A Comparative Study of Artificial Intelligence and Machine Learning Algorithms for Cybersecurity - Arcot Ramesh, Sai Kiran (International Journal of Intelligent Systems and Applications in Engineering, 2024)
The rapid expansion of cyberspace has been facilitated by a range of innovative networking and computing technologies, including software-defined networking, big data, and fog computing. Currently, cyber security has emerged as a paramount concern in the realm of cyberspace. The security of cyberspace has had significant effects on multiple essential infrastructures. The passive protection approach is no longer effective in safeguarding systems against emerging cyber risks, such as advanced persistent threats and zero-day assaults. So, the main objective of this study is to conduct a thorough examination of different implementations of artificial intelligence in the field of cybersecurity, encompassing activities such as identifying potential risks, responding to security incidents, and utilizing predictive analytics. The methodology employed in this study is qualitative research technique. The study emphasizes the efficacy of AI-powered solutions in strengthening the robustness of contemporary cybersecurity frameworks, based on current case studies and breakthroughs in machine learning algorithms. The paper critically examines the constraints and possible prejudices in AI systems used for cybersecurity, highlighting the significance of responsible AI methodologies. The study will be a contribution to the researchers, practitioners, and policymakers to know about the present condition of artificial intelligence (AI) in cybersecurity. It aims to encourage discussions on the efficient incorporation of AI technologies to tackle the continuously expanding challenges in the field of cyber threats.