Intelligent Intrusion Detection System Using Hybrid Swin Transformer-RNN for Efficient Cyber Threat Mitigation
Due to the rising frequency as well as complexity of Cyber-attacks the real-time Intrusion Detection Systems (IDS) have a greater demand for reliable. Conventional IDS techniques frequently encounter performance limitations when dealing with high-dimensional data as well as temporal patterns. In order to efficiently detect and prevent cyber-attacks, this research offers a hybrid Swin Transformer and Recurrent Neural Network (RNN) model with Principal Component Analysis (PCA) for dimensionality reduction. To identify time-dependent patterns and spatial linkages in network traffic, spatial and temporal learning modules are used. To handle complicated data and retain high predicted accuracy, a hybrid model that combines the advantages of the Swin Transformer and RNN is used for training. Using Network Intrusion dataset (CIC-IDS-2017) from kaggle delivers accuracy, precision, F1-score, as well as AUC-ROC measures for the proposed method is of 99.9%. Method delivers an accessible as well as effective resolution for contemporary cyber security requirements by addressing the difficulties of real-time detection in high-dimensional datasets.