much attention, and most of the research has aimed to apply the technique in real-world systems. A major concern about using identity-based encryption is the safety of the private keys, as disclosure of secret keys requires the reissuing of encryptions already doled out. The capability to minimize the risks associated with key disclosure is particularly important due to the increased use of mobile and unprotected devices. In this article, we shall propose a forward-secure identity-based encryption technique based on subtree for fuzzy user data sharing under cloud computing environment, and we shall demonstrate that the technique is semantically secure against a chosen subtree and chosen ciphertext attack (IND-CST–CCA). In addition, we will show the superiority of our new technique over the currently existing methods in terms of security and the length of public key. Then, we will also discuss the potential of our new technique to be deployed in pay TV systems and grid security.

In cyber crimes, attackers are becoming more inventive with their exploits and use more sophisticated techniques to bypass the deployed security system. These attacks are targeted and are commonly referred as Advanced Persistent Threats (APTs). The currently available techniques to tackle these attacks are mostly reactive and signature based. Security Information and Event Management (SIEM), a proactive approach is the best solution. However, the major problem with SIEM is tackling huge amount of data in real time that makes it a time consuming and tedious task for security analyst. The use of threat intelligence caters to such issue by prioritizing the level of threat. In this paper, we assign risk score and confidence value to each feed generated at our product “T-Eye platform”. On the basis of these values, we assign a severity score to each feed type. Severity score assigns a level to the threat means prioritize the threat. The results, we achieved for prioritizing the threat is more apparent and accurate. In addition, we optimize the rules of IBM-Q-Radar by using threat feeds generated at T-Eye platform. Furthermore, a huge amount of false positive alarms generated at IBM Q-Radar is reduced to a certain extent.

Blockchain, as a decentralized and distributed public ledger technology in peer-to-peer network, has received considerable attention recently. It applies a linked block structure to verify and store data, and applies the trusted consensus mechanism to synchronize changes in data, which makes it possible to create a tamper-proof digital platform for storing and sharing data. It is believed that blockchain can be utilized in diverse Internet interactive systems (e.g., Internet of Things, supply chain systems, identity management, and so on). However, there are some privacy challenges that may hinder the applications of blockchain. The goal of this survey is to provide some insights into the privacy issues associated with blockchain. We analyze the privacy threats in blockchain and discuss existing cryptographic defense mechanisms, i.e., anonymity and transaction privacy preservation. Furthermore, we summarize some typical implementations of privacy preservation mechanisms in blockchain and explore future research challenges that still need to be addressed in order to preserve privacy when blockchain is used.

Recently, Ostad-Sharif et al. pointed out the susceptibility of three different authentication schemes themed for telecare medicine/medical information systems to key compromise impersonation attack (KCIA). To further address this issue, they proposed an ECC-based authentication and key generation scheme for healthcare applications. In this paper, we show that Ostad-Sharif et al.'s scheme is not only affected with key compromise impersonation attack but also suffers from a key compromise password guessing attack. Several papers have been published by the researchers by applying KCIA on existing authentication protocols. Before any further move in research in this direction, researchers must contemplate about KCIA. We conclude this article with a rigorous analysis of KCIA along with two questions to ponderon for the research community working in this field.

The prediction of hidden or missing links in a criminal network, which represent possible interactions between individuals, is a significant problem. The criminal network prediction models commonly rely on Social Network Analysis (SNA) metrics. These models leverage on machine learning (ML) techniques to enhance the predictive accuracy of the models and processing speed. The problem with the use of classical ML techniques such as support vector machine (SVM), is the dependency on the availability of large dataset for training purpose. However, recent ground breaking advances in the research of deep reinforcement learning (DRL) techniques have developed methods of training ML models through self-generated dataset. In view of this, DRL could be applied to other domains with relatively smaller dataset such as criminal networks. Prior to this research, few, if any, previous works have explored the prediction of links within criminal networks that could appear and/or disappear over time by leveraging on DRL technique. Therefore, in this paper, the primary objective is to construct a time-based link prediction model (TDRL) by leveraging on DRL technique to train using a relatively small real-world criminal dataset that evolves over time. The experimental results indicate that the predictive accuracy of the DRL model trained on the temporal dataset is significantly better than other ML models that are trained only with the dataset at specific snapshot in time.

Agriculture is a fundamental pillar of society, bearing a crucial role in shaping the economy of the country and provides huge financial prospects and the possibility of employment to the ...

Autonomous vehicles (AVs) offer a wide range of promising benefits by reducing traffic accidents, environmental pollution, traffic congestion and land usage etc. However, to reap the intended benefits of AVs, it is inevitable that this technology should be trusted and accepted by the public. The consumer’s substantial trust upon AVs will lead to its widespread adoption in the real-life. It is well understood that the preservation of strong security and privacy features influence a consumer’s trust on a product in a positive manner. In this paper, we introduce a novel concept of digital labels for AVs to increase consumers awareness and trust regarding the security level of their vehicle. We present an architecture called Cybersecurity Box (CSBox) that leverages digital labels to display and inform consumers and passengers about cybersecurity status of the AV in use. The introduction of cybersecurity digital labels on the dashboard of AVs would attempt to increase the trust level of consumers and passengers on this promising technology.

