У нас вы можете посмотреть бесплатно USENIX Security '23 - HorusEye: A Realtime IoT Malicious Traffic Detection Framework using... или скачать в максимальном доступном качестве, видео которое было загружено на ютуб. Для загрузки выберите вариант из формы ниже:
Если кнопки скачивания не
загрузились
НАЖМИТЕ ЗДЕСЬ или обновите страницу
Если возникают проблемы со скачиванием видео, пожалуйста напишите в поддержку по адресу внизу
страницы.
Спасибо за использование сервиса ClipSaver.ru
USENIX Security '23 - HorusEye: A Realtime IoT Malicious Traffic Detection Framework using Programmable Switches Yutao Dong, Tsinghua Shenzhen International Graduate School, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; Qing Li, Peng Cheng Laboratory, Shenzhen, China; Kaidong Wu and Ruoyu Li, Tsinghua Shenzhen International Graduate School, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; Dan Zhao, Peng Cheng Laboratory, Shenzhen, China; Gareth Tyson, Hong Kong University of Science and Technology (GZ), Guangzhou, China; Junkun Peng, Yong Jiang, and Shutao Xia, Tsinghua Shenzhen International Graduate School, Shenzhen, China; Peng Cheng Laboratory, Shenzhen, China; Mingwei Xu, Tsinghua University, Beijing, China The ever-growing volume of IoT traffic brings challenges to IoT anomaly detection systems. Existing anomaly detection systems perform all traffic detection on the control plane, which struggles to scale to the growing rates of traffic. In this paper, we propose HorusEye, a high throughput and accurate two-stage anomaly detection framework. In the first stage, preliminary burst-level anomaly detection is implemented on the data plane to exploit its high-throughput capability (e.g., 100Gbps). We design an algorithm that converts a trained iForest model into white list matching rules, and implement the first unsupervised model that can detect unseen attacks on the data plane. The suspicious traffic is then reported to the control plane for further investigation. To reduce the false-positive rate, the control plane carries out the second stage, where more thorough anomaly detection is performed over the reported suspicious traffic using flow-level features and a deep detection model. We implement a prototype of HorusEye and evaluate its performance through a comprehensive set of experiments. The experimental results illustrate that the data plane can detect 99% of the anomalies and offload 76% of the traffic from the control plane. Compared with the state-of-the-art schemes, our framework has superior throughput and detection performance. View the full USENIX Security '23 program at https://www.usenix.org/conference/use...