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.
Infos pratiques
Prochains exposés
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Hardware-Software Co-Designs for Microarchitectural Security
Orateur : Lesly-Ann Daniel - EURECOM
Microarchitectural optimizations, such as caches and speculative out-of-order execution, are essential for achieving high performance. However, these same mechanisms also open the door to attacks that can undermine software-enforced security policies. The current gold standard for defending against such attacks is the constant-time programming discipline, which prohibits secret-dependent control[…]-
SoSysec
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Hardware/software co-design
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Micro-architectural vulnerabilities
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Should I trust or should I go? A deep dive into the (not so reliable) web PKI trust model
Orateur : Romain Laborde - University of Toulouse
The padlock shown in the URL bar of our favorite web browser indicates that we are connected using a secure HTTPS connection and providing some sense of security. Unfortunately, the reality is slightly more complex. The trust model of the underlying Web PKI is invalid, making TLS a colossus with feet of clay. In this talk, we will dive into the trust model of the web PKI ecosystem to understand[…]-
SoSysec
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Protocols
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Network
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