AI-Enabled Cybersecurity (AI4CS)

Co-organizers:

Mohammd Ashiqur Rahman
Director | Analytics for Cyber Defense (ACyD) Lab
Associate Professor | Dept of Electrical and Computer Engineering
Associate Professor | School of Computing and Information Sciences [Secondary Appointment]
Florida International University
Email: marahman (at) fiu.edu

Ehsan Aghaei
School of Computer Science
Carnegie Mellon University
Email: eaghaei (at) andrew.cmu.edu

As cyber threats continue to evolve at an unprecedented rate, the demand for innovative defense mechanisms grows stronger. Artificial intelligence (AI) presents a powerful ally in this battle, promising to learn and respond to cyber adversaries. As the cybersecurity domain is inundated with vast amounts of data and the need for real-time decision-making, it is particularly poised to benefit from AI’s capabilities. Machine learning algorithms, specifically deep learning techniques, excel in identifying intricate patterns within vast datasets and predicting potential threats. Natural language processing (NLP) models, like large language models (LLMs), contribute to context-aware analysis and response automation, especially in scenarios involving textual data. Additionally, Reinforcement Learning enables adaptive and evolving defense mechanisms by learning from real-time interactions with cyber threats. The convergence of these AI approaches fosters a dynamic synergy that fortifies cybersecurity with advanced, proactive measures.

This 2023 HONET Symposium on AI-Enabled Cybersecurity (AI4CS) invites experts, researchers, and practitioners from academia and industry to present their latest research, findings, and innovations in leveraging artificial intelligence to enhance cybersecurity.

Join us in this symposium to explore the frontier of AI-enabled cybersecurity, share knowledge, and shape the future of this exciting intersection of technologies.

Focus:

1. Machine learning models for intrusion detection
2. Deep learning in malware classification and analysis
3. AI-enhanced phishing detection techniques
4. Behavior analytics powered by neural networks.
5. Adversarial attacks against AI models in cybersecurity.
6. Natural Language Processing (NLP) for analyzing and predicting cyber threats.
7. Use of reinforcement learning in cyber defense strategies.
8. AI-based forensics and incident response.
9. Ethical considerations in AI-driven cybersecurity solutions.
10. Challenges and future directions in AI-enabled cybersecurity.
11. Data-driven defense measures
12. Automated Threat Mitigation and Patch Generation

Papers Submissions:

Papers that present original work, validated by experimentation, simulation, or analysis, testbeds, field-trials, or real deployments are welcome. All papers should be submitted at the HONET main website using the link below.
Paper Types: AI4CS will accept both (i) regular/full and (ii) short papers. A paper submitted as a full paper may be considered in the short paper category if it cannot be accepted as a full one.
Paper Length: Full papers can be up to 6 pages, and short papers up to 3 pages. The page length includes the bibliography and well-marks appendices.
Format: See authors instructions.
Originality: All submissions should be original, unpublished, and not under review elsewhere.
Review Process: Single-blind peer review.

Submit your paper.
Select topic "Symposium on AI-Enabled Cybersecurity (AI4CS)" to submit paper to this sympoisum.