IoT devices such as wearables, voice assistants, and home appliances are becoming an integral part of our lives. However, these devices still represent a security and privacy risk, with large-scale coordinated attacks often populating the news. For example, the Mirai botnet successfully compromised more than 100K IoT devices and engaged them in coordinated DDoS attacks over the last few years. One approach to securing IoT devices is to fingerprint them, i.e., identifying the device type, manufacturer, or event through different forms of traffic analysis. Ultimately, network administrators can use this information to quickly react to threats and/or take preventive measures. In this project, we leverage emerging machine learning techniques to protect IoT devices against security and privacy attacks. On one hand, we seek developing high-speed device fingerprinting appliances that can identify gadgets on-the-fly even on next-generation multi-hundred gigabit networks. On the other hand, we also consider protecting IoT devices against fingerprinting-based attacks from malicious actors (e.g., a man-in-the-middle) by carefully obfuscating their traffic patterns.
PoirIoT: Fingerprinting IoT Devices at Tbps Scale Carson Kuzniar, Israat Haque IEEE/ACM Transactions on Networking 2024
IoT Device Fingerprinting on Commodity Switches Carson Kuzniar, Miguel Neves, Vladimir Gurevich, Israat Haque IEEE/IFIP Network Operations and Management Symposium (NOMS) 2022
israat at dal dot ca
carson.kuzniar at dal dot ca
hn252486 at dal dot ca
mg478789 at dal dot ca
pl201920 at dal dot ca
wy875846 at dal dot ca