Satellite communications play an increasingly important role in a number of different industries with the rise of the Internet of Things (IoT). IoT-driven satellite (‘IoT-Satellite’ in short) networks have a number of vulnerabilities that can make them targets of common cyber attacks such as jamming and spoofing. These attacks can potentially be highly disruptive to the services they support. This paper presents a comprehensive survey of cyber threats and vulnerabilities with a focus on application areas in IoT-Satellite networks. Cyber threats include spoofing, jamming, malware and denial-of-service (DoS) attacks. Vulnerabilities in IoT-Driven satellite include deficits in encryption, access control and vulnerabilities in commercial off-the-shelf (COTS) parts. Subsequently, proposed in-depth solutions are also discussed in this paper. Proposed solutions include zero-trust security, software-defined networking, dynamic and context-based access control, blockchain and artificial intelligence (AI) approaches. While there are limited surveys specifically addressing IoT-driven satellite networks, we identify relevant studies and compare them with our work. Based on these findings we describe open research issues and potential future areas of research. The study suggests that further research is needed to develop a security framework for IoT-driven satellite networks, addressing prevalent cyber attacks and mitigating strategies.

Industrial control systems (ICS) are a collection of control systems and associated instrumentation for controlling and monitoring industrial processes. Critical infrastructure relies on supervisory control and data acquisition (SCADA), a subset of ICS specifically designed for monitoring and controlling industrial processes over large geographic areas. Cyberattacks like the Colonial Pipeline ransomware case have demonstrated how an adversary may compromise critical infrastructure. The Colonial Pipeline ransomware attack led to a week's pipeline shutdown, causing a gas shortage in the United States. As existing vulnerability assessment tools cannot be used in the context of ICS systems, vulnerability datasets specified for ICSs are needed to evaluate the security weaknesses. Our secondary metadata, ICS-LTU2022, consists of multiple features that can be used for vulnerability assessment and risk evaluation in industrial control systems. A description of the dataset, its characteristics, and data analysis are also presented in this paper. Vulnerability analysis was conducted based on the top 10 vulnerabilities in terms of severity, frequency by year, impact, components of the ICS, and common weaknesses. The ICS-LTU2022 vulnerabilities dataset is updated biannually. Our proposed dataset provides security researchers with the most recent ICS critical vulnerabilities.

Malware attacks continue to evolve, making detection challenging for traditional static and dynamic analysis techniques. On the other hand, memory analysis provides valuable behavioral insights, but prior research lacks temporal evaluations which are critical for robust detection of new malware variants over time. This paper presents MeMalDet, a novel memory analysis-based malware detection technique using deep autoencoders and stacked ensemble learning. We introduce an improved dataset with temporal attributes enabling more realistic evaluations of memory-based malware detection techniques under concept drift (temporal data split). MeMalDet extracts optimal features from memory dumps using deep autoencoders in an unsupervised manner, avoiding manual feature engineering. A stacked ensemble of supervised classifiers then performs highly accurate malware detection. Extensive experiments on our improved large-scale public dataset demonstrate MeMalDet’s ability to maintain high performance when detecting obfuscated malware under temporal splits. We achieve up to 98.82% accuracy and 98.72% F1-score in detecting previously unseen advanced obfuscated malware, significantly improving upon state-of-the-art memory analysis-based malware detection techniques. The improved dataset enables temporally robust evaluations, which is a novel contribution. MeMalDet combines the benefits of representation learning and supervised machine learning ensemble classification for effective malware detection over time using memory analysis. This research provides a new capability for identifying evasive modern malware and combating evolving real-world threats.

The aim of this systematic literature review (SLR) is to provide a comprehensive overview of the current state of Windows malware detection techniques, research issues, and future directions. The SLR was conducted by analyzing scientific literature on Windows malware detection based on executable files (.EXE file format) published between 2009 and 2022. The study presents new insights into the categorization of malware detection techniques based on datasets, features, machine learning and deep learning algorithms. It identifies ten experimental biases that could impact the performance of malware detection techniques. We provide insights on performance evaluation metrics and discuss several research issues that impede the effectiveness of existing techniques. The study also provides recommendations for future research directions and is a valuable resource for researchers and practitioners working in the field of Windows malware detection.

Overconsumption of resources is a global issue. To deal with resource depletion and mitigate impending crises, the circular economy (CE) solution provides an ecosystem by reducing waste via the reuse, repair, refurbishment, and recycling the existing materials and products. However, as the complexity of supply chains is increasing an effective CE management is very crucial. We want to address this issue by performing a feasibility study with AI-enabled blockchain technology using our developed customised NFT platform, TrackGenesis NFT, along with the OpenSea.io architecture for CE management to decrease transaction costs, enhance performance and communication along the supply chain, and reduce carbon footprints. Circulogy is an e-waste management system that can respond to supply chain challenges using blockchain technologies. A supply chain can get complicated very quickly, considering that each product component has its supply chain. In our proposed solution, blockchain provides a solution to this by establishing transparency in every node of the product's lifecycle and users can exchange or sell/buy NFTs. There are multiple copies of the audit trail for every transaction using blockchain, which will provide the ability to track and reuse/recycle Waste Electric and Electronic Equipment (WEEE).

