RESONANT is a fraud detection system that adapts ML models to outsmart attackers & improve security.

What is it about?

RESONANT is a smart system designed to tackle credit card fraud by staying one step ahead of scammers. Instead of relying on a single approach, it switches between different detection methods at the right time, making it harder for fraudsters to predict vulnerabilities or reverse engineer your defense system. This adaptive strategy improves accuracy and security, keeping fraud detection strong and effective for longer periods of time.

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Photo by Stephen Dawson on Unsplash

Photo by Stephen Dawson on Unsplash

Why is it important?

Unlike traditional methods that rely on a single, static approach, RESONANT uniquely adapts by switching between different detection methods at the right moments, making it much harder and more costly for fraudsters to predict or bypass. This work is especially timely as fraud tactics become more sophisticated, addressing the urgent need for flexible and proactive defenses that outsmart evolving threats. Most existing defenses rely on optimizing a single defense method/ model, which makes them vulnerable to a sophisticated attacker that spends the time and resources to explore the system looking for vulnerabilities, then launches quick and viscous attacks exploiting these vulnerabilities. However, by implementing RESONANT, the defender is able to confuse the attackers by switching machine learning detection models, and thus changing their vulnerabilities. This means that when the attacker thinks they found the vulnerability and tries to take advantage of it, they will only waste their resources and be detected by the defense model deployed by RESONANT.

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