Adversarial Training for Raw-Binary Malware Classifiers
Keane Lucas, Samruddhi Pai, Weiran Lin, Lujo Bauer, Michael K. Reiter, Mahmood Sharif. Adversarial Training for Raw-Binary Malware Classifiers. In Proc. USENIX Security '23. To appear.
CV
Downloadable PDF available here!
Education
- B.S. in Computer Science, United States Air Force Academy, 2014
- M.S. in Information Security, Carnegie Mellon University, 2016
- (in progress) M.S. in Machine Learning, Carnegie Mellon University
- (in progress) Ph.D in Electrical and Computer Engineering, Carnegie Mellon University
Highlighted Experience
- Student Researcher at Google AI
- Summer 2022
- Worked with Dr Nicholas Carlini on the challenges of evaluating randomized machine learning defenses. Pre-print of paper available here.
- Summer Intern at Massachusetts Institute of Technology Lincoln Laboratory
- Summer 2021
- Worked with Dr Ross Allen on an intrinsic augmentation to cooperative reinforcement learning that incentivizes accommodative behavior in agents, resulting in demonstrated zero-shot coordination. Paper published at AAMAS 2022 (click for more details).
- Deep Learning Researcher at Department of Defense (DoD) Research Directorate (RD)
- Spring 2017 to Fall 2019
- Customizes dense neural nets, recurrent neural nets, generative adversarial networks, and reinforcement learning networks to aid in solving Intelligence Community (IC) problems. Attended NeurIPS and other AI related conferences.
- Author on technical paper in final stages. The work has been presented at internal conferences and is being pushed into production as a pathfinding use case to justify deep learning analytics.
- Machine Learning Researcher at Center for Communications Research La Jolla
- Summer 2018
- Co-wrote 2 technical papers submitted for publication. Executed 100+ hours of experimentation with top university mathematicians to solve DoD’s hardest problems. Implemented experimental ML process derived from research
- Technical Project Lead at DoD Joint Artificial Intelligence Center
- Fall 2018 to Spring 2019
- Focused on applying ML algorithms for the Humanitarian Assistance and Disaster Relief National Mission Initiative. This is a particularly demanding job that requires constant mission justification to external non-technical organizations and rapid objective pivoting in a highly uncertain environment.
Publications
Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems
Clement Fung, Shreya Srinarasi, Keane Lucas, Hay Bryan Phee, Lujo Bauer. Perspectives from a Comprehensive Evaluation of Reconstruction-based Anomaly Detection in Industrial Control Systems. In Proc. ESORICS'22.
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
Weiran Lin, Keane Lucas, Lujo Bauer, Michael K. Reiter, Mahmood Sharif. Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks. In Proc. ICML'22.
Any-Play: an Intrinsic Augmentation for Zero-Shot Coordination
Keane Lucas, Ross E. Allen. Any-Play: an Intrinsic Augmentation for Zero-Shot Coordination. AAMAS'22.
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
Keane Lucas, Mahmood Sharif, Lujo Bauer, Michael K. Reiter, Saurabh Shintre. Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes. In Proc. AsiaCCS'21.
Presentations
Workshop Presentation: Adversarial Training with a Surrogate
Keane Lucas, Alec Jasen and Lujo Bauer. Adversarial Training with a Surrogate. NeurIPS NewInML Workshop. December 2020
Projects
Top Achievements
- 2019 National Defense Science and Engineering Graduate (NDSEG) Fellowship Awardee (166 awarded from 2928)
- Joint Commendation Medal – awarded August 2019
- 2018 DoD Research Directorate Military Performer of the Year, awarded Research Directorate and Math Research coins
- 2018 JASON Presenter – Conducted hour-long technical DoD AI presentation for the elite government advisory group
- 2016 Carnegie Mellon University Information Networking Institute Leadership Annual Award
- Undergraduate Cyber Training Top Graduate, Honor Graduate, and Major General Robert E. Sadler Award
- Honored by Alaska State Legislature (my home state) for ‘exemplary service with dignity and honor’ (2014)
- USAFA Instructor Letter (rare award given upon instructor agreement that this cadet should come back to teach)
- Honor Lists (7 x Dean’s and 4 x Commandant’s) / Dean’s: academic honor / Commandant’s: military honor
- 2010 Graduation High School Valedictorian in class size of 290
Skills
- Data Analysis/Security
- Introduction to Statistical Data Mining, Graduate Artificial Intelligence, Introduction to Machine Learning, Machine Learning Large Datasets, Search Engines, Cryptography, Secure Software Systems, Network Security
- Systems
- Software Reverse Engineering, Distributed Systems, Embedded Systems, Languages and Machines
- Languages
- Python, Java, LaTeX, C, Go, ARM, x86, Octave, Perl, C#, Objective C, SQL
Service and Leadership
- Deputy Flight Commander, 7th Intelligence Squadron, US Air Force
- Spring 2017 to Fall 2019
- Led a Flight consisting of 70+ airmen across 4+ directorates in research, capabilities, and IT support
- President, Graduate Organization Information Networking Institute, Carnegie Mellon University
- , Fall 2015 to Spring 2016
- Led graduate organization comprising ~200 students from ~15 countries and served as a dept strategic board member
- Commander, US Air Force Academy Cyber Defense Exercise
- Spring 2014
- Led an interdisciplinary team of 40 cadets to operate a secure network for the NSA Cyber Defense Competition
Other Experience
- Rapid Capability Scripter, Air Force Red Team (57th Information Aggressor Squadron), US Air Force
- Winter 2017/2018
- Delivered 28 new capabilities to simulate adversary behavior for large scale war games (RED FLAG)
- Cyber Security Intern, NASA Jet Propulsion Laboratory
- Summer 2015
- Found security bugs in Key System and built bare-metal to VM process for pen-testing and private cloud transition