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We also introduce a training mechanism that allows to automate the learning process and improves the precision of the classifiers.We present unique algorithms that helps to solve different problems in each of the identified challenges.For example, the PCI standard for organizations handling credit card transactions dictates that any application facing the internet should be either protected by a WAF or successfully pass a code review process.Nevertheless, despite their popularity and importance, auditing web application firewalls remains a challenging and complex task.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.
Finally, we present Light Bulb, an open source python framework for auditing web applications firewalls using the techniques described above.
We think it's OK for production and it's already in production since Jan 2016.
Android malware can often evade anti-malware security software if the author changes a few lines of code or designs the program to automatically mutate before each new infection or add shell on their app.
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.
Our deep learning system has high precision (99.96%) and high recall (88%).