Machine Learning (ML) models have proven effective in optimizing wireless and private networks. However, recent research highlights the threat of data poisoning attacks on ML models. To analyze such a threat on an industrial 5G private network, this work investigates the effectiveness of data poisoning against it. We primarily focus on poisoning four ML models: Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), and Artificial Neural Networks (ANN), at three poisoning levels: 10%, 15%, and 20%. Our research shows that all models introduce instability in the network, rather than optimization, whereas neural networks are less affected by data poisoning compared to other models. At the 20 % data poisoning level, model performance degrades by about 6-7% for SVM, RF, and DT, while ANN shows a minimal disruption of 2%.

Industry 4.0, driven by enhanced connectivity by wireless technologies such as 5G and Wi-Fi 6, fosters flexible industrial scenarios for high-yield production and services. Private 5G networks and 802.11ax networks in unlicensed spectrum offer very unique opportunities, however existing techniques limit the flexibility needed to serve diverse industrial use cases. In order to address a subset of these challenges, this paper offers a solution for time-sensitive application use cases. A new technique is proposed to enable data-driven operations through Machine Learning for technologies sharing unlicensed bands. This enables proportionate spectrum sharing informed by data to improve critical applications performance metrics. The results presented reveal improved performance to serve critical industrial operations, without degrading spectrum utilization.

With the growing popularity of the Internet of Things (IoT), billions of devices are anticipated to be deployed in various industries without establishing trust between them. In environments without pre-established trust, reputation systems provide an effective method of assessing the trustworthiness of IoT devices. There has been considerable literature on deploying reputation systems in industries that have not yet established trust among themselves. Therefore, the article reviews published studies on reputation systems for IoT applications to date, focusing on decentralised systems and decentralised systems using blockchain technology. These studies are evaluated regarding security (including integrity and privacy) and non-security requirements to highlight open research challenges. In alignment with this, an analysis and summary of the existing review studies on reputation systems for particular IoT applications are presented, demonstrating the need for a review article to consider all IoT applications and those that have not been explored. The IoT applications and sub-applications are described, and their problem statement, literature to date and research gap are comprehensively evaluated. Finally, the open research challenges concerning reputation systems are reviewed and addressed to provide the researcher with a road map of potential research directions.

Abstract Machine learning has become prevalent in transforming diverse aspects of our daily lives through intelligent digital solutions. Advanced disease diagnosis, autonomous vehicular systems, and automated threat detection and triage are some prominent use cases. Furthermore, the increasing use of machine learning in critical national infrastructures such as smart grids, transport, and natural resources makes it an attractive target for adversaries. The threat to machine learning systems is aggravated due to the ability of mal-actors to reverse engineer publicly available models, gaining insight into the algorithms underpinning these models. Focusing on the threat landscape for machine learning systems, we have conducted an in-depth analysis to critically examine the security and privacy threats to machine learning and the factors involved in developing these adversarial attacks. Our analysis highlighted that feature engineering, model architecture, and targeted system knowledge are crucial aspects in formulating these attacks. Furthermore, one successful attack can lead to other attacks; for instance, poisoning attacks can lead to membership inference and backdoor attacks. We have also reviewed the literature concerning methods and techniques to mitigate these threats whilst identifying their limitations including data sanitization, adversarial training, and differential privacy. Cleaning and sanitizing datasets may lead to other challenges, including underfitting and affecting model performance, whereas differential privacy does not completely preserve model’s privacy. Leveraging the analysis of attack surfaces and mitigation techniques, we identify potential research directions to improve the trustworthiness of machine learning systems.

Cyber attacks on the Internet of Things (IoT) have seen a significant increase in recent years. This is primarily due to the widespread adoption and prevalence of IoT within domestic and critical national infrastructures, as well as inherent security vulnerabilities within IoT endpoints. Therein, botnets have emerged as a major threat to IoT-based infrastructures targeting firmware vulnerabilities such as weak or default passwords to assemble an army of compromised devices which can serve as a lethal cyber-weapon against target systems, networks, and services. In this paper, we present our efforts to mitigate this challenge through the development of an intrusion detection system that resides within an IoT device to provide enhanced visibility thereby achieving security hardening of such devices. The device-level intrusion detection presented here is part of our research framework BTC_SIGBDS (Blockchain-powered, Trustworthy, Collaborative, Signature-based Botnet Detection System). We identify the research challenge through a systematic critical review of existing literature and present detailed design of the device-level component of the BTC_SIGBDS framework. We use a signature-based detection scheme with trusted signature updates to strengthen protection against emerging attacks. We have evaluated the suitability and enhanced the capability through the generation of custom signatures of two of the most famous signature-based IDS with ISOT, IoT23, and BoTIoT datasets to assess the effectiveness with respect to detection of anomalous traffic within a typical resource-constrained IoT network in terms of number of alerts, detection rates, detection time as well as in terms of peak CPU and memory usage.

