Epistemic uncertain computing for intrusion detection with explainability & multi-criteria optimization using AA-NLS and GRUSO-GRU.

Owing to incomplete and inaccurate data, intrusion detection in networks is uncertain. The out-of-order packet arrival uncertainty was not analyzed in the existing works, thereby causing false positives in intrusion detection. So, in this paper, a novel Algebraic Average-based Neutrosophic Logic System (AA-NLS)-based out-of-order uncertainty analysis is proposed. Firstly, the nodes are initialized, and the data is sensed. Then, for intrusion detection, the Network Intrusion Detection System (NIDS) is trained. Here, the data is collected and pre-processed. At the time of pre-processing, Weighted Distance Error Function-based K-Nearest Neighbour (WDEFKNN) is utilized to impute the uncertainty caused by missing values. Further, utilizing the Density-Based Beta Distribution Soergel Spatial Clustering of Applications with Noise (DBBDSSCAN), the behavioural patterns are grouped. Then, the features are extracted, and by utilizing AA-NLS, the epistemic uncertainty caused by out-of-order packet arrival is analyzed. In the meantime, by utilizing the Hidden Laplace Witten Bell Markov Model (HLWBMM), the temporal patterns are assessed. Then, the intrusion is detected by the Generalized Riccati Uniform Scaling Orthogonal-based Gated Recurrent Unit (GRUSO-GRU). Here, utilizing the Pareto Entropy-based SHapley Additive exPlanation (PESHAP) model, the explainability of the intrusion detection is carried out. If there is no intrusion, then data transmission is continued. And, if there is intrusion, then the gathered alerts are prioritized and transmitted utilizing the multi-criteria-based Log-Cosh Fennec Fox Optimization Algorithm (LCFFOA). Hence, the intrusion is detected by the proposed framework with an accuracy of 99.299% under the full multi-class setting, demonstrating effective performance compared to prevailing Deep Learning (DL)-based NIDS approaches.