Purpose The purpose of this article is to clarify current and widespread misconceptions about the properties of blockchain technologies and to describe challenges and avenues for correct and trustworthy design and implementation of distributed ledger system (DLS) or Technology (DLT). Design/methodology/approach The authors contrast the properties of a blockchain with desired, however emergent, properties of a DLS, which is a complex and distributed system. They point out and justify, with facts and analysis, current misconceptions about the blockchain and DLSs. They describe challenges that these systems will need to address and possible solution avenues for achieving trustworthiness. Findings Many of the statements that have appeared on the internet, news and academic articles, such as immutable ledger and exact copies, may be misleading. These are desired emergent properties of a complex system, not assured properties. It is well-known within the distributed systems and critical software community that it is extremely hard to prove that a complex system correctly and completely implements emergent properties. Further research and development for trustworthy DLS design and implementation is needed, both practical and theoretical. Research limitations/implications This is the first known published attempt at describing current misconceptions about blockchain technologies. Further collaborative work, discussions, potential solutions, evaluations, resulting publications and verified reference implementations are needed to ensure DLTs are safe, secure, and trustworthy. Practical implications Interdisciplinary teams with members from academia, business and industry, and from disciplines such as business, entrepreneurship, theoretical and practical computer science, cybersecurity, finance, mathematics and statistics, must be formed. Such teams must collaborate with the objective of developing strategies and techniques for ensuring the correctness and security of future DLSs in which our society may become dependent. Originality value The value and originality of this article is twofold: the disproving, through fact collection and systematic analysis, of current misconceptions about the properties of the blockchain and DLSs, and the discussion of challenges to achieving adequate trustworthiness along with the proposal of general avenues for possible solutions.

Recently, ransomware attacks have been among the major threats that target a wide range of Internet and mobile users throughout the world, especially critical cyber physical systems. Due to its unique characteristics, ransomware has attracted the attention of security professionals and researchers toward achieving safer and higher assurance systems that can effectively detect and prevent such attacks. The state-of-the-art crypto ransomware early detection models rely on specific data acquired during the runtime of an attack’s lifecycle. However, the evasive mechanisms that these attacks employ to avoid detection often nullify the solutions that are currently in place. More effort is needed to keep up with an attacks’ momentum to take the current security defenses to the next level. This survey is devoted to exploring and analyzing the state-of-the-art in ransomware attack detection toward facilitating the research community that endeavors to disrupt this very critical and escalating ransomware problem. The focus is on crypto ransomware as the most prevalent, destructive, and challenging variation. The approaches and open issues pertaining to ransomware detection modeling are reviewed to establish recommendations for future research directions and scope.

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In earlier works we presented a computational infrastructure that allows an analyst to estimate the security of a system in terms of the loss that each stakeholder stands to sustain as a result of security breakdowns. In this paper we illustrate this infrastructure by means of an e-commerce application.

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The Internet of Things (IoT) ecosystem has experienced significant growth in data traffic and consequently high dimensionality. Intrusion Detection Systems (IDSs) are essential self-protective tools against various cyber-attacks. However, IoT IDS systems face significant challenges due to functional and physical diversity. These IoT characteristics make exploiting all features and attributes for IDS self-protection difficult and unrealistic. This paper proposes and implements a novel feature selection and extraction approach (i.e., our method) for anomaly-based IDS. The approach begins with using two entropy-based approaches (i.e., information gain (IG) and gain ratio (GR)) to select and extract relevant features in various ratios. Then, mathematical set theory (union and intersection) is used to extract the best features. The model framework is trained and tested on the IoT intrusion dataset 2020 (IoTID20) and NSL-KDD dataset using four machine learning algorithms: Bagging, Multilayer Perception, J48, and IBk. Our approach has resulted in 11 and 28 relevant features (out of 86) using the intersection and union, respectively, on IoTID20 and resulted 15 and 25 relevant features (out of 41) using the intersection and union, respectively, on NSL-KDD. We have further compared our approach with other state-of-the-art studies. The comparison reveals that our model is superior and competent, scoring a very high 99.98% classification accuracy.

Since the proposal for the six object-oriented metrics by Chidamber and Kemerer (1994), several studies have been conducted to validate their metrics and have discovered some deficiencies. Consequently, many new metrics for object-oriented systems have been proposed. Among the various measurements of objectoriented characteristics, we focus on the metrics of class inheritance hierarchies in design and maintenance. As such, we propose two simple and heuristic metrics for the class inheritance hierarchy for the maintenance of object-oriented software.In this paper we investigate the work of Chidamber and Kemerer (1994) and Li (1998), and extend their work to apply specifically to the maintenance of a class inheritance hierarchy. In doing so, we suggest new metrics for understandability and modifiability of a class inheritance hierarchy. The main contribution here includes the various comparisons that we have made. We discuss the advantages over Chidamber and Kemerer's (1994) metrics and Henderson-Sellers's (1996) metrics in the context of maintaining class inheritance hierarchies. Figure 6(a): (1 + 1 + 2 + 2 + 4)/5 = 2 AM of Figure 6(a): 2 + (2/2 + 2/2 + 1/2)/5 = 2.5 AU of Figure 6(b): (1 + 2 + 2 + 2)/4 = 1.75 AM of Figure 6(b): 1.75 + (3/2)/4 = 2.13 The understandability and modifiability of Figure 6(b) is better than Figure 6(a) in our metrics. AU of

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