Abstract Over the past decade, there has been a rapidly rising trend of malware (ransomware) that limits user access by encrypting the data and demanding the ransom against the decryption key. In most cases, such encryption may lead to a permanent data loss. In order to prevent this unwanted encryption, we propose a method based on Moving Target Defense (MTD) approach. Our method is based on the alteration of the attack surface to reduce the attack success ratio. We have used multiple layers of MTD. The first layer generates random extensions that hide the existing known file extensions. This will protect user files against those ransomware variants which encrypt files having some specific extensions. Our second layer of protection uses event‐based MTD in which tasks are scheduled to change file extensions at the occurrence of specific events which mostly occur due to the execution of ransomware in the system. As a result of our proposed method, we have successfully protected user files against well‐known ransomware variants such as WannaCry, Cerber, Locky, Tesla, Revil, Bitlocker, Darkside, Ranzy.

During the development of software systems, even with advanced planning, problems with quality and security occur. These defects may result in threats to program development and maintenance. Therefore, to control and minimise these defects, machine learning can be used to improve the quality and security of software systems. This special issue focuses on recent advances in architecture, algorithms, optimisation, and models for machine learning applied to quality and security in software systems. After a rigorous review according to relevance, originality, technical novelties, and presentation quality, we selected 4 manuscripts. A summary of these accepted papers is outlined below. In the first paper entitled “Robust Malware Identification via Deep Temporal Convolutional Network with Symmetric Cross Entropy Learning” by Sun et al., the authors propose a robust Malware identification method using the temporal convolutional network (TCN). Moreover, word embedding techniques are generally utilised to understand the contextual relationship between the input operation code (opcode) and application programming interface (API) function names in many cases. Here, considering the numerous unlabelled samples in practical intelligent environments, the authors pre-train the TCN model on an unlabelled set using a word embedding method, that is, word2vec. In the experiments, the proposed method is compared to several traditional statistical methods and more recent neural networks on a synthetic Malware dataset and a real-world dataset. The performance comparisons demonstrate the better performance and noise robustness of the proposed method, that the proposed method can yield the best identification accuracy of 98.75% in real-world scenarios. In the second paper entitled “Just-In-Time Defect Prediction Enhanced by the Joint Method of Line Label Fusion and File Filtering” by Zhang et al., the authors propose a Just-in-Time defect prediction model enhanced by the joint method of line label Fusion and file Filtering (JIT-FF). First, to distinguish added and removed lines while preserving the original software changes information, the authors represent the code changes as original, added, and removed codes according to line labels. Second, to obtain semantics-enhanced code representation, the authors propose a cross-attention-based line label fusion method to perform complementary feature enhancement. Third, to generate code changes containing fewer defect-irrelevant files, the authors formalise the file filtering as a sequential decision problem and propose a reinforcement learning-based file filtering method. Finally, based on generated code changes, CodeBERT-based commit representation and multi-layer perceptron-based defect prediction are performed to identify the defective software changes. The experiments demonstrate that JIT-FF predicts defective software changes more effectively. In the third paper entitled “Android Malware Detection via Efficient API Call Sequences Extraction and Machine Learning Classifiers” by Wang et al., the authors propose a novel Android malware detection framework, where the authors contribute an efficient API call sequences extraction algorithm and an investigation of different types of classifiers. In API call sequences extraction, the authors propose an algorithm for transforming the function call graph from a multigraph into a directed simple graph, which successfully avoids unnecessary repetitive path searching. The authors also propose a pruning search, which further reduces the number of paths to be searched. The developed algorithm greatly reduces the time complexity. The authors generate the transition matrix as classification features and investigate three types of machine learning classifiers to complete the malware detection task. The experiments are performed on real-world APKs, and the results demonstrate that the proposed method reduces the running time and produces high detection accuracy. In the fourth paper entitled “Selecting Reliable Blockchain Peers via Hybrid Blockchain Reliability Prediction” by Zheng et al., the authors propose H-BRP, a Hybrid Blockchain Reliability Prediction model, to extract the blockchain reliability factors and then make the personalised prediction for each user. Connecting to unreliable blockchain peers is prone to resource waste and even loss of cryptocurrencies by repeated transactions. The proposed model primarily aims to select reliable blockchain peers and to evaluate and predict their reliability. Comprehensive experiments conducted on 100 blockchain requesters and 200 blockchain peers demonstrate the effectiveness of the proposed H-BRP model. Furthermore, the implementation and dataset of 2,000,000 test cases are released. The Guest Editors would like to express their deep gratitude to all the authors who have submitted their valuable contributions, and to the numerous and highly qualified anonymous reviewers. We think that the selected contributions, which