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An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

Computer Modeling in Engineering & Sciences 2024年第6期139卷 3535-3563页

作者： Younghoon Ban Myeonghyun Kim Haehyun Cho School of Software Soongsil UniversitySeoul06978Korea

Antivirus vendors and the research community employ Machine Learning(ML)or Deep Learning(DL)-based static analysis techniques for efficient identification of new threats,given the continual emergence of novel malware *** the other hand,numerous researchers have reported that Adversarial Examples(AEs),generated by manipulating previously detected malware,can successfully evade ML/DL-based *** antivirus systems,in particular,have been identified as vulnerable to such *** paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware *** attack method utilizes seven different perturbations,including Overlay Append,Section Append,and Break Checksum,capitalizing on the ambiguities present in the PE format,as previously employed in evasion attack *** directly applying the perturbation techniques to PE binaries,our attack method eliminates the need to grapple with the problem-feature space dilemma,a persistent challenge in many evasion attack *** a black-box attack,our method can generate AEs that successfully evade both DL-based and ML-based ***,AEs generated by the attack method retain their executability and malicious behavior,eliminating the need for functionality *** thorogh evaluations,we confirmed that the attack method achieves an evasion rate of 65.6%against well-known ML-based malware detectors and can reach a remarkable 99%evasion rate against well-known DL-based malware ***,our AEs demonstrated the capability to bypass detection by 17%of vendors out of the 64 on VirusTotal(VT).In addition,we propose a defensive approach that utilizes Trend Locality Sensitive Hashing(TLSH)to construct a similarity-based defense *** several experiments on the approach,we verified that our defense model can effectively counter AEs generated by the perturbation *** conclusion,our defense model alleviates the limitation of the mos

关键词： Malware classification machine learning adversarial examples evasion attack cybersecurity

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Kernel-based adversarial attacks and defenses on support vector classification

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Digital Communications and Networks 2022年第4期8卷 492-497页

作者： Wanman Li Xiaozhang Liu Anli Yan Jie Yang School of Computer Science and Technology Hainan UniversityHaikou570228China

While malicious samples are widely found in many application fields of machine learning,suitable countermeasures have been investigated in the field of adversarial machine *** to the importance and popularity of Support Vector Machines(SVMs),we first describe the evasion attack against SVM classification and then propose a defense strategy in this *** evasion attack utilizes the classification surface of SVM to iteratively find the minimal perturbations that mislead the nonlinear ***,we propose what is called a vulnerability function to measure the vulnerability of the SVM *** this vulnerability function,we put forward an effective defense strategy based on the kernel optimization of SVMs with Gaussian kernel against the evasion *** defense method is verified to be very effective on the benchmark datasets,and the SVM classifier becomes more robust after using our kernel optimization scheme.

关键词： Adversarial machine learning Support vector machines evasion attack Vulnerability function Kernel optimization

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