HICSS-60 Minitrack CFP: AI for Cybersecurity
Track: Internet at Work and Play
Conference: Hawaii International Conference on System Sciences (HICSS-60)
Location: Hilton Waikoloa Village, Big Island, Hawaii
Conference Website: https://hicss.hawaii.edu
Conference Dates: January 5–8, 2027
Track Description: https://hicss.hawaii.edu/tracks-and-minitracks/internet-at-work-and-play/#ai-for-cyberseucirty-minitrack

=====Dates (11:59 pm Hawaii Standard Time)=====
Submission Deadline: June 15, 2025
Decision Notification: August 17, 2025
Final Manuscript Deadline: September 22, 2025
Registration Deadline: October 1, 2025

=====Mini-Track Introduction & Topics=====
Cybersecurity and AI are key domains whose intersection gives great promises and poses significant threats. 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 solicit papers that address, but are not limited to, the following areas.
+ Novel applications of AI, machine learning, GEN AI, LLMs, and deep learning in cybersecurity
+ Adversarial AI applications in cybersecurity, i.e., malware, phishing, AMG, LLMs, or any applicable threat/identification domain
+ Protecting AI, i.e., protecting Gen AI, LLMs, shared data sets, shared models, shared applications
+ Using AI to protect AI, AI applications, and the people using AI for work or play
+ The security and integrity of AI systems that people now engage with and trust with PII and intimate details of their work and lives both in the work setting and outside of work in their private lives or when at play
+ Novel approaches to leveraging and protecting emerging AI domains (agentic systems, multi-agentic systems, AI based swarm applications, AI based drones, AI based partners / collaborators / therapists / Other, etc.)
+ Sharing/disseminating tools, techniques, and applications of AI in cybersecurity and cybersecurity for AI

Examples of areas where AI is applied include:
+ Modern GenAI / LLMs: Results integrity, prompt security, prompt attach detection, result error detection, dangerous output detection, hallucination detection, prompt jailbreaking
+ Cybersecurity Domain Data Analytics:
∟----1. 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
∟----2. Analyzing real-time data streams to identify immediate attacks as they occur
+ Vulnerability Assessment:
∟----1. Scanning Code for Vulnerabilities using AI / LLMS
∟----2. 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 GenAI, AI, ML, and LLM Datasets: Insuring distributed dataset integrity, detecting perturbations in datasets, identifying the effects of dataset perturbations, removing perturbations from datasets
+ Detection of phishing, fraud, and attacks including short-term and long-term multimodal attacks such as vishing, voice cloning, audio deepfakes, video deep fakes, and all forms of AI enabled multi-turn interactive attacks spanning varying time horizons

=====Co-Chairs Information=====
Mark Patton (Primary Contact)
University of Arizona
mpatton@email.arizona.edu
o: 520-626-8614
m: 520-250-4763

Sagar Samtani
Indiana University
ssamtani@iu.edu

Hongyi Zhu
University of Texas at San Antonio
hongyi.zhu@utsa.edu

Hsinchun Chen
University of Arizona
hsinchun@email.arizona.edu