Summary
This chapter introduced you to the topic of fraud as it applies to ML. The key takeaway from this chapter is that fraud involves deception for some type of gain. Often, this deception is completely hidden and subtle; sometimes, the gain is even hard to decipher unless you know how the gain is used. Fraud affects ML security by introducing flawed data into the dataset, which produces unreliable or unpredictable results that are skewed to the perpetrator’s goals. In addition, because the data is unreliable, it also presents a security risk.
When reviewing the security needs of an organization, it’s important to consider both background and real-time fraud. Depending on your organization, one form of fraud or the other may take precedence. For example, a marketing company with no direct consumer interaction would need to consider background fraud more strongly. Likewise, an online seller would need to consider real-time fraud more strongly. Tailoring the type...
