LSTM models can identify IoT device types from packet sizes and timing with 81-99% accuracy even when traffic is merged by NAT or VPN.
Network Traffic Anomaly Detection
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abstract
This paper presents a tutorial for network anomaly detection, focusing on non-signature-based approaches. Network traffic anomalies are unusual and significant changes in the traffic of a network. Networks play an important role in today's social and economic infrastructures. The security of the network becomes crucial, and network traffic anomaly detection constitutes an important part of network security. In this paper, we present three major approaches to non-signature-based network detection: PCA-based, sketch-based, and signal-analysis-based. In addition, we introduce a framework that subsumes the three approaches and a scheme for network anomaly extraction. We believe network anomaly detection will become more important in the future because of the increasing importance of network security.
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Your Smart Home Can't Keep a Secret: Towards Automated Fingerprinting of IoT Traffic with Neural Networks
LSTM models can identify IoT device types from packet sizes and timing with 81-99% accuracy even when traffic is merged by NAT or VPN.