A CONVOLUTIONAL NEURAL NETWORK-BASED MALWARE ANALYSIS, INTRUSION DETECTION, AND PREVENTION SCHEMA

Abstract

This paper discusses distributed denial of service (DDoS) attacks, their current threat level, and intrusion detection systems (IDS), which are one of the primary tools for mitigating them. It focuses on the difficulties and challenges that IDS systems face when detecting DDoS attacks, as well as the difficulties and challenges that they face today when integrating with artificial intelligence systems. Automatic and real-time detection of malicious threats is made possible by these ID systems. However, the network requires a highly sophisticated security solution due to the frequency with which malicious threats emerge and change. A significant amount of research is required to create an intelligent and trustworthy identification system for research purposes; numerous ID datasets are freely accessible. Due to the rapid evolution of attack detection mechanisms and the complexity of malicious attacks, publicly available ID datasets must be thoroughly modified on a regular basis. Due to the ever-evolving attack detection mechanism and the complexity of malicious attacks, publicly available ID datasets must frequently be modified. A Convolutional Neural Network (CNN) network was trained using four distinct training algorithms. The CICDDoS2019 dataset, which contains the most recent DDoS attack types created in CICDDoS2019, was tested, According to the analysis; the "Gradient Descent with Momentum Backpropagation" algorithm could be trained quickly. Network data attacks were correctly detected 93.1 percent of the time. The results indicate that The Convolutional Neural Network is able to successfully defend against DDoS attacks detection by using intrusion detection systems IDS, as evidenced by the high accuracy values obtained.