Exploring Artificial Intelligence for Network Security: A Case Study of Malware Defence
Li Peng, Tuyatsetseg Badarch · American Journal of Computer Science and Technology · 2022
AI has many applications in network security. Network security is one of the most challenging situations. The paper carries out AI based network security analysis and prevention ways of the deep learning models in the network security. We focus on some specific AI applications including voice supervision of public network, malicious code monitoring, smartphone intrusion monitoring, HTTP security monitoring, mobile phone malicious APK code monitoring are bringing the solutions for network security. We studied there are powerful methods such as mobile phone malicious APR code monitoring employed Artificial Neural Network (ANN) model which detects and mitigates predictable and unpredictable DDoS attacks (TCP, UDP, and ICMP protocols). HTTP is running over TCP, then the web server can face many TCP-related attacks, therefore, we have an experiment of HTTP security monitoring, mobile phone malicious APK code monitoring. This paper presents a potential security threats from malicious uses of AI, and proposes ways to better prevent, and mitigate these threats. When planning HTTP service protection, we present it is important to keep in mind that the attack surface is much broader than just the HTTP protocol. We suggest promising areas for further research that could expand the AI based solutions for development of cloud computing-related technologies, and the combination of cloud computing and deep learning technology in the security area.