ISSN : 2488-8648
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×published date:2026-Jun-08
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Abstract
The growing sophistication of cyberattacks within modern digital ecosystems has increasingly exposed the limitations of traditional rule-based security mechanisms. Artificial Intelligence (AI) and Machine Learning (ML), particularly Deep Learning (DL) and Graph Neural Networks (GNNs), have emerged as transformative approaches for threat detection, incident response, and cybersecurity automation. This paper examines AI-driven techniques for real-time anomaly detection in network traffic, automated identification and patching of software vulnerabilities, and intelligent incident-response systems capable of prioritising alerts, predicting attack propagation, and supporting autonomous mitigation strategies. Drawing on benchmark datasets such as CICIDS2017 and KDD99, the study demonstrates that deep learning and graph-based architectures can outperform conventional statistical models in terms of accuracy, adaptability, and scalability. The findings highlight the potential of hybrid AI frameworks that integrate explainable deep learning, automated code analysis, and reinforcement learning-based response mechanisms. The paper concludes by identifying future research directions toward interpretable, energy-efficient, and ethically aligned AI systems for building resilient cybersecurity infrastructures.
Keywords: Cybersecurity Automation, Artificial Intelligence (AI) ,Deep Learning (DL) ,,
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