Internship positions are temporary and available during three distinct sessions which are based on the academic semesters, with a duration of approximately 16 weeks per session. Future employment is not guaranteed and will be granted on a semester-by-semester basis by management. Must be authorized to work in the United States on a full-time basis for any employer.
Characterize complex, interconnected information security events using known scientific processes. Leverage approaches such as statistical mechanics and topological techniques to model systems, reduce data, and describe the time and spatial variance of a complex series of events, anomalies, etc.
Develop software to analyze large data sets. The large data sets are primarily composed of network data of hosts connected to University of Texas System networks. The purpose of the analysis will be to build statistical models that can be used to identify anomalies that can be associated with malicious activity on hosts connected to a monitored network. Research existing literature (white papers, research papers, journals, etc) for existing approaches related to 1 above. Use the knowledge gained from the research to design and implement automation described in 1 above. Document the design of any automation produced and the results of the analyses.
Other related functions as assigned.
Current student status or recent graduate (within 6 months) evidenced by admission to The University of Texas at Austin or another accredited institution of higher education. Knowledge of specialized equipment. Ability to demonstrate flexibility in acceptance of assignments and schedules; maintain professional behavior and appearance; exhibit dependability. Developed skills in prioritizing, organization, decision making, time management, and verbal/written communication skills based on past work or school experiences. Possess strong commitment to team environment dynamics with the ability to contribute expertise and follow leadership directives at appropriate times. Equivalent combination of relevant education and experience may be substituted as appropriate.
Coursework in probability and statistics. Coursework in Data Mining techniques. Experience with mathematical modeling (for example, using Matlab). Some programming experience, preferably using C, C++, or Java. Familiarity with statistics packages (e.g., R, Weka, etc). Familiarity with MapReduce techniques.
May work around standard office conditions Repetitive use of a keyboard at a workstation Use of manual dexterity