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Research Paper | Computer Science and Engineering | Volume 15 Issue 7, July 2026 | Pages: 274 - 276 | India
AI-Driven Cyber Security: Intelligent Threat Detection and Prevention Using Machine Learning
Abstract: The rapid adoption of digital technologies such as cloud computing and Internet of Things (IoT) devices has significantly increased the complexity of cybersecurity challenges. Traditional security approaches are often unable to effectively defend against modern and evolving cyber threats. In this context, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as powerful tools that enable intelligent, adaptive and automated threat management. This paper investigates the role of AI-based techniques in cybersecurity with particular emphasis on Intrusion Detection Systems (IDS), deep learning approaches and security frameworks for IoT environments. It further explores important aspects such as adversarial machine learning and explainable AI (XAI) which contribute to improving the reliability and transparency of security systems. The findings indicate that AI-driven solutions enhance the accuracy of threat detection, enable faster real-time analysis and support proactive defense mechanisms. Overall the integration of AI and ML into cybersecurity systems improves resilience and offers scalable solutions to effectively address continuously evolving cyber risks.
Keywords: Artificial Intelligence, Machine Learning, Cybersecurity, Intrusion Detection System, Deep Learning, Explainable AI, Adversarial Machine Learning, IoT Security
How to Cite?: Pradeep Kumar, "AI-Driven Cyber Security: Intelligent Threat Detection and Prevention Using Machine Learning", Volume 15 Issue 7, July 2026, International Journal of Science and Research (IJSR), Pages: 274-276, https://www.ijsr.net/getabstract.php?paperid=SR26630120458, DOI: https://dx.doi.org/10.21275/SR26630120458