MLHat @ KDD 2021

MLHat: The 2nd International Workshop on Deployable Machine Learning for Security Defense

15 August 2021 @ 1pm-5pm PT (Pacific Time) — Virtual Space
co-located with 26TH ACM SIGKDD Conference on Knowledge Discovery and Data Mining


Farinaz Koushanfar (UC San Diego)
Safe and Robust Machine Learning for Real Systems

We are at the CUSP of the fourth industrial revolution empowered by machine learning and application automation: seamlessly connecting people, data, and computing machines. Such intelligent technologies however bring numerous potential security vulnerabilities and threats that might severely compromise their safety. In this talk, I present our work in providing end-to-end solutions for practicable robust machine learning based upon co-design and optimization of ML, data, hardware and software. The goal is to characterize the potential ML attack surface, identify and address the nefarious threats in real-time, and devise novel solutions to tackle these problems. In particular, I will show how our work marks the first set of metrics, as well as real-time resource-efficient solutions for machine learning vulnerability characterization, adversarial attacks, and data poisoning. I conclude by briefly discussing the challenges and opportunities moving forward.

Farinaz Koushanfar is a professor and Henry Booker Faculty Scholar in the Electrical and Computer Engineering (ECE) department at University of California San Diego (UCSD), where she is the founding co-director of the UCSD Center for Machine Intelligence, Computing & Security (MICS). Prof. Koushanfar received her Ph.D. in Electrical Engineering and Computer Science as well as her M.A. in Statistics from UC Berkeley. Her research addresses several aspects of efficient computing and embedded systems, with a focus on system and device security, safe AI, privacy preserving computing, as well as real-time/energy-efficient AI under resource constraints, design automation and reconfigurable computing. Professor Koushanfar has received a number of awards and honors for her research, mentorship, teaching, and outreach activities including the Presidential Early Career Award for Scientists and Engineers (PECASE) from President Obama, the ACM SIGDA Outstanding New Faculty Award, Cisco IoT Security Grand Challenge Award, Qualcomm Innovation Award(s), MIT Technology Review TR-35, Young Faculty/CAREER Awards from NSF, DARPA, ONR and ARO, as well as a number of Best Paper Awards. Dr. Koushanfar is a fellow of the IEEE, and a fellow of the Kavli Foundation Frontiers of the National Academy of Sciences.


TimeScheduled Events
1:00 pm – 1:05 pm PTOpening remark
1:05 pm – 2:10 pm PT Keynote (Farinaz Koushanfar)
Safe and Robust Machine Learning for Real Systems
2:10 pm – 2:15 pm PTBreak
2:15 pm – 3:00 pm PTSession I
STAN: Synthetic Network Traffic Generation with Generative Neural Models
Shengzhe Xu, Manish Marwah, Martin Arlitt, Naren Ramakrishnan
Machine Learning for Fraud Detection in E-Commerce: A Research Agenda
Niek Tax, Kees Jan de Vries, Mathijs de Jong, Nikoleta Dosoula, Bram van den Akker, Jonathan Smith, Olivier Thuong,Lucas Bernardi
Few-Sample Named Entity Recognition for Security Vulnerability Reports by Fine-Tuning Pre-Trained Language Models
Guanqun Yang, Shay Dineen, Zhipeng Lin, Xueqing Liu
3:00 pm – 3:45 pm PTSession II
DexRay: A Simple, yet Effective Deep Learning Approach to Android Malware Detection based on Image Representation of Bytecode
Nadia Daoudi, Jordan Samhi, Abdoul Kader Kabore, Kevin Allix, Tegawendé Bissyandé, Jacques Klein
Attacks on Visualization-Based Malware Detection: Balancing Effectiveness and Executability
Jingyu Qian, Hadjer Benkraouda, Hung Quoc Tran, Berkay Kaplan
A Survey on Common Threats in npm and PyPi Registries
Berkay Kaplan, Jingyu Qian
3:45 pm – 3:50 pm PTBreak
3:50 pm – 4:50 pm PTPanel Session
Rajarshi Gupta (Amazon Web Services)
Ilya Mironov (Facebook)
Moderator: Ali Ahmadzadeh
4:50pm – 5:00 pm PTClosing remark
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