AI4CS-SDM2021: AI/ML for Cybersecurity Virtual Virtual, NY, United States, April 29-May 1, 2021 |
Conference website | https://sites.google.com/view/ai4cs-sdm2021/organizers-and-participants |
Submission link | https://easychair.org/conferences/?conf=ai4cssdm2021 |
Abstract registration deadline | March 8, 2021 |
Submission deadline | March 8, 2021 |
Camera-Ready Deadline | April 15, 2021 |
The AI/ML for Cybersecurity Workshop is accepting short 4-page papers for presentation at the workshop. Topics of interest to the organizers include, but are by no means limited to the following.
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Challenges and opportunities in data mining for cybersecurity tasks.
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Autonomous and semi-autonomous reasoning/decision making and response for defensive cyber operations or other complex domains.
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Challenges and successes of automation (non-AI/ML) capabilities on intrusion detection systems (IDS).
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Challenges and benefits of augmenting cybersecurity operations, especially automated intrusion detection systems, with AI/ML capabilities
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Data engineering challenges in AI for cybersecurity or other complex environments.
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Novel AI/ML techniques for cyber threat discovery.
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The impact of AI/ML generated information on human decision-making (i.e. human-machine teaming effectiveness) in complex environments, such as cyber defense systems.
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Applications of realistic human cognitive behavioral modeling of complex and multifaceted problems to appropriate tasks in cybersecurity.
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Application of multi-agent and distributed/decentralized AI solutions for collaboration and competition to cyber operations
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Leveraging evolutionary and/or genetic-based AI algorithms for improved cyber defense (e.g., threat modeling, anomaly detection, automated/adaptive response)
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Exploring requirements for explainable AI/ML algorithms in cybersecurity domain.
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Automated inference from compact knowledge representations.
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Mitigations for adversarial attacks on AI/ML algorithms deployed for complex problems, such as cyber operations.
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Suitability of AI/ML for cybersecurity command and control for complicated tasks.
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Unique challenges in development and sharing of benchmark cyber data sets and cyber simulation/emulation environments for testing and validating AI/ML techniques
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Novel techniques in AI/ML applied to complex, real-world problems, such as cybersecurity, with large, highly correlated data .
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Explorations of transfer-ability of solutions, or lessons learned, between complex, open-world problemsin the cyber- and non-cyber domain.