WSNetDefender: Securing Wireless Sensor Networks Using BBIDNet and Fuzzy-DQN Threat Mitigation System (FD-TMS)

In the Wireless Sensor Network (WSN), there has been a significant increase in intruders, owing towards rapid expansion in the cyber-space that encloses multi-layered cybersecurity. The aim of this work is to tackle the increasing cybersecurity issues in Wireless Sensor Networks (WSNs) by proposing an innovative framework named WSNetDefender. It encompasses a Boosted BiLSTM Intrusion Detector Network (BBIDNet) to identify intrusions with precise accuracy and a Fuzzy-DQN Threat Mitigation System (FD-TMS) to respond to threats dynamically. The framework aims to enhance the accuracy of detection of intrusion while reducing false positives and resource overhead.The first step involves gathering data from two distinct wireless sensor network datasets. Subsequently, preprocessing is done using the new Integrated Preprocessing Engine (IPE), wherein the Gaussian filtering for denoising, Kalman filtering for data fusion (2 databases), and Min-Max normalization for standardization are available. Then, features are extracted using the new NetFlow Profiling Network (NPN), which encloses the CAN Bus Analysis, Behavior Profiling, and Netflow Analysis. The newly introduced Jackal-Wolf Hybrid Optimizer (JWHO) assisted in selecting the optimal features from NPN, thereby assisting the model in lowering computing difficulty in terms of both memory and time. The proposed JWHO is the conceptual synergy of Golden Jackal Optimization and Grey Wolf Optimizer (GJO-GWO), respectively. The extracted JWHO-based features are utilized to train the new BBIDNet, which makes the accurate detection of intruders in WSN. This BBIDNet is the combination of the Bidirectional Long Short-Term Memory (BiLSTM) network with AdaBoost. Once, an attacker is found to be available, then for the concern attacker is mitigated via the new FD-TMS. In FD-TMS, the threat scoring and prioritization are undergone with respect to fuzzy rules, and based on the critical level identified, the mitigation is done using the Deep Q-Network (DQN) approach. As a consequence, there are noteworthy results from the implementation process in a variety of metrics, including F1-score, FPR, precision, FNR, NPV, MCC, accuracy, sensitivity, and specificity. The accuracy recorded by the suggested model is 99.6% and 99.7% for database-1 and database-2, respectively.