Network traffic analysis based on cybersecurity intrusion detection through an effective Automated Separate Guided Attention Federated Graph Neural Network

The increasing demand for the Internet of Things (IoT) and various distributed devices has highlighted the need for reliable and effective intrusion detection systems to safeguard physical devices and data from cyber threats. However, managing large volumes of data with diverse dimensions and security features poses significant challenges, including reduced recognition accuracy, increased computational time, a large number of false positives, and overfitting issues. Integrating Artificial Intelligence (AI) into intrusion detection systems has recently gained attention to enable intelligent recognition and protection against cyber threats. This research introduces a novel architecture called Automated Separate Guided Attention Federated Graph Neural Network (ASGAFGNN) for predicting and detecting cyberattacks. Initially, cyberattack data is gathered and pre-processed to enhance its quality. The pre-processed data then undergoes feature extraction to obtain global, local, and temporal features using a hybrid vision transformer with bidirectional long short-term memory. The extracted features are further processed using Batched Sparse Principal Component Analysis (BSPCA) and Intuitionistic Fuzzy C-Means (IFCM) for feature reduction. Finally, ASGAFGNN is deployed to detect and categorize network traffic as benign or malicious. An Enhanced Osprey Optimization Algorithm (EOOA) minimizes the network's loss function. The proposed technique is executed on four datasets including CICIDS2017, UNR-IDD, NSL-KDD, and NF-UQ-NIDS-v2, achieving detection accuracies of 99.98 %, 99.44 %, 99.74 %, and 99.95 %, respectively and it outperforms the existing schemes.