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Development of NetBot-AI: A Holistic Tool for Detecting and Mitigating VoIP Network Security Threats Using Hybrid Datasets
Abstract
The rapid adoption of Voice over IP (VoIP) has brought cost-effective, scalable communication, but also introduce security concerns, especially in areas with legacy infrastructure. Traditional methods struggle with emerging threats like SIP flooding and toll fraud. This study introduces NetBot-AI, an AI-powered system for real-time threat detection and adaptive response in VoIP networks. Using hybrid datasets and advanced preprocessing techniques, it employs Deep Q-Networks, ML classifiers, and the Ekolama (Eₖ) loss function to boost accuracy. Evaluated on 10 million call records, it achieved 98% binary and 95% multi-class accuracy with minimal false positives, reducing downtime by 40%. Its modular design ensures easy integration. Results show NetBot-AI outperforms traditional tools, offering scalable, proactive security for diverse telecom environments.
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