Description
In this paper, a new approach for the detection of ransomware based on the runtime analysis of their behaviour is presented. The main idea is to get samples by using a mini-filter to intercept write requests, then decide if a sample corresponds to a benign or a malicious write request. To do so, in a learning phase, statistical models of structured file headers are built using Markov chains. Then in a detection phase, a maximum likelihood test is used to decide if a sample provided by a write request is normal or malicious. We introduce new statistical distances between two Markov chains, which are variants of the Kullback-Leibler divergence, which measure the efficiency of a maximum likelihood test to distinguish between two distributions given by Markov chains. This distance and extensive experiments are used to demonstrate the relevance of our method.
Practical infos
Next sessions
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The Design and Implementation of a Virtual Firmware Monitor
Speaker : Charly Castes - EPFL
Low level software is often granted high privilege, yet this need not be the case. Although vendor firmware plays a critical role in the operation and management of the machine, most of its functionality does not require unfettered access to security critical software and data. In this paper we demonstrate that vendor firmware can be safely and efficiently deprivileged, decoupling its[…]-
SoSysec
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Compartmentalization
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Operating system and virtualization
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