A COMPREHENSIVE REVIEW OF ARTIFICIAL INTELLIGENCE APPLICATIONS IN ENHANCING CYBERSECURITY THREAT DETECTION AND RESPONSE MECHANISMS

Abstract

This literature review explores the transformative impact of artificial intelligence (AI) on enhancing cybersecurity measures across various domains. The study systematically examines the integration of AI in Intrusion Detection Systems (IDS), malware detection, phishing detection, threat intelligence, network security, and endpoint protection. Key findings reveal that AI-driven techniques significantly outperform traditional methods, particularly in real-time threat detection, accuracy, and adaptive response capabilities. Network-based IDS benefit from supervised and unsupervised learning algorithms, improving the identification of malicious network traffic and novel attack patterns. In malware detection, AI-enhanced static and dynamic analysis methods surpass signature-based approaches by detecting previously unknown malware and complex behaviors. Phishing detection has seen substantial improvements with AI applications in email filtering and URL analysis, reducing phishing incidents despite challenges like false positives. AI's role in threat intelligence is critical, automating data analysis to uncover hidden threats and employing predictive analytics to anticipate and mitigate cyber attacks. AI techniques in network security and endpoint protection enhance real-time monitoring and authentication processes, providing robust defenses against cyber intrusions. Despite these advancements, challenges such as handling high data volumes and the need for continuous learning to adapt to emerging threats remain. This review underscores the significant advancements, practical implementations, and ongoing challenges of leveraging AI in cybersecurity, highlighting its potential to fortify digital defenses and address the complexities of contemporary cyber threats.