Intrusion Detection System using Convolutional Neural Network for Industrial Internet of Things Security

Abstract

The rise of Industry 4.0 has led to the widespread adoption of Industrial Internet of Things (IIoT) devices, enhancing manufacturing efficiency while introducing significant cybersecurity risks. IIoT environments are highly susceptible to cyber threats such as Denial-of-Service (DoS), SQL injection, and ransomware, which can lead to production downtime and data breaches. Traditional intrusion detection systems (IDS) often fail to detect evolving threats, resulting in high false negative rates. This research proposes an advanced IDS integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) to enhance IIoT security. By leveraging both spatial and temporal feature extraction, the proposed model effectively identifies network anomalies in real-time industrial environments. This study contributes to IIoT cybersecurity by developing an IDS capable of improving threat detection through the integration of CNN and LSTM architectures. The approach enhances pattern recognition and sequential dependency modeling, making it more adaptive to dynamic cyber threats. The model is trained and evaluated on a large-scale IIoT dataset, achieving a binary classification accuracy of 71%, outperforming several state-of-the-art models. The CNN-LSTM IDS demonstrates a strong ability to recognize normal traffic, with a recall of 99%, significantly reducing false alarms. In multi-class classification, the model successfully identifies certain high-volume attack types, such as DDoS. These findings underscore both the strengths and limitations of deep learning-based intrusion detection in IIoT environments. While the proposed model offers significant improvements, further research is needed to address the detection of low-frequency attacks and optimize classification performance.