Mini-Track Description:
Cybersecurity and Artificial Intelligence (AI) are key domains whose intersection gives great promises and poses significant threats. The nature of AI and Cybersecurity encompasses many domains. While some perspectives are narrowly focused (e.g., point solutions inside an organization identifying threats in a network stream), many are very sweeping and are either collaborative or tackle collaborative domains (e.g., identifying intentional or unintentional cybersecurity threats propagating across collaboration platforms). Indeed, enhancing our capabilities in AI for cybersecurity has been noted as a key national priority by significant entities such as the National Science Foundation, the National Science Technology Council, and the National Academies of Science.
Implementing AI and Cybersecurity can also be internal to an organization or broadly collaborative (e.g., organizations working and competing together in adversarial AI research). Conversely, cybersecurity for AI has point solutions internal to organizations and broadly collaborative domains (e.g., collaboratively protecting from adversarial examples in shared data sets or shared models with multi-organizational transfer learning). However, the range and scope of how AI could be used for cybersecurity and how to improve the cybersecurity of AI remain relatively understudied yet critically important areas.
This minitrack seeks to focus on AI and Cybersecurity that works in broader domains, collaborative inter-organizational realms, shared collaborative domains, or with collaborative technologies. The threats being addressed with and/or to AI are intended to be sweeping in nature and of significant societal impact. Broadly, the topics and research areas include, but are not limited to:
- Novel applications of Artificial Intelligence, Machine Learning, and Deep Learning in Cybersecurity as it pertains to multi-user/multi-organizational collaborative domains and/or systems
- Adversarial AI/Machine Learning Applications in Cybersecurity that collaboratively span organizations or apply to collaborative systems (i.e., malware, phishing, or any applicable threat/identification domain)
- Protecting AI that is used collaboratively (i.e., shared data sets, shared models, shared applications) or spans collaborative domains from cybersecurity threats (i.e., adversarial examples, trojans, model inversion)
- Using AI to protect AI in any appropriate wide-reaching setting
- Novel Collaboration approaches to leveraging and protecting AI in the cybersecurity domain
- Sharing/disseminating tools, techniques, and applications of AI in Cybersecurity and Cybersecurity for AI that applies to the overarching theme of this minitrack
Mini-Track Chairs:
- Dr. Hsinchun Chen, University of Arizona
- Dr. Mark Patton, University of Arizona
- Dr. Sagar Samtani, Indiana University
- Dr. Hongyi Zhu, University of Texas, San Antonio
Submission Links:
- Papers will be submitted to the HICSS-57 Paper submission link no later than June 15th.
Submission Format:
- HICSS format (i.e., double column, single spaced, no more than 10 pages including references, tables, etc.). Please visit the HICSS author's link for detailed information (https://hicss.hawaii.edu/authors/).
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Hongyi Zhu
Assistant Professor
The University of Texas at San Antonio
San Antonio TX
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