Artificial Intelligence and Machine Learning-Enabled Cybersecurity Tools and Techniques
Diksha Dhiman, Amita Bisht, Gesu Thakur, Ankit Garg · 2024
Due to growing dangers and cybercriminals’ ongoing efforts to elude law enforcement, the field of cybersecurity has experienced considerable growth over the past ten years. Although cybercriminals’ tactics have advanced over time, their initial motivations for conducting hacks have generally remained the same. Cyberattacks are becoming more sophisticated and more difficult to detect and mitigate using traditional cybersecurity solutions. In order to stay ahead of adversaries, cybersecurity experts are believed to continuously upgrade their cryptographic and artificial intelligence (AI) methods (specifically machine learning (ML)) in order to keep up with the ever-evolving threats posed by adversaries as a result of technological advancements and to gain an advantage over adversaries. Protecting your computer, mobile device, and data against malicious attacks is what digital protection is all about. The use of AI and ML, which are fundamental to our daily lives, is necessary for network protection. Rules, attack signatures, and fixed algorithms comprise traditional cybersecurity systems. Traditional cybersecurity systems rely on human intervention to properly function because they can act only based on the information they are fed. ML can be used to anticipate or identify upcoming threats based on past experiences using both seen and unseen data. The ability of ML to manage massive amounts of data, which enables it to overcome many of the issues found in conventional cybersecurity systems, is a significant advantage. In this chapter, we look at AI’s advantages and disadvantages in terms of enhancing cybersecurity solutions. We also explore prospective research prospects related to the advancement of AI methods in the realm of cybersecurity across a variety of application domains as part of this study.