During a review of the logs, a cybersecurity analyst notices that the data inputs recorded for a machine learning model used in fraud detection exhibit an unusual pattern that significantly deviates from the expected data format and ranges. The anomaly led to a drop in detection accuracy, and subsequent investigations pointed to external manipulation. What type of vulnerability is most likely being exploited in this scenario?
Data poisoning is a technique where an attacker introduces corrupt or malicious data into a system's data set to manipulate the behavior of a machine learning model, reduce its effectiveness, or cause it to make incorrect predictions. Reasonable answers might involve other data-related vulnerabilities, but they generally do not involve direct manipulation of a model's input data to degrade its performance as data poisoning does.
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What is data poisoning?
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How can organizations defend against data poisoning attacks?
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What are some signs of data poisoning in machine learning models?