AI-Powered Cybersecurity: Graph-Based Anomaly Detection in Network Traffic

Abstract

This paper presents an AI-based cybersecurity model based on anomaly detection of network traffic analysis via graph-based analysis. The proposed model is founded on the graph structures and advanced machine learning techniques that are employed in the identification of advanced patterns of attacks with high accuracy. The results indicate that the detection rate is high at 96 as compared to traditional signature based systems which had a detection rate of 79. The model reduces the false positive occurrence by 21-9 = 57% that is bettering 57 percent of the reliability of the detection. Besides, the framework shortens the detection latency by 45 and this enables cyber threats to be addressed within a shorter time. The system can scale and is effective in large network environment at above 89 percent and it is detecting and more than 92 percent in all types of attack, including DDoS and malware traffic. The findings indicate the effectiveness of graph-based AI model in enhancing cybersecurity performance, reducing the operating cost by an approximate of 28 percent, and overall network resilience. Although the computational complexity and interpretability issues may be associated with the proposed framework, the framework still has a strong and scalable solution to the current cybersecurity systems.

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