Evolving malice scoring models for ransomware detection: An automated approach by utilising genetic programming and cooperative coevolution

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@Article{JOHN:2023:cose,

• author = "Taran Cyriac John and Muhammad Shabbir Abbasi and Harith Al-Sahaf and Ian Welch and Julian Jang-Jaccard",
• title = "Evolving malice scoring models for ransomware detection: An automated approach by utilising genetic programming and cooperative coevolution",
• journal = "Computer \& Security",
• volume = "129",
• pages = "103215",
• year = "2023",
• ISSN = "0167-4048",
• DOI = "doi:10.1016/j.cose.2023.103215",
• URL = "https://www.sciencedirect.com/science/article/pii/S0167404823001256",
• keywords = "genetic algorithms, genetic programming, Ransomware detection, Evolutionary computation, Symbolic regression, Malice score, Cooperative coevolution",
• abstract = "Malice scoring is a technique that is present throughout the literature to quantify a software malignance through the assignment of a malice score. However, the majority of existing malice scoring models are synthesised using manually selected features and weights, where a domain specialist is needed. Genetic Programming and cooperative coevolution are used to automatically evolve an ensemble of symbolic regression functions to assign a malice score to an instance of software data. Using a publicly available dataset, the effectiveness of the proposed method is assessed and compared to that of the state-of-the-art malice scoring method. The experimental results show that the proposed method has significantly outperformed the benchmark method and exhibits the best-performing model that produces an overall balanced accuracy of 95.80percent, correctly classifying 94.21percent and 97.39percent of unseen malicious and benign instances, respectively. Furthermore, various aspects of the proposed method and experimental results have been analysed in-depth to provide insight into the evolutionary process and some of the automatically evolved models",

}

Genetic Programming entries for Taran John Muhammad Shabbir Abbasi Harith Al-Sahaf Ian Welch Julian Jang-Jaccard

Citations