Detecting IoT malicious traffic based on autoencoder and convolutional neural network

Ren Hung Hwang, Min Chun Peng, Chien Wei Huang

研究成果: Conference contribution同行評審

11 引文 斯高帕斯(Scopus)

摘要

Due to the rise of the Internet of Things, a variety of devices have been made intelligent and connected to the Internet. However, the huge number of constantly connected but usually unattended IoT devices have made them one of the major sources of Interent attacks, e.g., a large-scale DDoS attack launching by millions of Mirai-injected compromised IoT devices in 2016. In order to mitigate DDoS attacks against IoT botnets, in this work, we proposed an effective malicious IoT traffic detection mechanism based on deep learning techniques. Specifically, we adopt convolutional neural network (CNN) to extract features of flows, then apply autoencoder to perform unsupervised malicious IoT traffic classification. Our goal is to be able to detect a malicious flow by examining as few of its packets as possible. To validate our proposed mechanism, we evaluate our model using both open data set from previous literature as well as the data set collected from a Mirai botnet we have built. Our experimental results show that the proposed mechanism is effective to detect malicious flows with near 100% accuracy, while only examining the first 2 packets of a flow.

原文English
主出版物標題2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings
發行者Institute of Electrical and Electronics Engineers Inc.
ISBN(電子)9781728109602
DOIs
出版狀態Published - 12月 2019
事件2019 IEEE Globecom Workshops, GC Wkshps 2019 - Waikoloa, United States
持續時間: 9 12月 201913 12月 2019

出版系列

名字2019 IEEE Globecom Workshops, GC Wkshps 2019 - Proceedings

Conference

Conference2019 IEEE Globecom Workshops, GC Wkshps 2019
國家/地區United States
城市Waikoloa
期間9/12/1913/12/19

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