Hybrid deep learning model for attack detection in internet of things

Internet of things (IoT) provides a new application, which helps the existing networks communicate with smart technologies. Things are now becoming increasingly connected to the Internet, and lots of new gadgets are being created at a faster rate. Since these interconnected smart objects are capable of interacting with one another in undefended surroundings, the entire communication ecology needs solutions related to security at various levels. Unlike the existing networks, IoT technology has its own set of features, including various network protocol requirements and a variety of resource constraints. To launch different attacks, the attacker takes many security vulnerabilities in the IoT system. The growth in cyber-attacks has rendered it important to address the consequences implied in the IoT. This paper intends to introduce a novel attack detection model. Originally, the input data are preprocessed, from which the most relevant features are extracted that include raw features, statistical features, and higher-order statistical features. The extracted features are subjected to the classification process. More importantly, the extracted raw features are directly given to the long short-term memory (LSTM), and the extracted statistical and higher-order statistical features are subjected to the deep reinforcement learning (DRL) for the classification process. Then, the average of both LSTM and DRL provides the detected output in an effective manner. To improve the performance of detection results, the weight of LSTM is optimized by the self-improved battle royale optimization (SIBRO) algorithm. At the end, the performance of the presented scheme is compared to the existing approaches in terms of different metrics like “F-measure, specificity, NPV, accuracy, FNR, sensitivity, precision, FPR, and MCC,” respectively.