%0 Conference Paper %1 8422832 %A Das, Rajshekhar %A Gadre, Akshay %A Zhang, Shanghang %A Kumar, Swarun %A Moura, Jose M. F. %B 2018 IEEE International Conference on Communications (ICC) %D 2018 %K authentication iot %P 1-6 %R 10.1109/ICC.2018.8422832 %T A Deep Learning Approach to IoT Authentication %U https://ieeexplore.ieee.org/abstract/document/8422832 %X At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.

@inproceedings{8422832, abstract = {At its peak, the Internet-of-Things will largely be composed of low-power devices with wireless radios attached. Yet, secure authentication of these devices amidst adversaries with much higher power and computational capability remains a challenge, even for advanced cryptographic and wireless security protocols. For instance, a high-power software radio could simply replay chunks of signals from a low-power device to emulate it. This paper presents a deep-learning classifier that learns hardware imperfections of low-power radios that are challenging to emulate, even for high- power adversaries. We build an LSTM framework, specifically sensitive to signal imperfections that persist over long durations. Experimental results from a testbed of 30 low-power nodes demonstrate high resilience to advanced software radio adversaries.}, added-at = {2022-08-15T02:17:38.000+0200}, author = {Das, Rajshekhar and Gadre, Akshay and Zhang, Shanghang and Kumar, Swarun and Moura, Jose M. F.}, biburl = {https://www.bibsonomy.org/bibtex/2071f19360fdd6d9ad094570a50f7dc23/danilofukuoka}, booktitle = {2018 IEEE International Conference on Communications (ICC)}, description = {A Deep Learning Approach to IoT Authentication | IEEE Conference Publication | IEEE Xplore}, doi = {10.1109/ICC.2018.8422832}, interhash = {ec8593ca14a14686baf3eab1582e52b4}, intrahash = {071f19360fdd6d9ad094570a50f7dc23}, issn = {1938-1883}, keywords = {authentication iot}, month = may, pages = {1-6}, timestamp = {2022-08-15T02:17:38.000+0200}, title = {A Deep Learning Approach to IoT Authentication}, url = {https://ieeexplore.ieee.org/abstract/document/8422832}, year = 2018 }