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Siamese Neural Network-Based Intrusion Detection System

Jonston Davis C, Krishna S Ayyappan, Mefin S R, Shehanaz Begum S, G. Venifa Mini

Abstract


Intrusion Detection Systems (IDS) play a critical role in cybersecurity, identifying and mitigating unauthorized access. Traditional IDS face challenges related to high false-positive rates and computational costs. In this paper, we propose a novel approach using a Siamese Neural Network (SNN) to improve detection accuracy while optimizing resource utilization. Our model is evaluated on the UNSW-NB15 dataset, demonstrating superior performance compared to conventional IDS solutions. This study provides an in-depth discussion on the methodology, dataset preparation, model architecture, hyperparameter tuning, feature extraction, evaluation metrics, and comparative analysis.


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References


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