Improvement of Malicious Software Detection Accuracy through Genetic Programming Symbolic Classifier with Application of Dataset Oversampling Techniques

Author:

Anđelić Nikola1ORCID,Baressi Šegota Sandi1ORCID,Car Zlatan1ORCID

Affiliation:

1. Faculty of Engineering, University of Rijeka, Vukovarska 58, 51000 Rijeka, Croatia

Abstract

Malware detection using hybrid features, combining binary and hexadecimal analysis with DLL calls, is crucial for leveraging the strengths of both static and dynamic analysis methods. Artificial intelligence (AI) enhances this process by enabling automated pattern recognition, anomaly detection, and continuous learning, allowing security systems to adapt to evolving threats and identify complex, polymorphic malware that may exhibit varied behaviors. This synergy of hybrid features with AI empowers malware detection systems to efficiently and proactively identify and respond to sophisticated cyber threats in real time. In this paper, the genetic programming symbolic classifier (GPSC) algorithm was applied to the publicly available dataset to obtain symbolic expressions (SEs) that could detect the malware software with high classification performance. The initial problem with the dataset was a high imbalance between class samples, so various oversampling techniques were utilized to obtain balanced dataset variations on which GPSC was applied. To find the optimal combination of GPSC hyperparameter values, the random hyperparameter value search method (RHVS) was developed and applied to obtain SEs with high classification accuracy. The GPSC was trained with five-fold cross-validation (5FCV) to obtain a robust set of SEs on each dataset variation. To choose the best SEs, several evaluation metrics were used, i.e., the length and depth of SEs, accuracy score (ACC), area under receiver operating characteristic curve (AUC), precision, recall, f1-score, and confusion matrix. The best-obtained SEs are applied on the original imbalanced dataset to see if the classification performance is the same as it was on balanced dataset variations. The results of the investigation showed that the proposed method generated SEs with high classification accuracy (0.9962) in malware software detection.

Funder

CEEPUS network

European Regional Development

Erasmus+ project WICT

University of Rijeka Scientific

Publisher

MDPI AG

Subject

Computer Networks and Communications,Human-Computer Interaction

Reference26 articles.

1. A deeper look into cybersecurity issues in the wake of COVID-19: A survey;Alawida;J. King Saud Univ.-Comput. Inf. Sci.,2022

2. A comprehensive review of cyber security vulnerabilities, threats, attacks, and solutions;Aslan;Electronics,2023

3. Broadhurst, R. (2017). The Oxford Handbook of Cyber Security, Oxford Handbooks Press.

4. Li, B., Zhao, Q., Jiao, S., and Liu, X. (2023, January 2–6). DroidPerf: Profiling Memory Objects on Android Devices. Proceedings of the 29th Annual International Conference on Mobile Computing and Networking, Madrid, Spain.

5. Techniques in detection and analyzing malware executables: A review;Jain;Int. J. Comput. Sci. Mob. Comput.,2014

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3