Abstract
In today's digital era, the security of networked systems is of utmost importance amidst the increasing prevalence of cyber threats and sophisticated intrusion techniques. This paper addresses the critical need for robust network intrusion detection systems (NIDS) in today's digital landscape, amidst escalating cyber threats. Leveraging a dataset derived from a simulated military network environment, we explore various intrusion scenarios encountered in cyber warfare. Reviewing existing literature reveals a spectrum of methodologies, including anomaly-based and deep learning approaches. To enhance current methodologies, we propose a binary classification framework using TabTransformer, a transformer-based architecture, for network intrusion detection. We present detailed methodology, encompassing data preprocessing, model architecture, and evaluation metrics, with empirical results demonstrating the efficacy of our approach in mitigating cyber threats and enhancing network security.
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