A Reinforcement Learning-Based ELF Adversarial Malicious Sample Generation Method

In recent years, domestic Linux operating systems have developed rapidly, but the threat of ELF viruses has become increasingly prominent. Currently, domestic antivirus software for information technology application innovation (ITAI) operating systems shows insufficient capability in detecting ELF viruses. At the same time, research on generating malicious samples in ELF format is scarce. In order to fill this gap at home and abroad and meet the growing application needs of domestic antivirus software companies, this paper proposes an automatic ELF adversarial malicious samples generation technique based on reinforcement learning. Based on reinforcement learning framework, after being processed by cycles of feature extraction, malicious detection, agent decision-making, and evade-detection operation, the sample can evade the detection of antivirus engines. Specifically, nine feature extractor subclasses are used to extract features in multiple aspects. The PPO algorithm is used as the agent algorithm. The action table in the evade-detection module contains 11 evade-detection operations for ELF malicious samples. This method is experimentally verified on the ITAI operating system, and the ELF malicious sample set on the Linux x86 platform is used as the original sample set. The detection rate of this sample set by ClamAV before processing is 98%, and the detection rate drops to 25% after processing. The detection rate of this sample set by 360 Security before processing is 4%, and the detection rate drops to 1% after processing. Furthermore, after processing, the average number of engines on VirusTotal that could detect the maliciousness of the samples decreases from 39 to 15. Many malicious samples were detected by $41\sim 43$ engines on VirusTotal before processing, while after the evade-detection processing, only $8\sim 9$ engines on VirusTotal can detect the malware. In terms of executability and malicious function consistency, the processed samples can still run normally and the malicious functions remain consistent with those before processing. Overall, the proposed method in this paper can effectively generate adversarial ELF malware samples. Using this method to generate malicious samples to test and train the anti-virus software can promote and improve anti-virus software’s detection and defense capability against malware.