Page 148 - Cyber Defense eMagazine Annual RSA Edition for 2024

Secondly, through device compromise: If a user's device is compromised, either through hacking or
physical theft, perpetrators can gain access to sensitive banking information stored on the device,
enabling them to perpetrate fraud directly from it.

What are the driving forces behind the rapid proliferation of ODF?

Advancements in anti-fraud defenses by banks are continually met with corresponding developments in
fraudster tactics. Banks evolve their defenses during onboarding and device enrollment processes,
leveraging improved device intelligence to identify suspect devices associated with multiple accounts.
This technological arms race highlights the necessity for ongoing vigilance and adaptation.

Additionally, the accessibility of artificial intelligence (AI) has lowered the barrier for fraudsters to develop
targeted technology. The widespread availability of AI tools empowers fraudsters to devise sophisticated
schemes, thus exploiting vulnerabilities and circumventing traditional security measures. Consequently,
combating on-device fraud necessitates proactive strategies that keep pace with emerging threats.

Our threat hunting team has recently tracked down an extensive campaign aimed at propagating the
Copybara, a malware capable of perpetrating ODF, across important banks’ online customers.

So, how can organizations effectively combat the rising tide of on-device fraud?

A multifaceted approach is paramount. Conducting app and content integrity checks serves as a frontline
defense, helping detect any tampering of content transmitted by app servers. Similarly, device integrity
checks are vital to identifying unauthorized modifications, such as rooting or dangerous access rights
granted to third-party apps.

Other proactive measures include searching for known or existing malware to swiftly identify and
neutralize threats. However, given the dynamic nature of malware, businesses need tools to detect
anomalies indicative of compromise from "zero-day malware"—malware not yet classified.

Accurate behavioral profiling is crucial in this regard, enabling organizations to spot anomalies in user
behavior and spending patterns indicative of fraudulent activity. Additionally, predictive mule account
identification can hugely enhance security by preemptively flagging suspicious accounts and
transactions.

Lastly, leveraging data analytics and machine learning algorithms can be transformative, enabling
organizations to proactively identify and mitigate risks associated with on-device fraud.

The threat of on-device fraud underscores the critical need for robust security measures and proactive
strategies. As technology advances, so too must our defenses evolve to counter emerging threats. By
harnessing the latest advancements in AI, behavioral analytics, and predictive modeling, organizations
can stay ahead of fraudsters and protect both their assets and customers from harm.

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