represent the current state of the art in the field, will be of great interest to the community. We also would like to thank the IET Software publication staff members for their continuous support and dedication. We particularly appreciate the relentless support and encouragement granted to us by Prof. Hana Chockler, the Editor-in-Chief of IET Software. Honghao Gao is currently with the School of Computer Engineering and Science, Shanghai University, China. He is also a Professor at the College of Future Industry, Gachon University, Korea. His research interests include Software Intelligence, Cloud/Edge Computing, and AI4Healthcare. He has publications in IEEE TII, IEEE T-ITS, IEEE TNNLS, IEEE TMM, IEEE TSC, IEEE TCC, IEEE TFS, IEEE TNSE, IEEE TNSM, IEEE TCCN, IEEE TGCN, IEEE TCSS, IEEE TETCI, IEEE TCE, IEEE/ACM TCBB etc. He has broad working experience in cooperative industry-university-research. He is a European Union Institutions-appointed external expert for reviewing and monitoring EU Project, is a member of the EPSRC Peer Review Associate College for UK Research and Innovation in the UK, and a founding member of the IEEE Computer Society Smart Manufacturing Standards Committee. Prof. Gao is a Fellow of the Institution of Engineering and Technology (IET), a Fellow of the British Computer Society (BCS), and a Member of the European Academy of Sciences and Arts (EASA). Dr. Walayat Hussain is a Visiting Fellow at the School of Computer Science. Currently he is a Senior Lecturer and the Head of Discipline-IT at the Australian Catholic University, Australia. He served as a Lecturer and Postdoctoral Research Fellow at the Victoria University, Melbourne, School of Information, Systems and Modelling, University of Technology Sydney Australia for several years. Prior to joining UTS, he worked as an Assistant Professor and the Postgraduate program coordinator at BUITEMS University for many years. Walayat's research areas are Distributed Systems, AI, Information Systems, Computational Intelligence, Machine Learning, Business Intelligence, Decision Support Systems, and Usability Engineering. His work has been published in different top-ranked reputable ERA-A*, A, Q1 journals and conferences such as IEEE Transactions on Fuzzy Systems, IEEE Transactions on Service Computing, Future Generation Computer Systems, Information Sciences, International Journal of Intelligent Systems, Information Systems, Journal of Ambient Intelligence and Humanized Computing, Neural Computing and Applications, The Computer Journal (Oxford University Press), Computer & Industrial Engineering, IEEE Access, ACM TOMM, IEEE TGCN, IEEE TETCI, International Journal of Communication Systems, Mobile Networks and Applications, GJFSM, FUZZ-IEEE, ICONIP, and many others. Ramón J. Durán Barroso received the degree in telecommunication engineering and the Ph.D. degree from the University of Valladolid, Spain, in 2002 and 2008 respectively. He currently works as an Associate Professor with the University of Valladolid. He is also the Coordinator of the Spanish Research Thematic Network “Go2Edge: Engineering Future Secure Edge Computing Networks, Systems and Services” composed of 15 entities and the H2020 IoTalentum Project. He has authored more than 150 papers in international journals and conferences. His current research interests include the use of artificial intelligence techniques for the design, optimisation, and operation of future heterogeneous networks, multi-access edge computing, and network function virtualisation. Dr. Junaid Arshad has 14 years of research experience and expertise in investigating and addressing cybersecurity challenges for diverse computing paradigms such as Grid computing, Cloud computing, IoT, and blockchain. He is actively engaged in cutting-edge R&D distributed ledger technologies including blockchains, Tangle and Hashgraphs, investigating novel challenges to improve state of the art for such technologies as well as their use to solve real-world challenges. Junaid is an alumnus of the Innovate UK & DCMS funded CyberASAP programme, commercially prototyping the CyMonD system for effective monitoring and defence of IoT-based systems against cyber-threats. Junaid has successfully achieved research funding from UK and overseas funding agencies, and has worked as a security specialist for a number of EU funded projects with experience of developing bespoke security solutions. He is also actively involved in research surrounding analysis of malware for mobile and IoT devices focusing on profiling malicious behavior to achieve runtime detection and defense. Junaid has successfully published high quality research within cybersecurity and has more than 50 publications at high quality venues including journals, book chapters, conferences and workshops. He is an Associate Editor for the Cluster Computing and IEEE Access journals and regularly serves on program and review committees of several journals and conferences. Yuyu Yin received the Ph.D. degree in computer science from Zhejiang University in 2010. He is currently a Professor with the College of Computer, Hangzhou Dianzi University, Hangzhou, China. He is also a Supervisor of master’s students with the School of Computer Engineering and Science, Shanghai University, Shanghai, China. He has authored or coauthored more than 40 articles in journals and refereed conferences, such as Sensors, Entropy, IJSEKE, Mobile Information Systems, ICWS, and SEKE. His research interests include service computing, cloud computing, and business process management. Dr. Yin is also a member of the China Computer Federation (CCF) and the CCF Service Computing Technical Committee. He has organised more than ten international conferences and workshops, such as FMSC 2011–2017 and DISA 2012 and 2017–2018. He has served as a Guest Editor for the Journal of Information Science and Engineering and International Journal of Software Engineering and Knowledge Engineering and a Reviewer for the IEEE Transaction on Industry Informatics, Journal of Database Management, and Future Generation Computer Systems.

