Efficient IoT Intrusion Detection with an Improved Attention-Based CNN-BiLSTM Architecture
This provides an efficient intrusion detection solution for IoT networks, though it appears incremental as it builds on existing hybrid architectures.
The paper tackled botnet attack detection in IoT systems by proposing an attention-based CNN-BiLSTM model, achieving 99% classification accuracy on the N-BaIoT dataset with high precision and recall.
The ever-increasing security vulnerabilities in the Internet-of-Things (IoT) systems require improved threat detection approaches. This paper presents a compact and efficient approach to detect botnet attacks by employing an integrated approach that consists of traffic pattern analysis, temporal support learning, and focused feature extraction. The proposed attention-based model benefits from a hybrid CNN-BiLSTM architecture and achieves 99% classification accuracy in detecting botnet attacks utilizing the N-BaIoT dataset, while maintaining high precision and recall across various scenarios. The proposed model's performance is further validated by key parameters, such as Mathews Correlation Coefficient and Cohen's kappa Correlation Coefficient. The close-to-ideal results for these parameters demonstrate the proposed model's ability to detect botnet attacks accurately and efficiently in practical settings and on unseen data. The proposed model proved to be a powerful defence mechanism for IoT networks to face emerging security challenges.