Enhancing Dendritic Cell Algorithm by Integration with Multi-Layer Perceptron for Anomaly Detection

Anomaly detection is crucial in a variety of areas, with the Dendritic Cell Algorithm (DCA) being one of the most used artificial immune systems (AIS) and introduced for binary classification of data. Both traditional and current perspectives on classification in DCA have primarily been threshold-based methods. Such approaches are limited in important ways, including inflexibility, manual tuning, and not being context-aware. The latest improvements in literature have provided adaptive dynamic threshold mechanisms that allow the system to adjust the sensitivity of the threshold using some statistical data of real-time observations. Although this is progress, the systems proposed are still based on rules, which have traditionally struggled with the more complex, higher-dimensional and nonlinear nature of data. This is common in most complex anomaly detection tasks today. In this study, we propose an improved DCA-MLP framework that uses a Multi-layer Perceptron (MLP) classifier replacing the thresholding phase. The MLP allows the DCA to learn from data context adaptively through a context-sensitive learning mechanism that can also change with the data distribution as it evolves, eliminating the need to robotically calibrate based on static or heuristic thresholds. The framework was tested thoroughly on fourteen benchmark datasets, and performance was evaluated against standard DCA in terms of accuracy, sensitivity and specificity measures. The performance results revealed considerable enhancements in DCA-MLP’s performance: 12%–50% improvements in accuracy (increasing accuracy to 93%–99%), 46% improvements in sensitivity (sensitivity as 98%), and 39% improvements in specificity. This shows that DCA-MLP is better adaptable, with learning capacity and robustness - a paradigm shift away from thresholds or threshold-based systems to an intelligent self-adjusting anomaly detection classification scheme.