Given the exponential growth of available data in large networks, the need for an accurate and explainable intrusion detection system has become of high necessity to effectively discover attacks in such networks. To deal with this challenge, we propose a two-phase Explainable Ensemble deep learning-based method (EED) for intrusion detection. In the first phase, a new ensemble intrusion detection model using three one-dimensional long short-term memory networks (LSTM) is designed for an accurate attack identification. The outputs of three classifiers are aggregated using a meta-learner algorithm resulting in refined and improved results. In the second phase, interpretability and explainability of EED outputs are enhanced by leveraging the capabilities of SHape Additive exPplanations (SHAP). Factors contributing to the identification and classification of attacks are highlighted which allows security experts to understand and interpret the attack behavior and then implement effective response strategies to improve the network security. Experiments conducted on real datasets have shown the effectiveness of EED compared to conventional intrusion detection methods in terms of both accuracy and explainability. The EED method exhibits high accuracy in accurately identifying and classifying attacks while providing transparency and interpretability.

Multi-criteria ABC classification is a useful model for automatic inventory management and optimization. This model enables a rapid classification of inventory items into three groups, having varying managerial levels. Several methods, based on different criteria and principles, were proposed to build the ABC classes. However, existing ABC classification methods operate as black-box AI processes that only provide assignments of the items to the different ABC classes without providing further managerial explanations. The multi-criteria nature of the inventory classification problem makes the utilization and the interpretation of item classes difficult, without further information. Decision makers usually need additional information regarding important characteristics that were crucial in determining the managerial classes of the items because such information can help managers better understand the inventory groups and make inventory management decisions more transparent. To address this issue, we propose a two-phased explainable approach based on eXplainable Artificial Intelligence (XAI) capabilities. The proposed approach provides both local and global explanations of the built ABC classes at the item and class levels, respectively. Application of the proposed approach in inventory classification of a firm, specialized in retail sales, demonstrated its effectiveness in generating accurate and interpretable ABC classes. Assignments of the items to the different ABC classes were well-explained based on the item’s criteria. The results in this particular application have shown a significant impact of the sales, profit, and customer priority as criteria that had an impact on determining the item classes.

Multi-criteria ABC classification is an effective technique that allows rapid and automatic organization of a growing number of inventory items into classes having different managerial levels. These built classes help decision-makers efficiently control the inventory and optimize the whole supply chain. However, existing ABC classification methods work as black-box processes that produce ABC classes without providing any explanations behind the assignment of the items. Given the multi-criteria nature of the ABC classification problem, managers cannot easily analyze and interpret the items’ managerial classes. Another problem of existing methods is their inability to follow the Pareto principle which states that items must be Pareto distributed over the ABC classes. To solve these problems, we propose a two-phase semi-supervised explainable approach based on semi-supervised clustering and explainable artificial intelligence. The first phase integrates an intelligent initialization and a constrained clustering process that guides the classification process to lead to Pareto distributed items while the second phase aims to build detailed micro and macro explanations of the ABC managerial classes at the item and the class levels. Application of the proposed approach for the automatic classification of chemical products of a distribution company has shown the effectiveness of the proposed approach in providing accurate, transparent and well-explained ABC classes.

Big data clustering is one of the recently challenging tasks that is used in many application domains. Traditional clustering methods are not able to deal with large-scale of data. Furthermore, Big data are often characterized by the mixed type of data, including numerical and categorical attributes. Thus, we propose in this paper the parallelization of k-prototypes clustering method (MR-KP) using MapReduce model to handle large-scale of mixed data. Experiments results show that MR-KP scales well with increasing data set sizes and achieves a close to linear speedup while maintaining the clustering accuracy.