CNN-LSTM Based Network Anomaly Detection in WSN-DS

The chapter delves into the critical area of network anomaly detection, highlighting the increasing threat of malicious network attacks in the era of advanced internet technology. It explores the evolution of artificial intelligence in network intrusion detection, from early AI techniques to modern machine learning algorithms. The text provides an in-depth analysis of various machine learning methodologies, including K-nearest neighbors, support vector machines, and artificial neural networks, each employed to detect anomalies in network traffic. The core of the chapter focuses on the CNN-LSTM network, a hybrid model that combines convolutional neural networks with long short-term memory layers to extract and analyze complex features from network data. The study utilizes the WSN-DS dataset, which includes various network anomalies such as grayhole, flooding, scheduling, and blackhole attacks. Through rigorous preprocessing, training, and evaluation, the CNN-LSTM model demonstrates high accuracy and robustness in detecting these anomalies. The chapter also compares the performance of the CNN-LSTM model with other machine learning approaches, showcasing its competitive edge in identifying different types of network attacks. The results underscore the potential of CNN-LSTM in real-time network security applications, paving the way for further research and development in this field.