A survey of Machine Learning and Deep Learning Approaches in the field of Information Security
Sanchit Agarwal, Pawan Singh Mehra · 2024
Among many issues that the world faces, cybersecurity holds the position of the most fundamental one due to the formation of conniving methods for the breaches of data and security by smart people literally every day. In this paper on Machine Learning (ML) and Deep Learning (DL) application in the computer security domain, we will be focusing on the technologies we already have and those that can be modified. We zoom in on how these technologies are employed for the recognition and annihilation of various cyber perils like malware, phishing, and Distributed Denial of Service (DDoS) attacks. Our outcomes prove that ML and DL not only discover the threats but also heighten the flexibility of safety systems that earlier were primarily based on static and rule-based operations. This way, ideas cut the issue of danger by foreseeing and implementing correct actions that lead to security level improvement: Yet, there will be no time when the challenges will be finished completely such as the hassle of perfecting the models and the continuous emergence of the never-seen cyber risks which can only be resolved by continuous improvement of our strategies. Apart from defining the problem, the paper suggests several approaches for the future study, such as ensuring data quality, decision transparency and flexibility to quickly adapt to new threat types.