Abstract
In this computerized era, cyber-attacks have turned quite common. Every year, the number of cyber-attacks escalates, and so does the austerity of the harm. In today’s digital environment, ensuring security against cyber-attacks has become important. Networking is becoming more sophisticated over time, and as the popularity of a successful technology grows, intrusion detection system security issues grow as well. There is a strong necessity for a solid defense in today’s cyber world. New attacks and malware pose a great challenge to the security community. Various machine learning techniques are being used in many intrusion detection systems to counter such attacks. Machine learning can learn on its own with minimal human interaction. Hence, it is vital to call for further attention to security concerns and associated machine learning defensive strategies, which inspires this paper’s complete survey. A thorough survey on diverse machine learning algorithms has been investigated in this paper to determine which algorithm is best suited for a specific attack; these techniques have been examined and compared in terms of their accuracy in detecting attacks.
Shitharth Selvarajan
Lecturer in Cyber Security
My research interests include Cyber Security, Blockchain, Critical Infrastructure & Systems, Network Security & Ethical Hacking.