Volume 15, Issue 10 · IJARST · ISSN 2319-1783 / 2320-1126

ARTIFICIAL INTELLIGENCE IN CYBERSECURITY

ARTIFICIAL INTELLIGENCE IN CYBERSECURITY is an open-access, peer-reviewed research paper by Mr. Gunda Rajesh, Mr. Kunal Vilas Bhide, Mr. Kaustubh Arun Deshpande, Mr. Anu Wilfred, Mr. Harshad Mengle, published in Volume 15, Issue 10 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

Mr. Gunda Rajesh, Mr. Kunal Vilas Bhide, Mr. Kaustubh Arun Deshpande, Mr. Anu Wilfred, Mr. Harshad Mengle

Abstract

As the world's infrastructure has quickly moved towards a Technological Revolution, so have the volume, velocity and sophistication of cyber threats. Though these are basics, traditional signature and rule based security solutions continue to be overwhelmed by polymorphism, zero day exploits and adapting adversarial tactics. The rapid evolution of technology has seen Artificial Intelligence (AI), including machine learning (ML), deep learning (DL), and natural language processing (NLP), become a game-changer in cybersecurity, creating the ability to learn from data, detect threats in real time, and act automatically to counter new attacks. This research paper reviews the literature on Artificial Intelligence in cybersecurity in a structured manner and analyzes literature obtained from the survey as well as literature obtained from secondary sources, and discusses the application of AI in cybersecurity, such as identification of intrusions, classification of malware, identification of phishing, prevention of fraud, and security orchestration. The research methodology used is a mixed research approach, which consists of a systematic literature review where the adoption trend and detection performance measure of the studies are quantitatively analyzed. It's found that the AI detection systems far surpass traditional detection systems in terms of both accuracy and reducing false-positives — but there are challenges associated with AI detection systems, including adversarial AI attacks, data privacy issues, explainability limitations and resource intensive model training. Overall, the paper concludes that, properly harnessed, AI can be a force multiplier to human expertise and significantly enhance organizational cyber resilience. Some directions for future research are also suggested — explainable AI (XAI), federated learning in the interest of a believable threat intelligence — and adversarial defense by AI versus AI itself.

Keywords: Artificial Intelligence, Cybersecurity, Machine Learning, Deep Learning, Intrusion Detection, Threat Intelligence, Malware Detection, Adversarial AI, Explainable AI

DOI: https://doi.org/10.62226/ijarst20262837

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DOI

10.62226/ijarst20262837

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How do you cite this paper?

Mr. Gunda Rajesh, Mr. Kunal Vilas Bhide, Mr. Kaustubh Arun Deshpande, Mr. Anu Wilfred, Mr. Harshad Mengle — “ARTIFICIAL INTELLIGENCE IN CYBERSECURITY.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 15, Issue 10. DOI: 10.62226/ijarst20262837.

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