Detecting Exploit kits using Machine Learning

Jagannatha, P. (2016) Detecting Exploit kits using Machine Learning.

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Abstract:The job of a security analyst is often similar to the task of finding a needle in a haystack when the clock is ticking. It is not about having the knowledge of computing and networking on a level of how it functions, but it is about finding something that is present underneath the covers. A security analyst has to go through a lot of data manually to learn/identify about a security incident. However, it is not only time-consuming, but also difficult to perform a thorough analysis of large data manually. This work focuses on providing an efficient methodology for a two-layer detection scheme, which is not only lightweight but also effective in attack detection and clustering. This research project has developed a new approach for the automatic detection of a network security incident, namely Exploit kits in this work. This research uses machine learning to detect Exploit kits with the help of supervised and unsupervised learning approach. The feature selection based on information gain allows data itself to explain the importance of individual features for the detection. This helps human experts avoid tedious manual feature selection.
Item Type:Essay (Master)
Clients:
FOX-IT, Delft, Netherlands
FOX-IT, Delft, Netherlands
FOX-IT, Delft, Netherlands
Faculty:EEMCS: Electrical Engineering, Mathematics and Computer Science
Subject:54 computer science
Programme:Computer Science MSc (60300)
Link to this item:http://purl.utwente.nl/essays/71416
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