Abstract
Stock exchanges are marketplaces to buy and sell securities such as stocks, bonds and commodities. Due to their prominence, stock exchanges are prone to a variety of attacks which can be classified as external and internal attacks. Internal attacks aim to make profits by manipulation of trading processes e.g., Spoofing, Quote stuffing, Layering and others, which are the specific focus of this paper. Different types of proprietary fraudulent activity detectors are deployed by stock exchanges to analyze the time series data of trader’s activities or the activity of a particular stock to flag potentially malicious transactions while human analysts probe the flagged transactions further. The key issue faced here is that while the number of anomalous transactions identified can run into thousands or tens of thousands, the number of such transactions that can realistically be probed by human analysts would be a small fraction due to resource constraints. The issue therefore reduces to a dynamic resource allocation problem wherein alerts that represent the most malicious transactions need to be mapped to human analysts for further probing across different time intervals. To address this challenge, we encode the scenario as a Cooperative Target Observation (CTO) problem wherein the analysts (modeled as observers) perform a cooperative observation of alerts that represent potentially malicious activity (modeled as targets) and develop multiple solution approaches in order to identify malicious activity.
B. Kalra and S. K. Munnangi—Equal contribution.
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We would like to thank CognitiveScale for providing access to their data generation software for experimentation purposes and related discussions and for the generous support provided.
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Kalra, B., Munnangi, S.K., Majmundar, K., Manwani, N., Paruchuri, P. (2021). Cooperative Monitoring of Malicious Activity in Stock Exchanges. In: Gupta, M., Ramakrishnan, G. (eds) Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2021. Lecture Notes in Computer Science(), vol 12705. Springer, Cham. https://doi.org/10.1007/978-3-030-75015-2_13
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