Jul 2026· International Mediterranean Conference on Communications and Networking· pp. 1-7· 0 citations· 11 references
Computer Science
Abstract
The Internet of Things (IoT) has become increasingly integrated into our daily lives, offering a wide range of services through the proliferation of connected devices. While this connectivity enhances convenience and functionality, it also introduces significant security challenges, exposing IoT systems to various forms of cyberattacks. In this paper, we propose a lightweight edge-based intrusion detection approach for massive IoT networks, leveraging a Long Short-Term Memory (LSTM) model to achieve high accuracy with minimal resource consumption. Unlike centralized solutions, the proposed system is fully implemented and deployed at the edge level, enabling local traffic analysis directly on edge devices. This design reduces latency, minimizes bandwidth consumption, enhances data privacy, and ensures real-time detection capabilities in large-scale IoT environments. The approach incorporates an efficient data pre-processing methodology applied to the wellknown Avast IoT-23 dataset, resulting in a detection accuracy of 99.9% with a compact model size of only 1767 KB. To further optimize performance, the system decomposes the data into clusters before applying a tailored LSTM model for each subset. Experimental evaluation using real malicious traffic demonstrates that the proposed model achieves up to 90% specificity and 88% precision under real-world conditions. These results confirm the effectiveness of our edge-level LSTM framework in providing secure, scalable, and resource-efficient intrusion detection for large-scale IoT environments.
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 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 explainable hybrid feature-selection framework (X-EFS) that combines multiple feature reduction techniques via a multi-expert system module, then uses the MDA metric to select the most important features, ensuring high performance and explainability.
Minh Trọng Hoàng, Le Thi Trang Linh, Hoang Minh Nguyen et al.· Journal of Communications So...· 0 citations
The results demonstrate the effectiveness of the proposed Tiny-IDS in accurately identifying Mirai botnet attacks on IoT devices along with a minimal memory footprint and low inference time, while also emphasizing the need for IoT-specific evaluation frameworks to support the development of robust and lightweight IDS.
Shyam Bahadur, Sudhanshu Kumar Jha, Rajkumar Singh Rathore et al.· IEEE Open Journal of the Com...· 0 citations
This work proposes a novel multiclass intrusion detection system using FL at the subnetwork level using various machine learning models, including Artificial Neural Networks, Convolutional Neural Networks, and Long Short-Term Memory to build an effective IoT IDS.
Mamta Rawat, Manan Suri, Gaurav Singal· Wireless personal communicat...· 0 citations
This paper introduces an innovative ML-based security paradigm that improves the attack detection accuracy by combining adaptive feature extraction techniques with a context-attentive hybrid mechanism and maximizes detection accuracy and computational efficiency.
P. P. Bairagi, Ashish Bagwari, Sailen Dutta Kalita et al.· international journal of eng...· 0 citations
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