Demo Abstract- CUDDoS: Correlation-aware Ubiquitous Detection of DDoS in IoT Systems
Published in The 21th ACM Conference on Embedded Networked Sensor Systems (SenSys 2023), 2023
Recommended citation: Jiahe Zhang, Tamoghna Sarkar, Arvin Hekmati, Bhaskar Krishnamachari. 2023. Demo Abstract: CUDDoS - Correlation-aware Ubiquitous Detection of DDoS in IoT Systems. In The 21st ACM Conference on Embedded Networked Sensor Systems (SenSys ’23), November 12–17, 2023, Istanbul,Turkiye. ACM, New York, NY, USA, 2 pages. https://doi.org/10.1145/3625687.3628392
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.
This paper will be published in the proceedings of the conference, and the source code associated with this paper is available here.
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Note: This work corresponds to the project “ML Inferencing for DDoS Attack Detection in Heterogeneous & Constrained Environments”, here.