A Robust Cross-Domain IDS using BiGRU-LSTM-Attention for Medical and Industrial IoT Security

The increased Internet of Medical Things IoMT and the Industrial Internet of Things IIoT interconnectivity has introduced complex cybersecurity challenges, exposing sensitive data, patient safety, and industrial operations to advanced cyber threats. To mitigate these risks, this paper introduces a novel transformer-based intrusion detection system IDS, termed BiGAT-ID a hybrid model that combines bidirectional gated recurrent units BiGRU, long short-term memory LSTM networks, and multi-head attention MHA. The proposed architecture is designed to effectively capture bidirectional temporal dependencies, model sequential patterns, and enhance contextual feature representation. Extensive experiments on two benchmark datasets, CICIoMT2024 medical IoT and EdgeIIoTset industrial IoT demonstrate the model's cross-domain robustness, achieving detection accuracies of 99.13 percent and 99.34 percent, respectively. Additionally, the model exhibits exceptional runtime efficiency, with inference times as low as 0.0002 seconds per instance in IoMT and 0.0001 seconds in IIoT scenarios. Coupled with a low false positive rate, BiGAT-ID proves to be a reliable and efficient IDS for deployment in real-world heterogeneous IoT environments

Enhancing IoT Security with CNN and LSTM-Based Intrusion Detection Systems

Protecting Internet of things (IoT) devices against cyber attacks is imperative owing to inherent security vulnerabilities. These vulnerabilities can include a spectrum of sophisticated attacks that pose significant damage to both individuals and organizations. Employing robust security measures like intrusion detection systems (IDSs) is essential to solve these problems and protect IoT systems from such attacks. In this context, our proposed IDS model consists on a combination of convolutional neural network (CNN) and long short-term memory (LSTM) deep learning (DL) models. This fusion facilitates the detection and classification of IoT traffic into binary categories, benign and malicious activities by leveraging the spatial feature extraction capabilities of CNN for pattern recognition and the sequential memory retention of LSTM for discerning complex temporal dependencies in achieving enhanced accuracy and efficiency. In assessing the performance of our proposed model, the authors employed the new CICIoT2023 dataset for both training and final testing, while further validating the model's performance through a conclusive testing phase utilizing the CICIDS2017 dataset. Our proposed model achieves an accuracy rate of 98.42%, accompanied by a minimal loss of 0.0275. False positive rate(FPR) is equally important, reaching 9.17% with an F1-score of 98.57%. These results demonstrate the effectiveness of our proposed CNN-LSTM IDS model in fortifying IoT environments against potential cyber threats.