Authors - Pranav Rao, Pranav S Acharya, Rishika Nayana Naarayan, Shreya M Hegde, Pavan A C Abstract - The rapid expansion of cloud computing, Internet of Things (IoT), 5G networks, and distributed enterprise infrastructures has significantly in creased the complexity and attack surface of modern networks. Traditional net work security mechanisms—primarily based on static rules and signature-based detection—are increasingly ineffective against advanced persistent threats (APTs), zero-day exploits, polymorphic malware, and encrypted attack chan nels. Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative technologies capable of enabling adaptive, predictive, and au tonomous cybersecurity systems. This paper presents a comprehensive technical framework for AI-driven network security. We propose a hybrid architecture in tegrating supervised classification, unsupervised anomaly detection, and deep learning-based behavioral modeling. Mathematical formulations for intrusion detection, anomaly detection, and adversarial robustness are provided. The framework is evaluated using benchmark intrusion detection datasets, and per formance is analyzed using standard metrics including accuracy, precision, re call, F1-score, and ROC-AUC. Results demonstrate that AI-driven models sig nificantly outperform traditional signature-based approaches in detecting zero day and evasive attacks. The paper concludes by discussing adversarial machine learning risks and future directions toward autonomous and self-healing net work security ecosystems.
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