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Multi-Binary Classifiers Using Optimal Feature Selection for Memory-Saving Intrusion detection Systems

Computer Modeling in Engineering & Sciences 2024年第11期141卷 1473-1493页

作者： Ye-Seul Kil Yu-Ran Jeon Sun-Jin Lee Il-Gu Lee Department of Future Convergence Technology Engineering Sungshin Women’s UniversitySeoul02844Republic of Korea Department of Convergence Security Engineering Sungshin Women’s UniversitySeoul02844Republic of Korea

With the rise of remote work and the digital industry,advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics,rendering them difficult to detect with conventional intrusion detection ***-based intrusion detection methods can be used to detect attacks;however,they cannot detect new *** detection and response(EDR)tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection ***,EDR tools are restricted by the continuous generation of unnecessary logs,resulting in poor detection performance and memory *** learning-based intrusion detection techniques for responding to advanced cyberattacks are memory intensive,using numerous features;they lack optimal feature selection for each attack *** overcome these limitations,this study proposes a memory-efficient intrusion detection approach incorporating multi-binary classifiers using optimal feature *** proposed model detects multiple types of malicious attacks using parallel binary classifiers with optimal features for each attack *** experimental results showed a 2.95%accuracy improvement and an 88.05%memory reduction using only six features compared to a model with 18 ***,compared to a conventional multi-classification model with simple feature selection based on permutation importance,the accuracy improved by 11.67%and the memory usage decreased by 44.87%.The proposed scheme demonstrates that effective intrusion detection is achievable with minimal features,making it suitable for memory-limited mobile and Internet of Things devices.

关键词： endpoint detection and response feature selection machine learning malware detection

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