Intrusion Detection and Prevention System (IDPS) Model for IIoT Environments Using Hybridized Framework - Manohar Srinivasan; N. C. Senthilkumar (IEEE Access)
As the Industrial Internet of Things (IIoT) becomes more popular, cyber threats have more places to attack. This is why intrusion detection and prevention systems (IDPS) are so important for keeping industrial environments safe and secure. The main goal of the proposed research is to create a complete Intrusion Detection and Prevention System (IDPS) for IIoT. This system will include detection and protection security models to keep the network safe from cyberattacks and other strange things happening. Convolutional Neural Networks (CNNs) are used in pattern recognition for the detection and protection models in this research. This helps find IIoT networks with strange traffic patterns. Additionally, blockchain-assisted reinforcement learning (RL) uses real-time learning and decision-making to stop or lessen threats on its own. The novelty of this research lies in the combination of deep learning and blockchain-based security for intrusion detection and prevention. While there are already models for finding intrusions, this is the first time that reinforcement learning has been used for dynamic threat prevention along with blockchain to ensure secure communication and data integrity in the IIoT domain. This hybrid approach ensures a higher level of security by continuously learning and adapting to new types of attacks. This approach utilizes a novel Intrusion Detection and Prevention System (IDPS) designed for IIoT environments, which is capable of real-time detection and response to cyber threats. In the simulation parameters, this research shows higher detection accuracy and lower false positive rates using the proposed hybrid model. The integration of deep learning and blockchain technology enhances security for industrial applications.