Supervisory control and data acquisition (SCADA) serves as the backbone of several critical infrastructures, including water supply systems, oil pipelines, transportation and electricity. It accomplishes essential functions, such as monitoring data from pumps, valves and transmitters. Across different generations, SCADA has undergone a significant evolution from a typically isolated environment to a highly interconnected network. Although this conversion has benefits for SCADA, such as enhanced performance efficiency and the cost reduction of heavy equipment, it has made SCADA more vulnerable to various cyber-attacks. Several SCADA security approaches are still provided by IT-based systems that are possibly not efficient enough to deflect the risks and threats originating from SCADA field operations. As a result, it is critically important to analyse cyber risks associated with the industrial SCADA system. The goal of this survey is to explore the security vulnerabilities of SCADA systems and classify the threats accordingly. In this project, we initially reviewed SCADA systems from different scopes, including architecture, vulnerabilities, attacks, intrusion detection techniques (IDS) and testbeds. We proposed taxonomies of vulnerabilities, attacks, IDS and testbeds according to predefined criteria. We concluded the survey by highlighting the research challenges and open issues for future research in the field of SCADA security.

This paper presents API-MalDetect, a new deep learning-based automated framework for detecting malware attacks in Windows systems. The framework uses an NLP-based encoder for API calls and a hybrid automatic feature extractor based on convolutional neural networks (CNNs) and bidirectional gated recurrent units (BiGRU) to extract features from raw and long sequences of API calls. The proposed framework is designed to detect unseen malware attacks and prevent performance degradation over time or across different rates of exposure to malware by reducing temporal bias and spatial bias during the training and testing. Experimental results show that API-MalDetect outperforms existing state-of-the-art malware detection techniques in terms of accuracy, precision, recall, F1-score, and AUC-ROC on different benchmark datasets of API call sequences. These results demonstrate that the ability to automatically identify unique and highly relevant patterns from raw and long sequences of API calls is effective in distinguishing malware attacks from benign activities in Windows systems using the proposed API-MalDetect framework. API-MalDetect is also able to show cybersecurity experts which API calls were most important in malware identification. Furthermore, we make our dataset available to the research community.

Blockchain Technology is an emerging technology with the potential to disrupt a number of application domains. Though blockchain platforms like Bitcoin and Ethereum have seen immense success and acceptability, their nature of being public and anonymous makes them unsuitable for many enterprise-level use-cases. To address this issue, Linux Foundation has started an open-source umbrella initiative, known as theHyperledger Platforms. Under this initiative, a number of private blockchain platforms have been developed. However, the scalability and performance of these private blockchains must be examined to understand their suitability for different use cases. Recent researches and projects on performance benchmarking for private blockchain systems are specific to use cases and generally tied to a blockchain platform. In this article, we presentBlockMeter, an application-agnostic performance benchmarking framework for private blockchain platforms. BlockMeter can be utilised to measure the key performance metrics of any application deployed on top of an external private blockchain application in real-time. In this article, we present the architecture of the framework and discuss its different implementation aspects. Then, to showcase the applicability of the framework, we use BlockMeter to evaluate the two most widely used Hyperledger platforms, Hyperledger Fabric and Hyperledger Sawtooth, against a number of use-cases.

COVID-19 has changed almost all aspects of our lives. Governments around the world have imposed lockdowns to slow down the transmissions. Fortunately, we have found the vaccine, in fact, a good number of them. However, managing the testing and vaccination process of the total population is a mammoth job. However, there are always delays or data silo problems in multi-organizational work. Therefore, streamlining this process is vital to improve efficiency and save more lives. Because of its effective data sharing mechanism among different entities with a number of security features, blockchain can be an effective tool for different applications in the health sector. Furthermore, blockchain provides a distributed system along with greater privacy, transparency, and authenticity. In this article, we have presented a blockchain-based system that seamlessly integrates testing and vaccination systems, allowing the system to be transparent. The instant verification of any tamper-proof COVID-19 test result has been developed, which will serve as “Test Certificates”. A transparent and efficient vaccination system has also been exhibited and implemented as the “Digital Vaccine Passport” (DVP) system. The infection rate-based prioritization will ensure a transparent and fair vaccination process as well as tackle the distribution issue of the limited amount of vaccine. The comparative review with other existing works is also discussed, highlighting a clear difference from the existing works. Our proposed system is distinctive on the basis of prioritization of vaccines and seamless integration of test certificates and vaccine passports, which will aid in controlling the pandemic situation. This system will also be handy in the case of tackling any future pandemics initially.

