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Research Topics for Intrusion Detection in Industrial Internet of Things

Research Topics for Intrusion Detection in Industrial Internet of Things

Latest Research Topics for Intrusion Detection in Industrial Internet of Things

   The industrial intrusion detection system is defined as monitoring the network activity for attack identification and detecting the malicious activity. The intrusion detection systems are broadly classified into three types that are anomaly-based, knowledge-based, and hybrid. The anomaly-based IDS models exploit the machine learning algorithms to classify the network data into normal and malicious.
   The knowledge or signature-based IDS models employ the pre-defined network knowledge to detect malicious activities. The anomaly-based IDS models can detect novel attacks, whereas the knowledge-based IDS models can only detect the known attacks. The hybrid models employ the advantages of both anomaly and signature-based IDS models. Based on the IDS placement architecture, the IIDS is categorized into centralized and distributed. The centralized IDS models necessitate a centralized server for intrusion detection activities. Unlike, the distributed model places the IDSs distributed over the network and takes the attack detection decisions distributive. The distributed models are more powerful than centralized models, as the distributed system enhances the attack detection accuracy by considering the monitoring activities of many IDSs.
   Due to the development of IIoT usage, new security concerns have arisen. Existing security measures are vastly inferior, especially for internet-connected smart devices. They lead the IIoT for DDoS-based attacks by botnets like Mirai. Previously, several Intrusion Detection Systems (IDSs) have been developed to against security attacks and to protect IIoT systems. The main issue behind the IDS execution over IIoT is collecting sufficient and appropriate information. Recent research on Intrusion detection in IIoT focuses on deep learning-based intrusion detection with feature selection to train and test the captured data packets over IIoT.