Deep Learning-Based Intrusion Detection Technique for IoT Security

Numerous connected machines compose the Internet of Things (IoT) like sensors and actuators, via wired or wireless networks. The number of IoT apps, including Vehicular Ad-hoc Networks (VANETs), Healthcare, Smart communities, and Wearable have recently risen significantly. Data Protection and Security is becoming more critical as the number of IoT-linked devices grows. In this paper, we suggest a new deep learning-based intrusion detection system (DL-IDS) to identify security breaches in IoT environments to address the difficulties of protecting IoT applications. There are several IDSs developed by researchers, optimal learning and management of data set functions are neglected, which are essential problems that impact the accuracy of tracking threats. To achieve optimum detection recognition, the presented framework incorporates the SMO technique and the Deep stacked polynomial network. SMO chooses the optimal features in the data, and SDPN categorizes it as regular or vulnerable. The presented model is analyzed for performance assessment in comparison to the state-of-The-Art decision-making models. In terms of accuracy, recall, precision, and F-score, the detailed review suggests that presented technique acquires enhanced performance and is immensely efficient.