An efficient deep learning-based solution for network intrusion detection in wireless sensor network

This paper introduces a unique intrusion detection method that integrates developmental and operational frameworks, focusing specifically on the wireless sensor network. With the growing number of intrusions, safeguarding sensor nodes has become increasingly crucial. In addition to security breaches, unauthorized access to systems by fraudsters or intruders poses a risk to critical assets. Therefore, detecting and blocking potential threats in the wireless environment is of utmost importance. The proposed detection approach consists of two steps: feature extraction and classification. The study emphasizes the necessity of a distinct intrusion detection method and robust feature extraction and classification techniques. Incorporating a deep learning model is vital for enhancing the precision and accuracy of attack detection. Additionally, it is crucial for efficiency to optimize the CNN architecture's filter size and filter count. The proposed DevOps-based intrusion detection technique involves feature extraction and classification. During the feature extraction stage, statistics and higher-order descriptors are combined with existing characteristics in the early processing of application data. The extracted features are then utilized by the classification method in conjunction with an improved DCNN approach. The technique optimizes the quantity and size of filters in the input vector and fully connected layers. In terms of accuracy as well as FNR, sensitivity, MCC, specificity, FDR, FPR, and NPV, F-1-score against GAF-GYT and other attacks, the suggested technique outperforms conventional models. Specifically, in Application 3, the technique surpasses the DCNN, Innovative Gunner Algorithm, and FAE-GWO-DBN methods by 60.14%, 3.10%, and 5.46%, respectively. Furthermore, for Application 4, the suggested model demonstrates significantly lower FPR rates (91.46%, 67.15%, and 98.4%) compared to the FAE-GWO-DBN, AIG, and DCNN methods. Additionally, the suggested approach outperforms the DCNN, Innovative Gunner Algorithm, and FAE-GWO-DBN approaches by 69.76%, 3.27%, and 22.68%, respectively.