Combining Artificial Immune System and Clustering Analysis: A Stock Market Anomaly Detection Model

Abstract

<p>As online trading systems have increased the amount of high volume, real-time data transactions, the stock market continues to have increased vulnerability to attacks. The goal of this project is to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data as well as its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are important as the models need to be able to adapt over time to adjust to normal trade behavior as it evolves and also due to confidentiality and data restrictions, real-world manipulations are not available for training. This proposed research work discovers a competitive alternative to the leading AIS approach and investigates the effects of combining AIS with clustering algorithms including Kernel Density Estimation, Self-Organized Maps, Density-Based Spatial Clustering of Applications with Noise, Cluster-Based Local Outlier Factor, and Spectral clustering. The best performing solution achieves leading performance in terms of many common clustering metrics including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time. </p>