HICSS-58 Mini-Track CFP
Collaborative AI, LLMs, & Cybersecurity - Cybersecurity in the Age of Artificial Intelligence, AI for Cybersecurity, and Cybersecurity for AI

Track: Collaboration Systems and Technologies


Submission Deadline: June 15th, 2024 (Submission Link Now Open)
Submission Link: https://hicss-submissions.org/submissions/new


Topics and research areas can include, but are not limited to:
+ Novel applications of Artificial Intelligence, Machine Learning, LLMs, and Deep Learning in Cybersecurity as they pertain to multi-user/multi-organizational collaborative domains and/or systems.
+ Adversarial AI Applications in Cybersecurity that collaboratively span organizations or apply to collaborative systems (i.e., malware, phishing, LLMs, or any applicable threat/identification domain).
+ Protecting AI that is used collaboratively (i.e., LLMs, 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 apply to the overarching theme of this mini-track.

Examples:
+ Modern LLM’s: Results Integrity, Prompt Security, Prompt Attach Detection, Result Error Detection, Dangerous Output Detection, Hallucination Detection, Prompt Jailbreaking.
+ Cybersecurity Domain Data Analytics: Leveraging AI to analyze any of the myriad datasets in the cybersecurity domain such as log files, network traffic, data at rest, etc., for legitimate cybersecurity purposes.
+ Vulnerability Assessment: Scanning Code for Vulnerabilities using AI / LLMS; Tracking and identifying / labeling code, containers, or repositories based on their vulnerabilities and/or vulnerability persistence over time and forks.
+ Secure Coding: Securing existing code or automatically generating new secure code either from scratch or by generating secure code clones.
+ Remediation: Effectively and efficiently identifying appropriate remediations for detected vulnerabilities from the large amounts of existing data.
+ Model Security for AI and LLM Models: Identifying models that have been perturbed, perturbing models to create model perturbation detection technologies, detecting the effect of model perturbations, identifying bias in models, identifying errors in models, removing perturbations from models.
+ Security for AI and LLM Datasets: Insuring distributed dataset integrity, detecting perturbations in datasets, identifying the effects of dataset perturbations, removing perturbations from datasets.
+ Attack Detection: Analyzing real-time data streams to identify immediate attacks as they occur.

Name, affiliation, and contact information of Mini-Track Chairs:
Hsinchun Chen
UA Regents' Professor of MIS
Management Information Systems
University of Arizona
hsinchun@email.arizona.edu

Mark Patton (Primary Contact)
Lecturer Management Information Systems
University of Arizona
mpatton@email.arizon.edu
o: 520-626-8614
m: 520-250-4763
McClelland Hall 430
1130 E. Helen Street
Tucson Arizona 85721

Sagar Samtani
Assistant Professor and Weimer Faculty Fellow
Department of Operations and Decision Technologies
Indiana University
ssamtani@iu.edu

Hongyi Zhu
Assistant Professor
Department of Information Systems and Cyber Security
Alvarez College of Business
University of Texas at San Antonio
hongyi.zhu@utsa.edu