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Blockchain overcomes numerous complicated problems related to confidentiality, integrity, availability of fast and secure distributed systems. Using data from a cross-sectoral survey of 449 industries, we investigate factors that hinder or facilitate blockchain adoption in supply chains. To capture the most vital aspects of blockchain adoption in supply chains, our conceptual model integrates the unified theory of acceptance and use of technology (UTAUT) model with the tasktechnology fit (TTF) and information system success (ISS) models, with trust-based information technology innovation adoption constructs. Using structural equation modelling, we find that the ISS, TTF, and UTAUT models positively influence the key factors affecting supply chain employees' willingness to adopt blockchain. Our results show that the UTAUT's social influence factor has no significant effect on the intention to adopt blockchain, while inter-organisational trust has a significant effect on the relationship between the UTAUT dimension and intention to adopt blockchain.Keywords Blockchain Á Cybersecurity Á Supply chain Á Trust ınformation technology adoption models Á Cross-sectoral sample peer (P2P) network for data verification and sharing. Blockchain technology uses public key encryption to verify transactions on the Internet and defend against cybersecurity threats including ransomware, trojans, worms, rootkits, and botnets [3][4][5]. It reflects a shared transaction system in which all entries are registered in public or private ledgers that are visible to users [6][7][8][9]. Blockchain-based supply chain applications include smart contracts, product traceability, enforcement tracking, stock control, transaction and settlement, and information immutability. These have led to the enhancement of market, economic, and environmental performance in the form of partnership growth. Blockchain also has marginal effects on partnership efficiency [10]. Furthermore, it supports connectivity and reliability in the electronic money market and offers a secure technology solution with a wide range of advantages and applications [11][12][13][14]. In addition, it expands the potential for sustainability by accelerating and automating the exchange of information critical to natural resources and environmental protection [10,15].According to the Market Watch report (2019), the global market size of blockchain-enabled supply chains is predictable to extent $9.8 billion by 2025. The global

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Many Internet of Things (IoT) services are currently tracked and regulated via mobile devices, making them vulnerable to privacy attacks and exploitation by various malicious applications. Current solutions are unable to keep pace with the rapid growth of malware and are limited by low detection accuracy, long discovery time, complex implementation, and high computational costs associated with the processor speed, power, and memory. Therefore, an automated intelligence technique is necessary for detecting apps containing malware and effectively predicting cyberattacks in mobile marketplaces. In this study, a system for classifying mobile marketplaces applications using real-world datasets is proposed, which analyzes the source code to identify malicious apps. A rich feature set of application programming interface (API) calls is proposed to capture the regularities in apps containing malicious content. Two feature-selection methods—Chi-Square and ANOVA—were examined in conjunction with ten supervised machine-learning algorithms. The detection accuracy of each classifier was evaluated to identify the most reliable classifier for malware detection using various feature sets. Chi-Square was found to have a higher detection accuracy as compared to ANOVA. The proposed system achieved a detection accuracy of 98.1% with a classification time of 1.22 s. Furthermore, the proposed system required a reduced number of API calls (500 instead of 9000) to be incorporated as features.

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Recent investigations have determined that many Android applications in both official and non-official online markets expose details of the user's mobile phone without user consent. In this paper, for the first time in the research literature, we provide a full investigation of why such applications leak, how they leak and where the data is leaked to. In order to achieve this, we employ a combination of static and dynamic analysis based on examination of Java classes and application behaviour for a data set of 123 samples, all predetermined as being free from malicious software. Despite the fact that anti-virus vendor software did not flag any of these samples as malware, approximately 10% of them are shown to leak data about the mobile phone to a third-party; applications from the official market appear to be just as susceptible to such leaks as applications from the non-official markets.