Workshop on AI for Cyber Threat Intelligence (WAITI) 2024

December 9, 2024, Hawaii, USA
co-located with the Annual Computer Security Applications Conference (ACSAC24)

The AI for Cyber Threat Intelligence (WAITI) Workshop will be held on Monday, December 9, 2024, in conjunction with the Annual Computer Security Applications Conference (ACSAC). ACSAC will be held in Hawaii, USA. We have merged the IoT Security and Cyber Threat Intelligence (IoT-SCTI) Workshop with WAITI to cater to a wider audience.

Call for Papers

The cybersecurity landscape constantly shifts, throwing a never-ending data stream at security professionals. Valuable threat intelligence is hidden within this large stream, including text reported in social media, technical reports, and dark web forums. Traditionally, Cyber Threat Intelligence (CTI) relied on manual analysis or basic keyword matching, leading to bottlenecks and missed opportunities. Security analysts face limitations in the sheer volume of data, sophistication in tactics like code obfuscation and social engineering, and the rapid evolution of threats necessitates real-time processing to stay ahead of attackers. In today's digital landscape, where the volume and complexity of data continue to escalate, Natural Language Processing (NLP) techniques and large language models have emerged as indispensable tools for deciphering and mitigating cyber threats. NLP empowers machines to understand and process human language, offering significant benefits for CTI such as automated processing, advanced threat detection, and real-time analysis, allowing immediate threat identification and response. Thus, extracting and analyzing this information efficiently is critical for proactive defense strategies. This workshop explores the exciting potential of Artificial Intelligence/Generative AI to revolutionize cybersecurity, particularly CTI gathering and analysis. The workshops will provide a platform for researchers, practitioners, and enthusiasts to delve deeper into specialized topics related to NLP, Large Language Models (LLMs), and, more in general, artificial intelligence techniques in the context of cybersecurity and cyber threat intelligence.

We encourage original and high-quality contributions, preliminary work, and novel ideas on topics, including but not limited to:

• Information extraction in cyber threat intelligence
• Deep learning architectures for threat detection and analysis
• Visualization techniques for CTI
• Large Language Models for CTI
• Intelligence-driven threat-hunting
• Attribution
• Sharing of CTI
• Hunting and Tracking Adversaries
• Threat Quantification & Prioritization
• Explainable AI in Cybersecurity
• Dynamic Threat Adaptation with LLMs
• Multimodal Threat Intelligence Fusion
• LLMs for Malware Detection
• Bias Mitigation in LLMs for Cyber Threat Intelligence
• Federated Learning for Threat Detection
• LLMs for Social Media Threat Analysis
• LLMs and Visual Content
• Multimodal Large Language Models (MLLMs)
• Understanding Technical Language for CTI
• Cross-lingual Threat Intelligence using LLMs
• Misinformation Detection in CTI with LLMs
• LLM-powered Threat Scenario Generation
• Human-in-the-loop systems for LLM-based CTI
• Explainable Threat Intelligence Reports with LLMs
• Benchmarking LLM Performance
• Legal and Ethical Considerations for AI
• Zero Trust and CTI
• CTI in the IoT domain
• AI/GenAI for users’ behavior analysis and inference
• GenAI for mobility management and network control
• AI/GenAI within 6G networks
• Blockchain-based approaches for CTI
• Applying CTI/Case Studies
• CTI for IoT Systems
• Using IoT for sourcing CTI
• Network and host-based intrusion detection systems for IoT
• Web crawlers and scrapers for IoT threat information
• Cyber threat intelligence feeds focusing on IoT
• Static and dynamic malware analysis for IoT
• Machine learning and data mining techniques for IoT threat analysis
• IoT-specific threat actor profiling and attribution
• Domain generation algorithms analysis for IoT malware
• Identifying trends and patterns in IoT attacks and vulnerabilities
• Correlating IoT threat data from multiple sources
• Evaluating the reliability and accuracy of IoT CTI sources
• Addressing false positives and noise in IoT threat data
• Collaborative platforms for sharing and responding to IoT threats

• Paper Format: Use the IEEE template for your ACSAC paper, https://www.ieee.org/conferences/publishing/templates.html. For Latex, use \documentclass[conference,compsoc]{IEEEtran}.
• Anonymity: Submissions must be anonymous. Author names and affiliations should not be included. Authors can cite their work but must do so in the third person.
• Submission Website: https://cmt3.research.microsoft.com/WAITI2024/.

Publication

Accepted papers will be published by IEEE Computer Society Conference Publishing Services (CPS) and will appear in the Computer Society Digital Library and IEEE Xplore® in an ACSAC Workshops 2024 volume alongside the main ACSAC 2024 proceedings. An addtional small fee might be required.