Multiclass anomaly detection for unsupervised and semi-supervised data based on a combination of negative selection and clonal selection algorithms

A key challenge in anomaly detection is the imbalance between the amounts of normal and abnormal signal data. Specifically, the amount of abnormal signal data is considerably less than that of normal signal data. To solve this problem, techniques for detecting abnormalities using only normal signal data derived from artificial immune systems (AISs) have been investigated. A representative example is the negative selection algorithm (NSA), which classifies data and detects anomalies using only normal signals through a process that mimics the underlying principle of vertebrate immunity. However, the NSA is optimized to detect only two classes of anomalies. Therefore, in this study, we developed a multiclass anomaly detection algorithm that hybridizes the principles of NSA and the clonal selection algorithm (CSA). We improved this algorithm using unsupervised and semi-supervised learning algorithms to conveniently detect anomalies at actual industrial sites. This paper presents a process for applying an AIS algorithm to anomaly detection using the evolution of data-based anomaly-detection algorithms. In particular, we leveraged the NSA principle of classification through semi-supervised learning to enable multiple classifications of unlabeled data. The obtained detector data formed clones optimized by the CSA and had constant memory, thus improving the classification accuracy and reducing run time. The proposed algorithm was validated using an intelligent maintenance system bearing dataset and a vacuum deposition equipment dataset.