期刊
2019 GLOBAL IOT SUMMIT (GIOTS)
卷 -, 期 -, 页码 -出版社
IEEE
DOI: 10.1109/giots.2019.8766388
关键词
IoT edge; botnet attack detection; sparse representation; reconstruction error threshold; small-sized data
资金
- European Union Horizon's 2020 research and innovation programme [780315]
- H2020 Societal Challenges Programme [780315] Funding Source: H2020 Societal Challenges Programme
Internet-of-Things (IoT) aims at interconnecting thousands or millions of smart objects/devices in a seamless way by sensing, processing and analyzing huge amount of data obtained from heterogeneous IoT devices. This rapid development of IoT-oriented infrastructures comes at the cost of increased security threats through IoT-based botnet attacks. In this work, we present an IoT botnet attack detection method based on a sparsity representation framework using a reconstruction error thresholding rule for identifying malicious network traffic at the IoT edge coming from compromised IoT devices. The botnet attack detection is performed based on small-sized benign IoT network traffic data, and thus we have no prior knowledge about malicious IoT traffic data. We present our results on a real IoT-based network dataset and show the efficacy of our proposed technique against a reconstruction error-based autoencoder approach.
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