Aug 2026· International Conference on Circuit, Power and Computing Technologies· pp. 2237-2243· 0 citations· 29 references
Abstract
The majority of assaults in heterogeneous networks are detected by intrusion detection systems (IDS). Cyberattack kinds that seriously harm networks are difficult for conventional IDSs to detect. The majority of existing solutions rely on deep learning models, which have a significant computational and energy overhead that limits their use in IoT environments with limited resources. A lightweight IDS based on ML is proposed in this research as a solution to this difficulty. Predicting the behavior of network traffic is achieved using ToN-IoT data and a tailored preprocessing pipeline. The voting-based ensemble classifier is built through the combination of models of RF and LightGBM to enhance the stability of the classification. The standard performance measures that are utilized to evaluate the proposed approach include accuracy, precision, recall, F1score, false alarm rates, and ROC analysis. The experimental findings indicate that RF achieve 99.81% accuracy, LGBM achieve 99.83%, and the ensemble model has a high accuracy of 99.99% with very low false alarms. Comparative evaluation with traditional ML and DL models demonstrates improved detection reliability with reduced computational overhead. These results prove that the suggested architecture is both computationally efficient and practically applicable to IoT settings with limited resources. However, direct hardware-level energy measurements are required to fully quantify the energy-saving characteristics of the proposed IDS.
A fully automated IoT-based Network Intrusion Detection System (NIDS), utilizing the Gotham Dataset 2025, using two AutoML approaches: TPOT (Tree-based Pipeline Optimization Tool) and FLAML (cost-aware lightweight AutoML framework).
Susan Al Naqshbandi· Sulaimani Journal for Engine...· 0 citations
The rapid propagation of Internet of Things (IoT) devices has significantly expanded the cyber-attack surface, particularly in essential infrastructure sectors such as energy, water, and healthcare. Machine learning (ML) based intrusion detection systems (IDS) offer a promising defense, but their real-world deployment is often hindered by data imbalance, lack of interpretability, and computational demands. In this paper, we introduce a lightweight ensemble approach, which integrates XGBoost and LightGBM using a soft-voting method. The system is evaluated on the IDSAI dataset after eliminating duplicates, resulting in 693,116 unique samples with a natural class imbalance. The preprocessing phase includes data cleansing and data scaling. The results indicate that the proposed ensemble achieves 99.95% accuracy, 99.95% F1-score, and a perfect AUC of 1.0 on a test set of 207,935 samples. Training completes in under 8 seconds on a standard CPU. The feature importance (gain) highlights delta_time; packet inter-arrival time, as the most significant feature, followed by source/destination ports. SHapley Additive exPlanations (SHAP) analysis provides local explanations, revealing that high inter-arrival times push predictions toward malicious—likely due to slow scanning or burst-and-pause attack patterns. All code and the trained model are publicly available to facilitate reproducibility1.
Nooruddine F. Assarwie, F. Alqasemi, Tasnim M. Al-Khawlani et al.· 2026 6th International Confe...· 0 citations
An intelligent cyberattack detection system that applies machine learning and deep learning techniques to classify network traffic as either normal or malicious, and demonstrates the potential of machine learningbased intrusion detection systems in improving network security and supporting the protection of modern smart environments.
KADADHARAPU ANUPRIYA, Dr.S.SWATHI RAO· International Journal of Eng...· 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
An intelligent hybrid deep learning framework based on a combination of deep neural networks (DNNs) and random forests (RF) to ensure the security of 5G IIoT networks for use in critical areas such as smart factories, cyber-physical systems, power grids, and industrial automation.
Rohan Rajoriya, Shweta Chouksey· International journal of com...· 0 citations
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