ML-Based Method for Detecting and Alerting to Cyber Attacks
D.V. Chandrashekar, K. Suneetha · 2023
Criminals are taking advantage of any and all loop holes in the system that they can uncover and are conducting massive amounts of cybercrime as a result. Ethical hackers are more concerned with locating vulnerabilities in a system and providing ideas on how these vulnerabilities might be fixed. Within the realm of cybersecurity, the development of efficient methods has become an increasingly urgent necessity. The majority of intrusion detection systems (IDS) are unable to manage cyberattacks on computer networks because these threats are constantly evolving and can be difficult to anticipate. They are useless at this point. Because it is so effective at resolving issues related to cybersecurity, machine learning has recently emerged as a topic of intense interest in the sector. Methods of applied machine learning have been utilized to identify solutions to significant issues pertaining to cybersecurity. These issues include the detection of intrusions, the categorization and discovery of malware, the detection of spam, and the detection of phishing. Despite the fact that machine learning cannot yet be utilized to its extent in better detection of malwares when a cyber defense system is automated, it is possible to discover threats more quickly than when using alternative software-based methods. This makes the task of security analysts much simpler. For example, machine learning can be used to enhance detection rates, minimize the number of false alarms, and cut down on the amount of money spent on calculation and transmission. The group of people who work on intrusion detection ought to have a difficult time utilizing machine learning. This is due to the fact that the application of machine learning to the detection of assaults requires a fundamentally different approach than its application in other fields.