Detection of Cyber Attacks and Network Attacks using Machine Learning Algorithms

Abstract

Cyber-crime is spreading throughout the world, exploiting any type of vulnerability in the cloud computing platform. Ethical hackers are primarily concerned in identifying flaws and recommending mitigation measures. In the cyber security world, there is a pressing need for the development of effective techniques. The majority of IDS techniques used today are incapable of dealing with the dynamic and complex nature of cyber-attacks on computer networks. Because of the effectiveness of machine learning in cyber security issues, machine learning for cyber security has recently become a hot topic. In cyber security, machine learning approaches have been utilised to handle important concerns such as intrusion detection, malware classification and detection, spam detection, and phishing detection. Although ML cannot fully automate a cyber-security system, it can identify cyber security threats more efficiently than other software-oriented approaches, relieving security analysts of their burden. As a result, effective adaptive methods, such as machine learning techniques, can yield higher detection rates, lower false alarm rates, and cheaper computing and transmission costs. Our key goal is that the challenge of detecting attacks is fundamentally different from those of these other applications, making it substantially more difficult for the intrusion detection community to apply machine learning effectively. In this study, the CPS is modelled as a network of agents that move in unison with one another, with one agent acting as a leader and commanding the other agents. The proposed strategy in this study is to employ the structure of deep neural networks for the detection phase, which should tell the system of the attack's existence in the early stages of the attack. The use of robust control algorithms in the network to isolate the misbehaving agent in the leader-follower mechanism has been researched. Following the attack detection phase with a deep neural network, the control system uses the reputation algorithm to isolate the misbehaving agent in the presented control method. Experiment results show that deep learning algorithms can detect attacks more effectively than traditional methods, making cyber security simpler, more proactive, and less expensive and more expensive.