Peer-to-Peer (P2P) energy trading platforms are being actively designed, tested, and operated by engineers, power distribution companies, and prosumers. The assurance of the accountability of the conduct of different stakeholders through a robust trust management mechanism is imperative in such platforms. The usage of blockchain, as an underlying technology, can ensure numerous properties, such as immutability, transparency, and traceable execution of transactions, in addition to ensuring trust establishment among different entities of the system. Few blockchain-based decentralized energy trading platforms have been designed in the literature to build trust about the platform and among prosumers. However, none of these proposals have considered human-in-the-loop in the trust establishment process. Moreover, these solutions have considered trust only at a particular layer of the blockchain, such as at the application or consensus layer. To bridge this gap, this article presents a novel cross-layer trust-based consensus protocol that considers human-in-the-loop and employs fuzzy logic to address the issue of vagueness of trust values by offering human interpretable trust level. The experimental results demonstrate the efficiency and effectiveness of our proposed protocol in comparison to established consensus mechanisms. The analysis also shows the protocol is immune against selfish mining, 51% and Sybil attacks.

Abstract In fault‐based Boolean expression testing, the main challenge is to generate effective test cases that can detect faults within expressions. Previous studies show that it is hard to detect literal insertion faults, more specifically Disjunctive literal insertion fault (LIF[+]) compared with other faults in Boolean expressions. Researchers have been using different strategies such as multiple near false point coverage (MNFP) and modified condition decision coverage (MCDC) to detect LIF[+] faults. However, these strategies have their own limitations. For example, MNFP can only be applied when the expression is in irredundant disjunctive normal form (IDNF), and MCDC detects a low percentage of LIF[+] faults. In this paper, we propose an abstract syntax tree (AST)‐based test case generation strategy for LIF[+] fault detection that overcomes these limitations. Furthermore, our experimental results indicate that, on average, the test suites satisfying the proposed strategy can detect approximately 97.3% of LIF[+] faults for general form expressions and 89.7% of LIF[+] faults for IDNF expressions, which are 15.6% and 13.8% improvement, respectively, compared to the MCDC test suites. Moreover, the size of the required test suite is smaller than that of MCDC test suite.

A Chatbot is a popular platform to enable users to interact with a software or website to gather information or execute actions in an automated fashion. In recent years, chatbots are being used for executing financial transactions, however, there are a number of security issues, such as secure authentication, data integrity, system availability and transparency, that must be carefully handled for their wide-scale adoption. Recently, the blockchain technology, with a number of security advantages, has emerged as one of the foundational technologies with the potential to disrupt a number of application domains, particularly in the financial sector. In this paper, we forward the idea of integrating a chatbot with blockchain technology in the view to improve the security issues in financial chatbots. More specifically, we present BONIK, a blockchain empowered chatbot for financial transactions, and discuss its architecture and design choices. Furthermore, we explore the developed Proof-of-Concept (PoC), evaluate its performance, analyse how different security and privacy issues are mitigated using BONIK.

Blockchain technology has evolved through many changes and modifications, such as smart-contracts since its inception in 2008. The popularity of a blockchain system is due to the fact that it offers a significant security advantage over other traditional systems. However, there have been many attacks in various blockchain systems, exploiting different vulnerabilities and bugs, which caused a significant financial loss. Therefore, it is essential to understand how these attacks in blockchain occur, which vulnerabilities they exploit, and what threats they expose. Another concerning issue in this domain is the recent advancement in the quantum computing field, which imposes a significant threat to the security aspects of many existing secure systems, including blockchain, as they would invalidate many widely-used cryptographic algorithms. Thus, it is important to examine how quantum computing will affect these or other new attacks in the future. In this paper, we explore different vulnerabilities in current blockchain systems and analyse the threats that various theoretical and practical attacks in the blockchain expose. We then model those attacks using Petri nets concerning current systems and future quantum computers.

Wearable devices continuously produce physiological data that can provide individuals critical information about their daily routine or fitness level in combination with their smartphones without requiring manual calculations or maintaining log-books. Real-time participant-generated data can enable large scale observational studies of health conditions, provide better insights into medical conditions of individuals and streamline clinical trial processes in medical research. However, privacy is a major concern for health data and there can be a lack of trust among different parties in the health data collection process. In addition, individuals often do not have sufficient control over the sharing of their data from the wearable devices. The lack of control, trust and privacy are key barriers to research participants being prepared to share their personal data from wearable devices. In this work, we propose a trust model to overcome the trust deficit among different parties. Then, we present a reference system architecture, rooted on the developed trust model, that provides incentive for individuals to securely share their health data through a data marketplace. By encouraging individuals to share their real-time health data, researchers will have access to large data sets at low cost.