Smart Internet of Things (IoT) devices, from home IP cameras to network-attached storage (NAS) drives, are increasingly hijacked and repurposed as proxies for financial crimes, according to a new study.
The 17-month investigation led by Howard University’s Danda B. Rawat traced compromised devices across darknet markets, underground forums, and Telegram channels, where they were used for illegal money transfers, cryptocurrency theft, and credit card fraud.
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“Our 17-month study uncovers a harsh truth: smart IoT devices are cybercriminals’ go-to weapons, hijacked as proxies for financial crimes, with smart IP cameras and NAS drives topping the hit list,” said Rawat, associate dean for research and graduate studies at Howard University’s College of Engineering and Architecture.
The research – the first sufficient study to map the scale of such exploitation – found that financial institutions emerged as prime targets, with attacks posing risks of significant monetary losses, operational disruption, and breaches of confidential data.
Rawat’s team—including Howard graduate student researchers Yuba Siwakoti and Manish Bhurtel, and industry experts Adam Oest and RC Johnson—recommends frequent network monitoring, vulnerability scanning, and exposure checks using tools like Shodan, alongside deploying anti-financial crime tools at the institutional level.
Rawat is founding director of the Howard University Data Science & Cybersecurity Center, the DoD Center of Excellence in Artificial Intelligence & Machine Learning, and the Cyber-security and Wireless Networking Innovations Research Lab. Over his career, he has secured more than $110 million in research funding as principal investigator, published over 350 scientific articles and 11 books, and supervised 35 doctoral graduates.
The study, IP Camera Can Be Abused for Payments: A Study of IoT Exploitation for Financial Services Leveraging Shodan and Criminal Infrastructures, published in IEEE Transactions on Consumer Electronics, used standardized data from known vulnerabilities, public research datasets, and Shodan exposure metrics.
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