Artificial intelligence (AI), machine learning (ML), and data mining (DM) are related to a number of emerging security and privacy problems. Firstly, AI algorithms are part of critical infrastructures, such as electrical grids, road networks and healthcare applications. ML is increasingly important for autonomous real-time analysis and decision-making in domains with a wealth of data. The use of learning methods in security sensitive domains creates new frontiers for security research, in which adversaries may attempt to mislead or evade intelligent machines. Additionally, ML and DM techniques create a wealth of privacy issues, due to the overabudance and accessibility of data. The 2014\ \textcolor{myblue}{\bf ACM Workshop on Artificial Intelligence and Security (AISec)} provides a venue for presenting and discussing new developments in this fusion of security/privacy with AI and machine learning.

We invite original research papers describing the use of AI or machine learning in security, privacy and related problems. We also invite position and open problem papers discussing the role of AI or machine learning in security and privacy. Submitted papers of these types may not substantially overlap papers that have been published previously or that are simultaneously submitted to a journal or conference/workshop proceedings. Finally we again welcome a ‘systematization of knowledge’ category of papers, which should distill the AI or machine learning contributions of a previously published series of security papers.

Paper format

This year we invite both original submissions and presentation-only papers. Please indicate the type of submission when submitting.

Original submissions: This include original research, systematization of knowledge, and open problem/position papers. They must be at most 10 pages in double-column ACM format (note: pages must be numbered) excluding the bibliography and well-marked appendices, and at most 12 pages overall. Committee members are not required to read the appendices, so the paper should be intelligible without them. Submissions need not be anonymized. We recommend the use of the ACM SIG Proceedings templates for submissions. The ACM format is the required template for the camera-ready version. Accepted papers will be published by the ACM Digital Library and/or ACM Press. To meet page numbering requirements, you may use the following modified "style file" for initial submissions (thanks to Battista Biggio): modified ACM template. Both research and open problem papers will undergo a thorough review process.
Presentation-only papers: As an experiment, this year we also invite presentation-only papers, for research currently under review elsewhere or published in 2014. These need not adhere to the ACM format and will not be published in the proceedings. They will undergo a light review for correctness, relevance and importance. Priority will be given to original submissions.
Submissions can be made through EasyChair using the following link: https://www.easychair.org/conferences/?conf=aisec2014

Topics of interest include, but are not limited to:

Theoretical topics related to security

• Adversarial Learning
• Robust Statistics
• Online Learning
• Learning in stochastic games
• Differential privacy

Security applications

• Computer Forensics
• Spam detection
• Phishing detection and prevention
• Botnet detection
• Intrusion detection and response
• Malware identification
• Authorship Identification
• Big data analytics for security

Security-related AI problems

• Distributed inference and decision making for security
• Secure multiparty computation and cryptographic approaches
• Privacy-preserving data mining
• Adaptive side-channel attacks
• Design and analysis of CAPTCHAs
• AI approaches to trust and reputation
• Vulnerability testing through intelligent probing (e.g. fuzzing)
• Content-driven security policy management & access control
• Techniques and methods for generating training and test sets
• Anomalous behavior detection (e.g. for the purposes of fraud prevention, authentication)