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Crypto Malicious

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Submitted By jsmally00
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Stopping Malicious Behavior
What is the problem?
Can the field of fraud detection (and cyber security in general) be improved by new technology and approaches?
If companies develop a program that searches for unusual activity by looking at risk factors then they could improve how they detect fraud. Since a lot of fraud detection is rule based, they have to develop a system that addresses the gray areas of their rules. For instance if a bank is looking for someone who transfers over $10,000 for one transaction in a day as an alert, then what happens if they separate the $10,000 into smaller payments? What happens if the malicious person makes sure to deposit just under $10,000 to avoid having alerts for their illegal actions? Addressing different patterns of new gray flavors of activity can help address management issues, overlapping issues and detection issues.
Also developing better anomaly, link and predictive analysis can help guide fraud detection into greater improvements. Anomaly analysis consists of being able to identify risk using certain behaviors when someone doesn’t do things the normal way. The main issue with anomaly detection is there is a great challenge on managing the detection systems, certain behavior that you think is odd may be normal to a certain group of people, and that it is hard to make generalizations on new data.
Link analysis develops relationships between different kinds of entities. Links can be made with how entities relate to each other, certain factors that make something what it is, and what a malicious person’s actions is likely to be. Lastly, predictive analysis is where you look for patterns or relationships that help you define future events. Most fraud detectors use decision trees or ensemble methods to classify the different results (Daren Zha, 2010). The issue with predictive analysis is that your model

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