Deep ensemble of classifier for intrusion detection in WSN and improved attack mitigation process

Summary Attacks have a significant negative impact on the wireless sensor network's network operations. They target to weaken the network layer, if they are not completely eradicated, the networks' ability to execute their desired function becomes collapsed. While intended to monitor such unexpected attacks, anomaly‐based intrusion detection systems (AIDS) have a high risk of false positives. This article intends to propose a Deep Ensemble Intrusion detection with a Self‐adaptive Sewing Training‐Based Optimization (DIED‐SASTO) model that includes the following steps: (a) preprocessing, (b) feature extraction, (c) optimal feature selection, and (d) detection. Initially, an improved class imbalance processing will take place to solve the imbalance problem during the preprocessing step. Subsequently, the features are extracted including higher‐order statistical features, improved entropy, and correlation‐based features during the feature extraction step. From the extracted feature set, as the curse of dimensionality becomes a serious challenge, it is significant to choose the optimal features. In the optimal feature selection process, a new Self‐adaptive Sewing Training‐Based Optimization (SASTO) is used. In addition, the intrusion detection will be employed based on the selected optimal features and trained using deep ensemble models like Deep maxout, Deep Belief Network (DBN), and Bidirectional Long short‐term memory (Bi‐LSTM). Once the presence of an attack is detected, it is mandatory to mitigate the attacker node form the network. For Attack Mitigation, improved entropy will be progressed. The effectiveness of the DIED‐SASTO is evaluated over conventional methods in terms of various metrics.