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This activity gives us a competitive visibility advantage.Nowadays we have the computational power and mechanisms to process huge amounts of data.In this talk we address four key challenges related to automatic malware detection in the network traffic: how to detect malware changing its network behaviour over time (e.g.changing different parts of the URL), how to mitigate potential mislabeling of the training data and how to perform large scale multi-class detection.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.
Even today's most stealth malware, if it's controlled remotely, needs an active network communication for reporting back to the attacker.
Our deep learning system has high precision (99.96%) and high recall (88%).
Web Applications Firewalls (WAFs) are fundamental building blocks of modern application security.
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.
Finding attacks that bypass the firewall usually requires expert domain knowledge for a specific vulnerability class.