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Efforts should then be focused on early detection to contain and quickly mitigate the threats before they manage to cause any substantial damage.Even today's most stealth malware, if it's controlled remotely, needs an active network communication for reporting back to the attacker.Results of our research constitute part of a working intrusion detection system that consumes real network traffic from more than 5 million users per day.We show how these methods can be used to learn from well known malware samples, generalise the behaviour and consequently find novel threats.This framework based on modules and libraries that can be used all together in different combos to get exactly what researcher/tester needs.The malware landscape is characterised by its rapid and constant evolution.Given such a model, we show how to construct, either manually or automatically, a grammar describing the set of possible attacks which are then tested against the obtained model for the firewall.Moreover, if our system fails to find an attack, a regular expression model of the firewall is generated for further analysis.

We also elaborate on how the found infections would have been otherwise missed using traditional detection tools.

Machine learning give us the algorithms to analyse network data in order to find specific types of behaviour.

The challenge is how to use this technology to detect what matters most: malicious behaviours that pose a high risk to companies.

CANToolz is an open-source framework for working with CAN bus.

In this presentation we will demonstrate use-cases and examples of black-box analyses of CAN network and ECU devices.

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This activity gives us a competitive visibility advantage.