DYNAMICS 2020 : 2020 Workshop in DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security
Call For Papers
DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS) Workshop 2020 Call for Submissions
About the DYNAMICS Workshop
The 2020 DYnamic and Novel Advances in Machine Learning and Intelligent Cyber Security (DYNAMICS) Workshop will be held on Monday, December 7, 2020. The workshop will be co-located with the 2020 Annual Computer Security Applications Conference (ACSAC), held at the AT&T Hotel and Conference Center in Austin, Texas. Due to the evolving COVID situation, DYNAMICS will be held virtually.
Machine learning has become critical to the evolution and sustainability of cyber security. While the theoretical objectives and principles behind cyber security are still valid, traditional technologies that require humans in the loop to read log files, triage alerts, and harden devices are neither fast enough nor scalable enough to meet the demands of modern networks and attacks. While network traffic and devices have grown by orders of magnitude, the ability for operators to triage the resulting alerts has not.
The sophistication of threats has also increased substantially. Sophisticated zero-day attacks go undetected for months at a time. Attacks take place over extended periods, effectively outwaiting traditional intrusion detection technology. Worse, new attack tools and strategies can now be developed using adversarial machine learning techniques, requiring rapid co-evolution of defenses that match the speed and sophistication of machine learning- based offensive techniques.
This is intended to focus on novel applied and theoretical work that combines machine learning techniques such as reinforcement learning, adversarial machine learning, and deep learning with significant problems in cybersecurity. We consider both offensive and defensive applications of machine learning to security.
Technical Paper Submissions
The DYNAMICS Workshop invites submissions of original, previously unpublished technical papers, posters, and panels on research in machine learning and cybersecurity. Papers should be between 5 and 12 pages, and should use the 2020 ACM Proceedings Template: https://www.acm.org/publications/proceedings-template, using the [sigconf, anonymous] options. Submissions will be evaluated using a standard peer review process. While authors may wish to align their submissions with one of the suggested topics below, submissions on other topics related to the workshop theme are welcome. Papers should be submitted via email to email@example.com.
A DYNAMICS Forum is a 1-2 hour extended discussion on a specific topic of interest to the DYNAMICS machine learning and cyber security community. A forum may focus on a specific technical problem, a policy issue, a social concern, or another DYNAMICS-related topic that you believe should be explored in depth. The intent of the forum format is to bring together a community that will not only explore your topic deeply within the context of the DYNAMICS Workshop itself, but that will have the potential to persist and grow beyond the workshop, in order to develop collaborative solutions over the long term. A DYNAMICS Forum is led by one or more moderators, who facilitate a discussion with a fully engaged audience.
If you would like to submit a DYNAMICS Forum proposal, please send an abstract of no more than 2 pages to firstname.lastname@example.org. Please be sure to include your proposed topic, moderators, a statement of why your idea is relevant and important to the DYNAMICS community, proposed length, and a high-level outline with your major discussion topics. Your submission will be evaluated for inclusion in the workshop based on its relevance to the workshop theme, the quality of the submission, and the availability of space in the workshop schedule. While proposed topics may be aligned with one of the suggested topics below, submissions on other topics related to the workshop theme are welcome.
Panel submissions are invited on topics of interest to the DYNAMICS machine learning and cyber security community. To submit a panel, please send an abstract of up to 1 page to email@example.com. Please be sure to include your proposed topic, panelists, panel chair, affiliations, and position statements for each panelist. DYNAMICS panels follow the same format as those of the main ACSAC conference.
Extensive information about ACSAC’s panel requirements can be found at https://www.acsac.org/2020/submissions/panels/.
Poster submissions are invited on topics of interest to the DYNAMICS machine learning and cyber security community. Posters provide a way for workshop attendees to present early stage, ongoing research that is not yet ready for submission as a peer reviewed paper, Poster presenters also gain feedback from conference and workshop attendees, and spark discussion among conference and workshop participants. Poster dimensions can be up to 36×48 inches (91x122 cm). Poster abstracts are not peer reviewed. Accepted abstracts will be made available on the workshop website prior to the event, but they will not be included in the workshop proceedings. To submit a poster, submit an e-mail with a PDF of your draft poster to firstname.lastname@example.org. For questions on posters, please contact email@example.com.
