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.
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