MLCCSA 2013 - Special Session on Machine Learning Challenges in Cyber Security Applications

The goal of this special sessionis to explore advances in the above-mentioned areas as they pertain to cyber security, to expose the broader machine learning community to problems arising in cyber security applications, to promote the use of advanced machine learning techniques to solve these pressing issues in cyber security, and to encourage a larger subset of machine learning practitioners to engage in coordinated efforts to solve some of these real world problems.
We will invite submissions of papers pertaining to the above areas and related topics. Papers that involve well-designed evaluation studies of machine learning on recent, real-world cyber data will be strongly encouraged.
Topics of interestinclude, but are not limited to, the following:
? Large-scale learning methods for security applications;
? Data fusion and data imputation techniques in cyber security;
? Privacy-preserving learning methods, e.g.,that can utilize sensitive attack capture examples;
? Publicly available cyber security datasets amenable to machine learning research;
? Ultra-low false positive, high-accuracy machine learning methods;
? Semi-supervised or unsupervised learning in cyber security to deal with limited ground truth;
? Learning methods designed to minimize the burden placed on human analysts;
? Learning methods that are not based on the IID assumption;
? Transfer learning for cyber security applications;
? Multi-distribution feature selection;
? Multi-view or multi-modal learning in cyber security applications;
? Machine learning studies on cyber security application topic areas, such as network/host intrusion detection, malware detection, data loss prevention, critical infrastructure attack detection, alert correlation/fusion, and automated response/mitigation.