We illustrate the detection performance of each algorithm presenting real examples of malware detected by algorithms described in this work.
We also elaborate on how the found infections would have been otherwise missed using traditional detection tools.
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.
This talk will introduce our work on AI based Antivirus using deep learning.
We can control the false positive rate less than 0.05% and false negative rate less than 12%.
Defenders often find themselves one step behind, resulting at best in monetary losses and in most extreme cases even endangering human lives.