AI-POWERED CYBERSECURITY: GRAPH-BASED ANOMALY DETECTION IN NETWORK TRAFFIC

Sohail Ahmad; Raza Iqbal; Fatima Ubaid; Abdul Sattar

doi:10.71146/kjmr896

Authors

Sohail Ahmad Department of Computer Science and Software Technology University of Swat Khyber Pakhtunkhwa Pakistan. Author
Raza Iqbal Department of Computer Science, National College of Business Administration & Economics (AL-HAMRA UNIVERSITY) Multan Campus Multan, Pakistan. Author https://orcid.org/0009-0007-0687-2917
Fatima Ubaid Department of Engineering and Information Technology Foundation University, Rawalpindi, Punjab, Pakistan. Author
Abdul Sattar Department of Computer Science, Government College University Faisalabad, Punjab, Pakistan. Author

DOI:

https://doi.org/10.71146/kjmr896

Keywords:

Cybersecurity, Graph-Based Anomaly Detection, Network Traffic Analysis, Artificial Intelligence, Graph Neural Networks, Threat Detection

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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