Sep 2026· IAES International Journal of Robotics and Automation· 0 citations· 25 references
TL;DR
A new graph-guided contrastive transformer-based intrusion detection system (GCT-IDS) which aims at improving detection accuracy and robustness and preserving real-time feasibility is presented.
Abstract
Intrusion detection systems (IDS) are very instrumental in protecting contemporary network infrastructures against the ever-advancing cyberattacks. Conventional signature-based and machine learning-enabled IDS solutions frequently have difficulty when it comes to high false-positive rates, inability to flexibly adapt to novel attacks, and the lack of support for complex traffic dynamics. New deep learning architectures have better detection properties, yet are limited by feature overlap, temporality, and lack of extensiveness to generalization in changing network conditions. To overcome these issues, this paper presents a new graph-guided contrastive transformer-based intrusion detection system (GCT-IDS) which aims at improving detection accuracy and robustness and preserving real-time feasibility. The framework combines feature interaction by graph modeling, contrastive representation learning, and a sparse self-attention transformer to effectively learn global traffic relationships and behavioral variations. The CSE-CIC-IDS2018 data is used to test the proposed method in real network conditions.
A Transformer-based network intrusion detection system (t-NIDS) that integrates a Transformer encoder with contrastive learning to learn discriminative and robust feature representations for network traffic classification is proposed, suitable for practical deployment in real-world network intrusion detection environme...
The exponential growth of interconnected systems has dramatically expanded the attack surface of modern networks, making zero-day attacks, which exploit previously unknown vulnerabilities, a critical threat to organizational security. Traditional signature-based and anomaly detection systems often struggle to identify...
An Enhanced Multi-Model Ensemble Network Intrusion Detection System (EME-NIDS), a deep meta-learning system that combines five different heterogeneous learning paradigms, including Convolutional Neural Networks, Dense Neural Networks, Transformers, XGBoost, and Random Forests is introduced.
The principal contribution of this work is architectural and diagnostic rather than a performance improvement: it documents that combining feature-wise attention with out-of-fold stacked generalization does not, in this setting, outperform a plain multi-layer perceptron, while incurring the highest memory footprint of...
Mahima Khanna, V. Murthy, Siva Ramavarapu et al.· International Journal for Gl...· 0 citations
In this review, the shift from traditional intrusion detection systems (IDSs) to machine-learning based network intrusion detection and explicit explainability is explored. The source material is assembled with recent literature and the field is structured around five recurring concerns: detection capability, data qual...
Abhishek Tiwari, S. Degadwala· International Journal of Sci...· 0 citations
Dynamic Multiscale Graph Contrastive Representation Learning (DMGCRL), a self-supervised framework that hierarchically models network intrusions at different levels, is proposed, which consistently outperforms SOTA methods in network intrusion detection.
Raeed Al-Sabri, Abdullatif Albaseer, Mohamed M. Abdallah et al.· IEEE Transactions on Network...· 0 citations
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