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- W3048151017 abstract "The ubiquity of the Internet of Things (IoT) can cause devastating distributed denial-of-service (DDoS) attacks. In order to thwart IoT-enabled DDoS attacks, network operators can deploy an anomaly detection system that detects and mitigates the DDoS traffic near the source of the attack. Unfortunately, anomaly detection systems face conflicting real-world deployment challenges in IoT networks. Namely, many IoT network operators cannot deploy systems that require manual parameter tuning or labeling, but without such system maintenance, previously proposed anomaly detection systems display far too many false positives for acceptable deployment in IoT networks. To exacerbate this problem, pre-trained machine learning models often fail to accurately classify traffic when ported to a new environment (i.e., domain shift), and must either retrain with labeled data painstakingly collected from the newly deployed network or exhibit poor accuracy.In order to overcome these IoT deployment challenges, we propose a novel anomaly detection system, Rapid, that successfully detects IoT-enabled DDoS attacks but also meets the accuracy demands of IoT under domain shift with little to no system maintenance from IoT network operators. Rapid initially provides robust detection through advanced machine learning techniques such as Long Short Term Memory (LSTM), but further, Rapid introduces a novel active learning technique that interweaves closely with attack mitigation to automatically adapt to new network domains without manual system maintenance. Rapid operates in real-time, and even though we leverage neural network techniques that typically cannot explain their predictions, our system architecture can provide diagnostic insight into any detected attack." @default.
- W3048151017 created "2020-08-13" @default.
- W3048151017 creator A5027835055 @default.
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- W3048151017 date "2020-06-01" @default.
- W3048151017 modified "2023-10-01" @default.
- W3048151017 title "Rapid: Robust and Adaptive Detection of Distributed Denial-of-Service Traffic from the Internet of Things" @default.
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- W3048151017 doi "https://doi.org/10.1109/cns48642.2020.9162278" @default.
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