IACR News item: 30 November 2011
Norwegian Information Security Laboratory, GUC
Job Posting** Main objective of the research is to overcome limitations of existing intrusion-detection systems (IDS), which are presently mainly based on expert knowledge or contemporary online learning. For IDSs, the continuous learning of new and changing attack patterns, and the use of relevant attributes or features that represent abnormal behaviour in network-traffic data is of greatest importance in order to detect hostile activities in dynamic network environments. Online-learning systems with an embedded online-feature selection have a great potential to assist in understanding the nature of network intrusions as well as to assist in establishing the ability to process massive amounts of data in large-scale networks. Specific objectives of the proposed research are two-fold:
- To develop new computational-intelligent methods for online-learning in malware and intrusion-detection systems that can deal with the challenges of massive data, obfuscation, adversarial activities, changing environments and the lack of a real-labeled reference data and training dataset, and
- To develop new embedded-online-feature-selection methods without prior knowledge or limited number of features (open-system system approach)
** Specific background and skills in one or more of the following areas is highly desirable:
- Excellent MSc degree in computer science/engineering, mathematics or statistics
- Experience in numerical analysis, algorithms and complexity analysis
- Knowledge in machine learning and pattern recognition
- Programming ability in one or more of the following languages: Matlab, Python, Java,C, C++, or C#
- Fluent in English: oral and written communication skills
- Ability to communicate technical concepts clearly and effectively
- Scientific publications in re
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