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Enhancing PDF Malware Detection through logistic model trees

Computers, Materials & Continua 2024年第3期78卷 3645-3663页

作者： Muhammad Binsawad Department of Information Systems King Abdulaziz UniversityP.O.Box 80217Jeddah21589Saudi Arabia

Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity,and because of its complexity and evasiveness,it is challenging to identify using traditional signature-based detection *** study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses,highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial *** article presents a new method that improves PDF virus detection by using document analysis and a logistic model *** a dataset from the Canadian Institute for Cybersecurity,a comparative analysis is carried out with well-known machine learning models,such as Credal Decision tree,Naïve Bayes,Average One Dependency Estimator,Locally Weighted Learning,and Stochastic Gradient *** traditional structural and JavaScript-centric PDF analysis,the research makes a substantial contribution to the area by boosting precision and resilience in malware *** use of logistic model tree,a thorough feature selection approach,and increased focus on PDF file attributes all contribute to the efficiency of PDF virus *** paper emphasizes logistic model tree’s critical role in tackling increasing cybersecurity threats and proposes a viable answer to practical issues in the *** results reveal that the logistic model tree is superior,with improved accuracy of 97.46%when compared to benchmark models,demonstrating its usefulness in addressing the ever-changing threat landscape.

关键词： Malware detection PDF files logistic model tree feature selection cybersecurity

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