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dc.contributor.authorDowning, Evan
dc.date.accessioned2019-05-08T00:22:44Z
dc.date.available2019-05-08T00:22:44Z
dc.date.issued2019-04-16
dc.identifier.urihttp://hdl.handle.net/1853/61037
dc.descriptionPresented on April 16, 2019 at 3:30 p.m. in the Klaus Advanced Computing Building, Room 1116.en_US
dc.descriptionPresenter: Evan Downing is a Ph.D. student studying the intersection between malware/ intrusion detection, machine learning, and adversarial machine learning. He works under Dr. Wenke Lee in the Institute for Information Security & Privacy (IISP). His current projects study how well machine learning and deep learning algorithms perform in the presence of classical security attacks as well as explaining what these algorithms have learned after training them on large datasets.en_US
dc.descriptionJoint work with Shang-Tse Chen, Nilaksh Das, Jinho Jung, Carter Yagemann, Polo Chau, Taesoo Kim, Wenke Lee, and Le Song.en_US
dc.descriptionRuntime: 3:06 minutesen_US
dc.description.abstractMachine learning is at risk of being attacked. As companies continue to depend on machine learning to solve their problems, more sophisticated attacks are being created to undermine and take advantage of machine learning algorithms. Worse, these machine learning attacks can have adverse effects on our physical world, like forcing a self-driving car to run a stop sign. MLsploit is a framework designed to solve this problem by allowing operators to evaluate their trained machine learning models against a variety of attacks in order to strengthen them. MLsploit focuses not just on image, video, and audio data, but also contains information security datasets used to detect malware and defend against network intrusions. Using MLsploit, a company can evaluate machine learning models trained on a number of different datasets which provide valuable services to themselves and to their customers.en_US
dc.format.extent3:06 minutes
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.relation.ispartofseriesCybersecurity Demo Day 2019en_US
dc.subjectCloud-based securityen_US
dc.titleMLsploit [Judges Remarks]en_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 Computer Scienceen_US


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