Cyber threats continue to grow with novel methods to attack computing systems, highlighting the need for sophisticated mechanisms and techniques to protect against such dynamic threats. Contemporary cyber defence mechanisms utilise a range of methods which rely on monitoring network or system-level events. However, with the growing use of the dark web by mal-actors to share exploits, breaches, and data leaks, the use of such information to strengthen defence mechanisms becomes an intriguing prospect. In this paper, we present our efforts to develop a text mining engine (Threat Miner) which analyses data from dark web forums and transforms it into actionable intelligence. Leveraging cutting-edge machine learning techniques and utilising a bespoke threat dictionary, Threat Miner extracts useful information from dark web forums into STIX form, enabling it to be used with threat intelligence platforms. We also present the results of a thorough evaluation of our scheme which was conducted with the CrimeBB dataset [1] to understand the feasibility of the approach as well as its effectiveness in strengthening defence capability against cyber threats.

Blockchain has introduced new opportunities with the potential to enhance systems and services across diverse application domains. Fundamental characteristics of blockchains such as immutability, decentralisation, transparency and traceability have a profound role in this. However, integration with contemporary systems and among disparate blockchain-based applications is a non-trivial challenge primarily due to differences with respect to platforms, consensus mechanism, and governance. Although this challenge has received some attention from the research community, it requires careful analysis to analyse existing work and ascertain gaps to achieve effective and efficient solution to this challenge. This article presents a thorough systematic review of existing research within blockchain interoperability highlighting significant contributions. Leveraging this analysis, the article presents an internet-inspired framework (Chain-Net) to facilitate interoperability within blockchain-based systems whereby two systems within independent Blockchain networks can securely exchange data with each other. This is achieved by using gateway module at each network. This module is a lightweight node registered by the Blockchain network, equipped with discovery service to lookup a target blockchain, and is responsible for forwarding cross-chain transactions to gateway module at the target blockchain. Gateway module plays a vital role in the Chain-Net model, as it holds a cross-chain transaction in a pending state until a confirmation is received from the target blockchain, thus maintaining the record integrity between the two chains. The article presents our efforts to evaluate the proposed blockchain interoperability framework against success criteria based on our analysis of the blockchain interoperability challenge.

V2V messaging systems enable vehicles to exchange safety related information with each other and support road safety and traffic efficiency applications. The effectiveness of these applications depends on the correctness of the information reported in the V2V messages. Consequently, the possibility that malicious agents may send false information is a major concern. The physical features of a transmission are relatively difficult to fake, and one of the most effective ways to detect lying is to check for consistency of these features with vehicle position information in the message. In this paper, we propose a message consistency checking scheme whereby a vehicle acting independently can utilise the strength and variability of received signals to estimate the distance from a transmitting vehicle without prior knowledge of the environment (building density, traffic conditions, etc.). The distance estimate can then be used to check the correctness of the reported position. We show through simulation that our RMCSS method can detect false information with an accuracy of about 90% for separation distances less than 100m. We believe this is sufficient for the method to be a valuable adjunct to use of digital signatures to establish trust.

