Hybrid ILinkNet–Squeeze Model for Attack Detection and Improved Entropy-Based Attack Mitigation in Industrial Internet of Things

Abstract

Due to the complex, real-time, and resource-constrained nature of the industrial Internet of Things (IIoT) network, cyberattack detection is becoming more challenging. It's hard for traditional detection techniques to adapt to the dynamic environment of IIoT. Although signature-based and anomaly-based approaches are utilized for intrusion detection system (IDS) in IIoT, they have limitations in detecting evolving threats and inefficiency in threat management, which proves the novel IDS model's requirement in the IIoT network environment. Consequently, a novel hybrid ILinkNet–SqueezeNet-based attack detection and mitigation model is introduced, which comprises four stages. In the initial preprocessing stage, an improved synthetic minority oversampling technique is proposed for the class imbalance solution, while min–max normalization is applied for data scaling. Afterward, feature extraction is employed, where features like Mutual Information-based features, ReliefF features, holoentropy features, and Higher-Order Statistics features are extracted to provide accurate attack detection. To choose the most useful features from the extracted features, an improved filter-based feature selection is conducted. With these chosen features, the attack detection process is conducted using the proposed hybrid ILinkNet–SqueezeNet-based model, while an improved entropy-based model is proposed for attack mitigation in IIoT. The experimental evaluation demonstrates that the proposed hybrid model significantly outperforms traditional methods in terms of accuracy, sensitivity, specificity, and precision, as evidenced by the experimental results using the WUSTL-IIoT-2018 and Internet of Things Intrusion Datasets. The model achieves detection accuracies of up to 96.6% with a sensitivity of 0.97 and specificity of 0.976, showcasing its effectiveness in providing robust security in IIoT environments. These findings highlight the model's potential to enhance IIoT security by accurately detecting and mitigating evolving cyber threats in dynamic and resource-constrained environments.