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

Liam Close, Rasha Kashef

Abstract: Artificial intelligence research in the stock market sector has been heavily geared towards stock price prediction rather than stock price manipulation. As online trading systems have increased the amount of high volume and re-al-time data transactions, the stock market has increased vulnerability to at-tacks. This paper aims to detect these attacks based on normal trade behavior using an Artificial Immune System (AIS) approach combined with one of four clustering algorithms. The AIS approach is inspired by its proven ability to handle time-series data and its ability to detect abnormal behavior while only being trained on regular trade behavior. These two main points are essential as the models need to adapt over time to adjust to normal trade behavior as it evolves, and due to confidentiality and data restrictions, real-world manipula-tions are not available for training. This paper discovers a competitive alterna-tive to the leading approach and investigates the effects of combining AIS with clustering algorithms; Kernel Density Estimation, Self-Organized Maps, Densi-ty-Based Spatial Clustering of Applications with Noise and Spectral clustering. The best performing solution achieves leading performance using common clustering metrics, including Area Under the Curve, False Alarm Rate, False Negative Rate, and Computation Time.
Keywords: models / stock / clustering algorithms / Artificial Immune System / normal trade behavior / adapt

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