MSc/BSc theses - Naivasha Consortium - Google Sites

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The MBP graduate office will submit the completed to SGPS.


See to SGPS for approval.
(scroll down web page associated with this link until the MSc submission timelines chart is visible)

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Contact the secretariat to make an appointment with one of the academic staff members to discuss the research you are interested in and a rough planning. This consultation should be done at least 6 months in advance to ensure the actual start date of the research. Preferably, the thesis subjects should fit within the research fields (PhD candidates/staff) of the ANU group. Subjects are selected taking into account your interests. Once a subject has chosen, an ANU supervisor will be identified who is responsible for the coordination of the thesis research. Agreements made between you and your supervisor(s) regarding the subject and organisation of the research are documented in the Thesis Agreement. You are responsible to ensure that a signed contract is developed and handed in at the ANU secretariat.

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The thesis-based Master of Science (MSc) is a two-year, research and course-based degree. It is expected that applicants will have a four-year undergraduate BSc (or equivalent) from an accredited University with a major or specialization in Biochemistry, Microbiology, Cell Biology, Genetics, Biology, Chemistry, or related discipline, as judged appropriate by the Biochemistry Graduate Program. During the final two years of your undergraduate program, a minimum academic average of B+ (78%) as calculated by the Department is required.

MSc Thesis and Defense | Feinberg Graduate School


Adaptive Landscape - MSc thesis - SlideShare

Before Medical Biophysics MSc students proceed to the last requirement (thesis defense) for successful completion of their degrees, the following must be in place:

Msc thesis in networking | Lanchonete Sabor e Arte

Differential analysis of memory snapshots is not widely utilised or explored. The amount of data in memory makes it hard to efficiently search for malicious activity without deep insights in OS internals and memory forensics. Our proposed method reduces the waste amount of data by implementing a differential analysis module on top of the Volatility Framework. The method extracts relevant artefacts from memory such as loaded DLLs, registry changes and driver modules and compares the data of the two snapshots. Our experiments show that the data reduction method is quite efficient and makes is easier for the analyst to detect malicious activity. In this thesis we present a method that can detect malicious activity on a system by comparing the data from two memory-snapshots post and pre-infection. The work has a special emphasis on Remote Administration Tools that is often utilised in sophisticated attacks by advanced threat actors.