Unbalanced Traffic Intrusion Detection Using Advanced Deep Learning Techniques

The work attempts a deep learning-based approach unbalanced network traffic intrusion detection, applying AE, DBN networks, and SNN models. The proposed system will efficiently extract features from raw network traffic data by employing AE. For the better analysis of temporal dependencies in the sequence of traffic data, the work will make use of DBN and SNN models for increasing the accuracy in intrusion detection. Malicious intrusions that disturb normal traffic flow are identified by the model through the analysis of network traffic patterns. Network traffic datasets are used to train the model with many different kinds of traffic behavior patterns. Performance was again checked using accuracy, precision, and recall metrics, which determine how well the model would detect the varying traffic intrusions and classify them aptly. This method provides a critical solution toward network security in real-time applications since it addresses the ever-increasing problem of cyber-attacks on network infrastructures.