Cloud data centers rely on virtualization to run a diverse set of applications. Container technology allows for a more lightweight execution, in comparison with popular Virtual Machines. Efficient scheduling of containers is still challenging due to varying request arrival patterns, application-specific resource consumption and resource heterogeneity in physical servers. Besides, containers are also more prone to resource contention and performance interference. Cloud providers need to overcome these challenges with a goal in mind: maximize resource utilization to satisfy as many requests as possible. This paper introduces RLSched, a deep reinforcement learning-based (DRL) scheduler that is self-adaptive and automatically captures the resource usage dynamics in the data center. The scheduler is based on a decentralized actor-critic multi-agent architecture that enables for parallel execution and faster convergence. RLSched relies on an enhanced network model with action shaping, which filters invalid actions and prevents the agent to fall into a sub-optimal policy. The proposed scheduler is compared against other state-of-the-art DRL methods on a simulated data center environment based on real traces from Microsoft Azure. The results show faster convergence and higher number of containers placed per session.

The Internet of drones (IoD) is a very useful application of the Internet of things (IoT) and it can help the daily life comfort through various functions including the smart city surveillance. The IoD can enhance the comfort to reach inaccessible and hard to access sites and can save lot of effort, time and cost. However, in addition to traditional threats, the IoD may suffer from new threats and requires customized methods to combat the security weaknesses. Very recently, Wazid et al. proposed a security solution for securing IoD application scenario and claimed its security. However, in this paper we show that their scheme cannot resist stolen verifier and traceability attacks. Moreover, an attacker with access to the verifier, can impersonate any user, drone or server of the system. An enhanced scheme is then proposed to cope with these weaknesses. The security claims of proposed scheme are endorsed by formal and informal security analysis. Moreover, the performance and security comparisons show that proposed scheme completes a cycle of authentication with a slight increase in computation time, but it offers all the required security features as compared with the scheme of Wazid et al.

In an Intelligent Precision Agriculture (IPA), several Internet of Things (IoT) smart devices and drones can be deployed to monitor an agricultural environment. The drones can be further utilized to collect the data from smart devices and send to the Ground Station Server ( G S S ) . However, insecure communication among the smart devices, drones and the G S S make the IoT agriculture environment vulnerable to various potential attacks. For this goal, a new authentication and key management scheme for IoT-enabled IPA, called AKMS-AgriIoT, has been put forward with the private blockchain-based solution. The blocks formed with the encrypted transactions and their respective signatures by the G S S are mined by the cloud servers to verify and add the blocks in the private blockchain center. A detailed security analysis and comparative study reveal that the proposed AKMS-AgriIoT supports better security, and provides more functionality features, less communication costs and comparable computation costs as compared to other relevant schemes. In addition, the blockchain-based implementation on the proposed AKMS-AgriIoT has been also carried out.

Cloud storage is widely used by both individual and organizational users due to the many benefits, such as scalability, ubiquitous access, and low maintenance cost (and generally free for individual users). However, there are known security and privacy issues in migrating data to the cloud. To ensure or verify data integrity, a number of cloud data integrity checking schemes with different properties have been presented in the literature. Most existing schemes were subsequently found to be insecure or have high computation and communication costs. More recently in 2016, Yu et al. (Future Gener Comput Syst 62:85–91, 2016) proposed an identity-based auditing scheme for checking the integrity of cloud data. However, in this paper, we reveal that the scheme is vulnerable to data recovery attack. We also present a new identity-based public auditing scheme and formally prove the security of the scheme under the RSA assumption with large public exponents in the random oracle model. We then evaluate the performance of our proposed scheme and demonstrate that in comparison with Yu et al.'s scheme, our proposal is more practical in real-world applications. Refereed/Peer-reviewed

Today, digital multimedia (audio, video, images) is a common evidential source in litigation and criminal justice proceedings, and, not surprisingly, multimedia forensics is an active research area. One particular challenge faced by multimedia forensic researchers is the lack of a comprehensive and publicly available data-set for evaluating existing and new algorithms. This paper presents a digital audio forensic data-set, designed to facilitate evaluation of audio forensic algorithms (e.g. microphone identification, acoustic environment identification and splice detection). This paper also briefly describes the data-collection settings, microphones, speakers, languages and notations used. Existing tamper-detection techniques rely on artefacts due to recording devices, codec and/or the acoustic environment in the audio in question. Experimental results show that the selected approaches achieved promising results. Refereed/Peer-reviewed

Major provisioning of cloud computing is mainly delivered via Software as a Service, Platform as a Service and Infrastructure as a Service. However, these service delivery models are vulnerable to a range of security attacks, exploiting both cloud specific and existing web service vulnerabilities. Taxonomies are a useful tool for system designers as they provide a systematic way of understanding, identifying and addressing security risks. In this research work, Cloud based attacks and vulnerabilities are collected and classify with respect to their cloud models. We also present taxonomy of cloud security attacks and potential mitigation strategies with the aim of providing an in-depth understanding of security requirements in the cloud environment. We also highlight the importance of intrusion detection and prevention as a service. Display Omitted Cloud Security Attacks Taxonomy.Cloud Intrusion Detection and Prevention as a Service.Intrusion detection in cloud computing service models.Need for in-depth advanced cloud protection systems.