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  • Open Access

    ARTICLE

    The Impact of SWMF Features on the Performance of Random Forest, LSTM and Neural Network Classifiers for Detecting Trojans

    Fatemeh Ahmadi Abkenari*, Melika Zandi, Shanmugapriya Gopalakrishnan

    Journal of Cyber Security, Vol.8, pp. 93-109, 2026, DOI:10.32604/jcs.2026.074197 - 20 January 2026

    Abstract Nowadays, cyberattacks are considered a significant threat not only to the reputation of organizations through the theft of customers’ data or reducing operational throughput, but also to their data ownership and the safety and security of their operations. In recent decades, machine learning techniques have been widely employed in cybersecurity research to detect various types of cyberattacks. In the domain of cybersecurity data, and especially in Trojan detection datasets, it is common for datasets to record multiple statistical measures for a single concept. We referred to them as SWMF features in this paper, which include… More >

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  • Open Access

    ARTICLE

    An Evolutionary Algorithm for Non-Destructive Reverse Engineering of Integrated Circuits

    Huan Zhang1,2, Jiliu Zhou1,2,*, Xi Wu2

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.3, pp. 1151-1175, 2021, DOI:10.32604/cmes.2021.015462 - 24 May 2021

    Abstract In hardware Trojan detection technology, destructive reverse engineering can restore an original integrated circuit with the highest accuracy. However, this method has a much higher overhead in terms of time, effort, and cost than bypass detection. This study proposes an algorithm, called mixed-feature gene expression programming, which applies non-destructive reverse engineering to the chip with bypass detection data. It aims to recover the original integrated circuit hardware, or else reveal the unknown circuit design in the chip. More >

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