A Deep Neural Network Strategy to Distinguish and Avoid Cyber-Attacks

Abstract

In this paper, we will discuss the incorporation of machine learning techniques with traditional intrusion detection systems so that we can deal with different types of cyber-attacks using a single IDS. By applying machine learning to the IDS, we not only increase the system’s sensitivity to malicious packets, but we can also remain secure from highly complex or previously unknown attacks. Initially, this paper introduces the current existing intrusion detection systems and their drawbacks. Secondly, this paper discusses a new system, secured attack-avoidance technique (SAAT), with the incorporation of machine learning and its architecture. Over the course of the paper, it was found that SAAT produced results of high accuracy and less false feedbacks.