PPT-CS 502 Directed Studies: Adversarial Machine Learning

Dr Alex Vakanski Lecture 1 Introduction to Adversarial Machine Learning Lecture Outline Machine Learning ML Adversarial ML AML Adversarial examples Attack taxonomy Common adversarial attacks. huangintelcom Anthony D Joseph UC Berkeley adjcsberkeleyedu Blaine Nelson University of T57596bingen blainenelsonwsiiuni tuebingende Benjamin I P Rubinstein Microsoft Research benrubinsteinmicrosoftcom J D Tygar UC Berkeley tygarcsberkeleyedu ABSTRAC Machine: Adversarial Detection . of Malicious . Crowdsourcing Workers . Gang . Wang. , Tianyi Wang, Haitao . Zheng, Ben . Y. Zhao . UC Santa Barbara. gangw@cs.ucsb.edu. Machine Learning for Security. !!!. Cindy Bryant. LearnBop Director of Learning. cindy@learnbop.com. Agenda . Self-Directed Learning Fact or Fiction. Self-Directed Learner Characteristics. Benefits of Effective Self-Directed Learning. A teacher who . needs. to know about . self-directed learning!. A teacher who knows about . self-directed learning!. Resourcing and Facilitating Self-directed Learning. How to work . SMARTER. not HARDER. Statistical Relational AI. Daniel Lowd. University of Oregon. Outline. Why do we need adversarial modeling?. Because of the dream of AI. Because of current reality. Because of possible dangers. Our initial approach and results. Directed Mixed Graph Models. Ricardo Silva. Statistical Science/CSML, University . College London. ricardo@stats.ucl.ac.uk. Networks: Processes and Causality, Menorca 2012. Graphical Models. Graphs provide a language for describing independence constraints. —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . Adversarial examples. Ostrich!. Adversarial examples. Ostrich!. Intriguing properties of neural networks. . Christian . Szegedy. , . Wojciech. . Zaremba. , Ilya . Sutskever. , Joan Bruna, . Dumitru. Florian Tramèr. Intel, Santa Clara, CA. August 30. th. 2018. First they came for images…. The Deep Learning Revolution. The Deep Learning Revolution. And then everything else…. The ML Revolution. Deep Learning and Security Workshop 2017. Chang Liu. UC Berkeley. Deep Learning and Security is a trending topic in academia in 2017. Best Papers in Security Conferences. Towards Evaluating the Robustness of Neural Networks (Oakland 2017 Best Student Paper). Using Adult Education Strategies to Actively Cope with Chronic Illness. By Dr. Kristin . Brittain. & Dr. Valerie Bryan. Introduction. Due to the complexity of the health care system, patients are increasingly being asked to take more responsibility for their own self-care. . Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. Generative Adversarial Networks (GANs). Generative Adversarial Networks (GANs). Goodfellow. et al (2014) . https://arxiv.org/abs/1406.2661. Minimize distance between the distributions of real data and generated samples. Dr. Alex Vakanski. Lecture . 10. AML in . Cybersecurity – Part I:. Malware Detection and Classification. . Lecture Outline. Machine Learning in cybersecurity. Adversarial Machine Learning in cybersecurity.