Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 264-269· 0 citations· 27 references
Abstract
The growth of Internet of Things devices has expanded the number of attackable targets of advanced cyber threats by orders of magnitude, and the limited computational capabilities of IoT devices have made more traditional intrusion detection systems infeasible to execute at the edge. A Knowledge-Distilled Multi-Model Intrusion Detection and Prevention System is presented, which combines a Residual Graph Convolutional Network, Long Short-Term Memory classifier, and a Conditional Tabular Generative Adversarial Network enhanced with the student-only autoencoders through multi-loss knowledge distillation to create a high-performing Teacher ensemble and compresses its intelligence into an edge-deployed system. The system is evaluated using the IoT-23 benchmark of 325 million network flow records and attains 98.2% Teacher-Student fidelity, 0.94 Knowledge Retention Score, and 4.4x throughput increase with 82.7% reduction in the parameter and provides per-packet SHapely Additive exPlanation forensic explainability.
A lightweight, explainable IDS that combines a 1D-CNN for spatial feature analysis with SHAP for model interpretation, yielding streamlined models that preserve over 93% F1-score and reduce computational overhead by more than 38%, facilitating millisecond-level inference on edge hardware.
Miracle Udurume, Vladimir V. Shakhov, Insoo Koo· Scientific Reports· 0 citations
The framework introduces CNN–BiLSTM deep learning networks to represent traffic in a spatiotemporal manner and adopts ensemble machine learning classifiers to enhance the robustness of traffic detection and its interpretability, to enhance the robustness of traffic detection and its interpretability.
Ramesh N. S. V. S. C. Sripada, A. Bhavani, Kiran B. Malagi et al.· Discover Computing· 0 citations
This study proposes a hybrid machine learning-based intrusion detection and prevention framework for securing IoT networks that integrates Isolation Forest, Autoencoder, Extreme Gradient Boosting, and Bidirectional Long Short-Term Memory models within a stacked ensemble architecture to improve attack detection while reducing false-positive predictions.
Ruthwik Palem, Likhith Reddy Peketi, Vanathi M et al.· Cureus Journal of Computer S...· 0 citations
The Hybrid Autoencoder–TabTransformer framework provides an effective intrusion detection solution that demonstrates strong performance under the evaluated experimental conditions and comparative analysis with existing deep learning‐based intrusion detection approaches confirms the superior and balanced performance of the proposed method.
Rui Guo, Guangjun Wen· Transactions on Emerging Tel...· 0 citations
LSTM had good detection for frequent attacks and slow-changing patterns, which shows its capacity in learning long-lasting dependencies, which shows its capacity in learning long-lasting dependencies.
Jawad Hussain Awan, Misbah Safdar, Muhammad Ayaz Shirazi et al.· Italian National Conference...· 0 citations
A new explainable hybrid IDS architecture for IoT environments named XABiL-IDS (Explainable Attention-based Bi LSTM-Intrusion Detection System) in response to this challenge, which uses a robust hybrid architecture to detect attacks effectively.
Ravi Patni, Gurvinder Singh· International journal of com...· 0 citations
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