A Lightning Talk is timed, 5-minute talk on a topic of interest to the DYNAMICS machine learning and cyber security community. While lightning talks may be given on works in progress, or other topics of relevance to the workshop, you can even use a lightning talk to ask a question, find a community of shared interest on a topic, engage people in an issue, ask a question, or solicit feedback! A good lightning talk is fast paced, engaging, and high energy, and can use any desired presentation format.
Note that although Lightning Talk abstracts are reviewed by the DYNAMICS Workshop committee for relevance, they are not peer reviewed, and will not appear in the workshop proceedings. To submit a Lightning Talk, please e-mail your proposed topic to firstname.lastname@example.org
Papers that have been accepted by the DYNAMICS workshop will be published in the workshop proceedings.
By submitting a paper, DYNAMICS Forum, poster, panel, or lightning talk to the DYNAMICS workshop, you agree that if your submission is accepted, one or more of the submission’s authors will present the final version of the submission at the workshop.
Technical paper submission deadline: Technical paper acceptance notification:
Final technical paper PDF submission deadline: DYNAMICS Forum submission deadline DYNAMICS Forum acceptance notification: Panels, posters, and lightning talks
October 16th, 2020 (11:59 PM Eastern Time) October 31st, 2020
November 13th, 2020 (11:59 PM Eastern Time) November 13th, 2020 (11:59 PM Eastern Time) Rolling acceptances through the deadline Submissions accepted through November 13th, 2020
Attacking and Defending Machine Learning-Based Systems, Models, and Data Sets
• Trojan attacks on machine learning models
• Attacking and defending ML supply chains
• Attacking and defending autonomous
systems and sensors
• Trustworthy ML-based systems
Data Generation and Preparation:
• Data generation and labeling for machine learning-based security
• Feature extraction, weighting, and validation
• Data set validity
• Standardized data sets and data generation environments for scientific algorithm comparison
Feature Finding and Event Analysis:
• Detection of malicious code and events in large data sets
• Attack detection
• Insider threat detection
• Zero-day attack detection
• Large-scale network data analysis using machine learning
• Detection of threats with evolving behaviors or implementations
Adversarial Machine Learning for Cybersecurity:
• Training environments for adversarial machine learning-based security
• Training data poisoning: Adversarial ML attacks on training data
• Adversarial ML-derived strategies for attacking and defending networks
• Adversarial ML for deception
• Training humans and non-humans using adversarial ML
Machine Learning-Based Defense and Response:
• Automated responses to attacks
• Machine learning for autonomous and resilient cyber defense
• Machine learning-based cyber deception
• Automatic detection of zero-day attacks
• Machine learning-based vulnerability analysis
• Machine learning-driven access controls, security policies, etc.
• Counter-machine learning techniques, such as data poisoning and deception
• Real-time threat detection, decision making, and response
• Deep learning for automated recognition of novel threats or threat implementations Machine Learning-Based Offensive Techniques
• Machine learning-driven cyber offense
• Adversarial machine learning for network attacks
• Trust of data sources
• What makes an analytic trustworthy?
• Trust of analytic behavior
• Analytic validation
• Attacking analytics
• Manipulating analytic results with deception
• Trusted Execution Environment (TEE)-based analytics
How to Contact the Workshop Organizers
If you have questions related to the workshop, please e-mail them to email@example.com.
2020 DYNAMICS Workshop Organizers
Dr. Michael Clifford, Toyota
Dr. Michael Collins, USC-ISI
Dr. Nidhi Rastogi, RPI
Dr. Matt Bishop, UC Davis
Sharmisha Dutta, RPI
Dr. Anthony Palladino, Boston Fusion
Junie Shen, UCI
Dr. Sanchari Das, IU Bloomington
Daniel Kats, Norton Lifelock
Dr. Sara Rampazzi, U. Mich
Dr. Christoph Hauser, USC-ISI
ACSAC Workshops Chair:
Harvey Rubinovitz, The MITRE Corporation