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Applicants should possess at least two years of experience (work or academic) in the field of software security, like performing software security audits. Ability to engage with product teams independently with minimal supervision is a must. Applicants should have expertise in two or more of the following areas:
- Code review, analysis and vulnerability assessment (C, C++, ARM assembly,C#,Java)
- Security testing, e.g. fuzzing, instrumentation, monitoring
- Operating system security
- Mobile platform security
- Mitigation techniques
- Incident response
In-depth knowledge in the following specific areas will be considered a plus:
- Mobile operating system internals (Android, Windows Phone, iOS)
- Static analysis
- Firmware analysis and reverse engineering
- ARM architecture
Graduate degree in a security related field of Computer Science or Mathematics is a plus.
If interested, please apply directly to requisition G1889943
** 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
* 2 Ph.D. Students in Computer Security
* 1 Ph.D. Student in Socio-Technical Aspects of Security
Duration: 3 years (extension up to 4 years in total is possible).
For more information see URLs:
* 1 Post-Doc in Socio-Technical Aspects of Security
Duration 2 years (extension up to 5 years is possible).
For more information and for application see URL:
All the positions are related to the CORE-FNR project \\\"Socio-Technical Analysis of Security and Trust\\\" (STAST). STAST will be highly interdisciplinary. It teams up the Interdisciplinary Centre for Reliability, Security and Trust (SnT), the Applied Security and Information Assurance (APSIA), led by Prof. P. Y. A. Ryan who is also the principal investigator of the project, and Security and Trust of Software Systems (SaToSS), led by Prof. S. Mauw, and the Educational Measurement and Applied Cognitive Science (EMACS), ref. Dr. V. Koenig.