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Hide Your Hackable Smart Home from Remote Attacks: The Multipath Onion IoT Gateways

  • Lei Yang
  • Chris Seasholtz
  • Bo Luo
  • Fengjun Li
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11098)

Abstract

The rapid expansion of IoT-enabled home automation is accompanied by substantial security and privacy risks. A large number of real-world security incidents exploiting various device vulnerabilities have been revealed. The Onion IoT gateways have been proposed to provide strong security protection for potentially vulnerable IoT devices by hiding them behind IoT gateways running the Tor hidden services, in which the gateways can only be accessed by authorized users with the .onion addresses of the gateways and correct credentials. However, the limited bandwidth of Tor makes this approach very impractical and unscalable. To tackle this issue, we present two novel designs of multipath Onion IoT gateway and split channel Onion IoT gateway. The first design implements a customized multipath routing protocol in Tor to construct a multi-circuit anonymous tunnel between the user and the Onion gateway to support applications that require low latency and high bandwidth. The second scheme splits command and data channels so that small-sized command packets are transmitted through the more secure channel over the Tor hidden service, while the less secure data channel over the public network is used for outbound very-high-bandwidth data traffic. Experiment results show that the proposed approaches significantly improve the performance of Onion IoT gateways, so that they can be practically adopted to securely transmit low-latency and high-bandwidth data, such as HD video streams from home surveillance cameras. We also prove the security guarantees of the proposed mechanism through security analysis.

Keywords

IoT security Smart homes Tor hidden service 

Notes

Acknowledgment

This work is sponsored in part by the National Security Agency (NSA) Science of Security Initiative and the US National Science Foundation under NSF CNS-1422206 and DGE-1565570.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Amazon LLC.SeattleUSA
  2. 2.The University of KansasLawrenceUSA

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