Temporal representation and event dating
Deep learning involves training an artificial neural network with many layers of simulated neurons using huge quantities of data.
The networks trained to recognize the characteristics of malicious code by looking at ten million of examples of malware and non-malware files, could offer a far better way to catch such malicious code.
Defenders often find themselves one step behind, resulting at best in monetary losses and in most extreme cases even endangering human lives.
Corporations with the unique challenges they face, must assume that sooner or later malware infections will get through their security perimeter.
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.
We select high-quality app features data with only a little size, and use innovative normalization preprocessing, unique activation function and advanced multilayer artificial neural network to recognize the unknown malware variants and defense zero-day attacks.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.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.