Jiahe Zhang
ABSTRACT
In recent years, there has been a significant surge in the deployment of Internet of Things (IoT) devices, which has consequently escalated security threats, notably Distributed Denial of Service (DDoS) attacks. Our prior research developed an LSTM-based framework for detecting futuristic DDoS attacks but largely relied on simulated datasets [1]. To bridge this gap, we designed a Raspberry Pi (RPi) testbed that mimics the complexities of large-scale IoT networks. This setup allows us to simulate realistic DDoS attacks originating from IoT devices and evaluate the effectiveness of various DDoS detection techniques. Specifically, using this RPi testbed, we validated the effectiveness of our LSTM-based framework in identifying futuristic DDoS attacks, observing an F1 score ranging between 0.8 and 0.86 depending on the aggressiveness of the DDoS attack.
- Arvin Hekmati, Nishant Jethwa, Eugenio Grippo, and Bhaskar Krishnamachari. 2023. Correlation-Aware Neural Networks for DDoS Attack Detection In IoT Systems. arXiv preprint arXiv:2302.07982 (2023).Google Scholar
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