Artificial Intelligence and Machine Learning in Cybersecurity: Impact on Threat Detection and Incident Response

Israel Boafo Bansah

doi:10.56201/ijcsmt.vol.10.no6.2024.pg181.192

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Artificial Intelligence and Machine Learning in Cybersecurity: Impact on Threat Detection and Incident Response

Israel Boafo Bansah

Abstract

The increasing sophistication, volume, and velocity of cyber threats have rendered traditional rule- based and signature-driven cybersecurity mechanisms increasingly inadequate. Modern attacks, such as zero-day exploits, ransomware, advanced persistent threats, and insider attacks, operate with high adaptability and stealth, overwhelming conventional security systems and delaying effective incident response. In response, Artificial Intelligence (AI) and Machine Learning (ML) have emerged as critical enablers of intelligent, adaptive, and automated cybersecurity defenses. This study systematically reviews existing literature to examine the impact of AI and ML on cybersecurity, with a specific focus on threat detection and incident response. Adopting a Systematic Literature Review methodology, the study synthesizes peer-reviewed academic articles and high-quality industry reports published between 2018 and 2024, sourced from major databases including Scopus, IEEE Xplore, Web of Science, and ScienceDirect. The review analyzes how supervised, unsupervised, reinforcement learning, and deep learning techniques are applied across cybersecurity domains such as intrusion detection, malware classification, behavioural analytics, and automated incident response. The findings reveal that AI-driven cybersecurity systems significantly enhance detection accuracy, reduce false positives, and substantially decrease mean time to detection and response. Deep learning models demonstrate superior performance in identifying complex and previously unseen attack patterns, while reinforcement learning supports adaptive policy optimization for dynamic threat environments. However, the review also identifies persistent challenges related to data quality, model interpretability, adversarial attacks, ethical considerations, and governance constraints. This study, therefore, advances the conceptual understanding of AI as both an adaptive learning mechanism and a systemic component within securit

Keywords

Artificial IntelligenceMachine LearningCybersecurityThreat DetectionIncident Response

References

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Artificial Intelligence and Machine Learning in Cybersecurity: Impact on Threat Detection and Incident Response

Abstract

Keywords

References

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