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dc.contributor.authorRaychowdhury, Arijit
dc.date.accessioned2019-10-31T02:39:24Z
dc.date.available2019-10-31T02:39:24Z
dc.date.issued2019-10-18
dc.identifier.urihttp://hdl.handle.net/1853/62008
dc.descriptionPresented on October 18, 2019 at 12:00 p.m in the Krone Engineered Biosystems Building, room 1005.en_US
dc.descriptionArijit Raychowdhury is currently a Professor in the School of Electrical and Computer Engineering at the Georgia Institute of Technology where he joined in January 2013. His research interests include low power digital and mixed-signal circuit design, design of power converters, and embedded hardware security.en_US
dc.descriptionRuntime: 59:01 minutesen_US
dc.description.abstractComputationally secure Cryptographic algorithms, when implemented on physical hardware leak correlated physical signatures (e.g. power supply current, electromagnetic radiation, acoustic, thermal) which could be utilized to break the crypto-engine in linear time. While the existence of such side-channel attacks have been known for decades, the impact of them have been increasing with the proliferation of billions of IoT edge-devices with resource constraints. In this talk I will discuss some of our recent work on profiled attacks that take advantage of the advances in Deep Neural Networks to break AES in a few iterations. In the second half of the talk, I will describe some of the embedded hardware techniques that can provide resiliency against such power side channel attacks.en_US
dc.format.extent59:01 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesCybersecurity Lecture Seriesen_US
dc.subjectDeep Neural Networksen_US
dc.subjectIoT edge-devicesen_US
dc.subjectSide-channel attacksen_US
dc.titleMachine Learning in Profiled Side-Channel Attacks and Low-Overhead Countermeasuresen_US
dc.typeLectureen_US
dc.typeVideoen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Information Security & Privacyen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Electrical and Computer Engineeringen_US


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