Leveraging Quantum Computing in Multiclassification Fusion to Enhance Network Intrusion Detection Performance

Security of networks is becoming progressively more necessary as the commercial internet keeps on developing. Intrusion detection techniques using network data have emerged as a significant area of investigation among the various network protection solutions. Many techniques for detecting network anomalies were suggested up to this point. A substantial amount of labeled data is needed for modeling in Machine Learning (ML) that constitutes the basis of the majority of more effective performance detection strategies. Nevertheless, manually filtering and labeling large-scale traffic data is not feasible in a real network. For real detection, network managers can only employ unsupervised ML techniques and the results of such strategies are far less accurate than those of supervised ML strategies. To increase network security’s efficiency of processing, adaptability, and specificity of detection, this method is useful for instantaneous, network-based safety applications because it maximizes intrusion detection accuracy across several attack types by utilizing quantum computer technology. This paper suggests an innovative multiclassification fusion strategy for optimizing network intrusion detection performance. Numerous fusion approaches are used in this research to combine the identification outcomes of effective intrusion detection approaches. A variety of publicly available KDD Cup 99 datasets were utilized for comparative investigations. According to the experiments, the suggested fusion approach [Decision Tree, K-Nearest Neighbor (KNN), improved Siam Intrusion Detection System (I-SiamIDS), Long Short-Term Memory (LSTM)] outperforms individual intrusion detection techniques in multiple measurements. It also exhibits enhanced resilience and stability, making it suitable for use in a variety of network-based safety recognition settings. The study uses the four primary classes of attacks such as Remote-to-Local (R2L), User to Root (U2R), Denial of Service (DoS), and Probing (Probe), which were compared with the performance metrics such as Precision, F-score, Accuracy, and Recall.