Today's smartphones are equipped with a large number of powerful value-added sensors and features, such as a low-power Bluetooth sensor, powerful embedded sensors, such as the digital compass, accelerometer, GPS sensors, Wi-Fi capabilities, microphone, humidity sensors, health tracking sensors, and a camera, etc. These value-added sensors have revolutionized the lives of the human being in many ways, such as tracking the health of the patients and the movement of doctors, tracking employees movement in large manufacturing units, monitoring the environment, etc. These embedded sensors could also be used for large-scale personal, group, and community sensing applications especially tracing the spread of certain diseases. Governments and regulators are turning to use these features to trace the people's thoughts to have symptoms of certain diseases or viruses, e.g., COVID-19. The outbreak of COVID-19 in December 2019, has seen a surge of the mobile applications for tracing, tracking, and isolating the persons showing COVID-19 symptoms to limit the spread of the disease to the larger community. The use of embedded sensors could disclose private information of the users, thus potentially bring a threat to the privacy and security of users. In this article, we analyzed a large set of smartphone applications that have been designed to contain the spread of the COVID-19 virus and bring the people back to normal life. Specifically, we have analyzed what type of permission these smartphone apps require, whether these permissions are necessary for the track and trace, how data from the user devices are transported to the analytic center, and analyzing the security measures these apps have deployed to ensure the privacy and security of users.

Abstract The number of unsolicited and advertisement telephony calls over traditional and Internet telephony has rapidly increased over recent few years. Every year, the telecommunication regulators, law enforcement agencies and telecommunication operators receive a very large number of complaints against these unsolicited, unwanted calls. These unwanted calls not only bring financial loss to the users of the telephony but also annoy them with unwanted ringing alerts. Therefore, it is important for the operators to block telephony spammers at the edge of the network so to gain trust of their customers. In this paper, we propose a novel spam detection system by incorporating different social network features for combating unwanted callers at the edge of the network. To this extent the reputation of each caller is computed by processing call detailed records of user using three social network features that are the frequency of the calls between caller and the callee, the duration between caller and the callee and the number of outgoing partners associated with the caller. Once the reputation of the caller is computed, the caller is then places in a spam and non‐spam clusters using unsupervised machine learning. The performance of the proposed approach is evaluated using a synthetic dataset generated by simulating the social behaviour of the spammers and the non‐spammers. The evaluation results reveal that the proposed approach is highly effective in blocking spammer with 2% false positive rate under a large number of spammers. Moreover, the proposed approach does not require any change in the underlying VoIP network architecture, and also does not introduce any additional signalling delay in a call set‐up phase. Copyright © 2016 John Wiley & Sons, Ltd.

Clouds represent a major paradigm shift from contemporary systems, inspiring the contemporary approach to computing. They present fascinating opportunities to address dynamic user requirements with the provision of flexible computing infrastructures that are available on demand. Clouds, however, introducing novel challenges particularly with respect to security that require dedicated efforts to address them. This study is focused at one such challenge, that is, determining the extent of damage caused by an intrusion for a victim virtual machine. It has significant implications especially with respect to effective response to the intrusion. This study presents the efforts to address this challenge for Clouds in the form of a novel scheme for intrusion damage assessment for Clouds. In addition to its context‐aware operation, the scheme facilitates protection against multi‐stage attacks. The study also includes the formal specification and evaluation of the scheme, which successfully demonstrate its effectiveness to achieve rigorous damage assessment for Clouds.

Cloud computing is a promising technology to facilitate development of large-scale, on-demand, flexible computing infrastructures. However, improving dependability of cloud computing is critical for realization of its potential. In this paper, we describe our efforts to quantify security for Clouds to facilitate provision of assurance for quality of service, one of the factors contributing to dependability. This has profound implications for delivering customized security solutions such as effective intrusion prevention and detection which is the overall objective of our research. In order to demonstrate the applicability of our research, we have incorporated these requirements in the resource acquisition phase for Clouds. We also present experiments to demonstrate the effectiveness of our approach to address the random migration problem for virtualized computing environments.

With the passage of time and increase in the need for mobility wireless or mobile networks emerged to replace the wired networks. This new generation of networks is different from the earlier one in many aspects like network infrastructure, resources and routing protocols, routing devices etc. These networks are bandwidth and resource constrained with no network infrastructure and dedicated routing devices. Moreover, every node in such networks has to take care of its routing module itself. These characteristics become reasons for the importance of security in mobile ad-hoc networks as there is very high probability of attacks in such networks. Some work has been done to compare different protocols on basis of security but keeping in view the resource limitations in such networks, evaluation based on networking context is also important. We evaluate the overall performance overhead associated with secure routing protocols for mobile ad-hoc networks (MANETs). We implement the secure ad-hoc on-demand distance vector routing protocol (SAODV) extensions with AODV in the network simulator 2 (NS-2) and use the Monarch project implementation of Ariadne for our evaluation purpose. We try to figure out the amount of extra work a mobile node has to do in order to operate securely