Artificial Intelligence-Driven Cybersecurity Framework for Modern Networks

Abstract

With the rapid expansion of interconnected devices and the proliferation of smart applications, the challenge of securing modern networks has become more complex than ever. Traditional rule-based cybersecurity solutions struggle to keep pace with the increasingly sophisticated attacks. This research proposes an Artificial Intelligence-driven Cybersecurity Framework that leverages machine learning models for real-time threat detection, anomaly identification, and automated response. The framework integrates supervised learning for known threats and unsupervised learning algorithms to detect novel attacks by analyzing network traffic patterns. We conducted extensive experiments using a publicly available intrusion detection dataset, applying algorithms such as Random Forest, Support Vector Machines, and Autoencoders. Our results show that the proposed framework achieves over 98% accuracy in detecting both known and unknown threats, while reducing false positives compared to conventional signature-based systems. Furthermore, the framework incorporates a decision support system to guide security administrators in incident response, promoting efficient mitigation strategies without overwhelming human operators. The proposed model demonstrates scalability and adaptability to various network environments, offering a promising solution to combat evolving cyber threats in real time.

KEYWORDS: Artificial Intelligence, Cybersecurity, Machine Learning, Intrusion Detection, Anomaly Detection