PPT-Adversarial Evasion-Resilient Hardware Malware Detectors

Nael AbuGhazaleh Joint work with Khaled Khasawneh Dmitry Ponomarev and Lei Yu Malware is Everywhere Malware is Everywhere Over 250000 malware registered every day Hardware Malware Detectors HMDs. 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. : . The Evolution of Evasive Malware . Giovanni Vigna. Department of Computer Science. University of California Santa Barbara. http://. www.cs.ucsb.edu. /~. vigna. Lastline, Inc.. http://. www.lastline.com. Antti. . Levomäki. , Christian . Jalio. , Olli-. Pekka. . Niemi. . 28 October 2009. Intrusion Prevention Systems should protect vulnerable hosts from remote exploits. Exploits can apply multiple evasion method to bypass the detection of Intrusion Prevention Systems and break into the remote system . Malware Resistant by Design. Nathan Ide Chris Hallum. Principal Development . Lead Senior Product Manager. Microsoft . Corporation Microsoft Corporation. SIA309. Agenda. Securing the . Boot. Windows Editions and Form Factors. etc. Convnets. (optimize weights to predict bus). bus. Convnets. (optimize input to predict ostrich). ostrich. Work on Adversarial examples by . Goodfellow. et al. , . Szegedy. et. al., etc.. Generative Adversarial Networks (GAN) [. 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. Suman Jana and Vitaly Shmatikov. The University of Texas at Austin. All about sophisticated detection and evasion techniques. Polymorphism, metamorphism, obfuscation… . Modern malware research. All about sophisticated detection and evasion techniques. with . DroidRide. : And How Not To. Min Huang, Kai Bu, . Hanlin. Wang, . Kaiwen. Zhu. Zhejiang University. CyberC. 2016. Reviving Android Malware. with . DroidRide. : And How Not To. ?. Reviving Android Malware. Nets. İlke Çuğu 1881739. NIPS 2014 . Ian. . Goodfellow. et al.. At a . glance. (. http://www.kdnuggets.com/2017/01/generative-adversarial-networks-hot-topic-machine-learning.html. ). Idea. . Behind. —An Introduction. Binghui. Wang, Computer Engineering. Supervisor: Neil . Zhenqiang. Gong. 01/13/2017. Outline. Machine Learning (ML) . Adversarial . ML. Attack . Taxonomy. Capability. Adversarial Training . Meltem Ozsoy. *. , Caleb . Donovick. *. , . Iakov. . Gorelik. *. ,. Nael. Abu-. Ghazaleh. **. and Dmitry . Ponomarev. *. *. Binghamton University, . **. University of California, Riverside. HPCA 2015 - San Francisco, CA. Florian Tramèr. Stanford University, Google, ETHZ. ML suffers from . adversarial. . examples.. 2. 90% Tabby Cat. 100% Guacamole. Adversarial noise. Robust classification is . hard! . 3. Clean. Adversarial (. Dr. Alex Vakanski. Lecture 6. GANs for Adversarial Machine Learning. Lecture Outline. Mohamed Hassan presentation. Introduction to Generative Adversarial Networks (GANs). Jeffrey Wyrick presentation. 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.