In recent years,with the rapid development of Internet and hardware technologies,the number of Internet of things(IoT)devices has grown exponentially.However,IoT devices are constrained by power consumption,making the...In recent years,with the rapid development of Internet and hardware technologies,the number of Internet of things(IoT)devices has grown exponentially.However,IoT devices are constrained by power consumption,making the security of IoT vulnerable.Malware such as Botnets and Worms poses significant security threats to users and enterprises alike.Deep learning models have demonstrated strong performance in various tasks across different domains,leading to their application in malicious software detection.Nevertheless,due to the power constraints of IoT devices,the well-performanced large models are not suitable for IoT malware detection.In this paper we propose a malware detection method based on Markov images and MobileNet,offering a cost-effective,efficient,and high-performing solution for malware detection.Additionally,this paper innovatively analyzes the robustness of opcode sequences.展开更多
基金This work was supported by the National Key R&D Program of China under Grant 2020YFB1807503 and NSFC Fund under Grant U20A20156.
文摘In recent years,with the rapid development of Internet and hardware technologies,the number of Internet of things(IoT)devices has grown exponentially.However,IoT devices are constrained by power consumption,making the security of IoT vulnerable.Malware such as Botnets and Worms poses significant security threats to users and enterprises alike.Deep learning models have demonstrated strong performance in various tasks across different domains,leading to their application in malicious software detection.Nevertheless,due to the power constraints of IoT devices,the well-performanced large models are not suitable for IoT malware detection.In this paper we propose a malware detection method based on Markov images and MobileNet,offering a cost-effective,efficient,and high-performing solution for malware detection.Additionally,this paper innovatively analyzes the robustness of opcode sequences.
