Enhancing Intrusion Detection in Wireless Sensor Networks Using Deep Learning-Based K-Barriers Prediction

For wireless sensor networks (WSNs) to remain secure, effective intrusion detection is a vital requirement. This research work enhances a feed-forward artificial neural network (ANN) based on deep learning as a novel approach to intrusion detection. The proposed approach focuses on precisely forecasting the count of k-barriers, which is essential for efficient intrusion detection and mitigation. Important characteristics, such as the region of interest, sensor sensing area, sensor count, and sensor sensing transmission area, all derived via Monte Carlo simulation, are utilize to train and evaluate the model. The model successfully estimates k-barrier counts, which is demonstrated by the achieved Root Mean Square Error and correlation coefficients. A conditional generative adversarial network (CGAN) based intrusion detection system and an extreme gradient boosting (XGBoost) classifier for quick result comparison and visualization are developed to further increase the system's effectiveness. The suggested approach has the ability to drastically reduce false alarms and eliminate the requirement for additional sensors to produce false data. The created deep learning architecture uses a fully linked feed-forward ANN with a focus on quick intrusion detection and prevention. Through thorough training and evaluation, this ANN achieves amazing accuracy in predicting k-barrier counts. Comparative assessments demonstrate the suggested method's advantage in terms of precision and computational effectiveness, supporting its value in enhancing wireless sensor network security.