Burnwal, Shantanu Prasad
(2016)
Machine Learning Approaches to Cyber Security.
Masters thesis, Indian Institute of Technology Hyderabad.
Abstract
Cyber-security is used to identify cyber-attacks while they are acting on a computer or network
system to compromise security of the system. We discuss the concept of Hidden Markov Model
with the Large Deviation Theory approaches because now a days statistical anomaly detection with
Large Deviation theory approach have been used to find attack signatures in network traffic. We
present two different approaches to characterize traffic: a model-free approach and a model-based
approach. Model free approach is method of types based approach using Sanov’s theorem whereas
model based approach is HMM based approach uses Large deviation theory. We discuss how these
theories can be applied for anomaly detection from network traffic. We study their effectiveness in
anomaly detection. We will discuss how much these statistical methods affective on spatio-temporal
traffic data. We also discuss about how length of traffic data affect our Markov model. How our
estimated model is related with true but unknown model.
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