DEEP LEARNING NETWORK INTRUSION DETECTION

One or more computer processors intercept one or more network inputs entering or existing an internal network; synthesize one or more network input images from a random noise vector sampled from a normal distribution of textually embedded network inputs utilizing a trained generative adversarial network; classify one or more synthesized network input images by identifying contained objects utilizing a trained convolutional neural network with rectified linear units, wherein the objects include patterns, sequences, trends, and signatures; predict a security profile of the one or more classified network input images and associated one or more network inputs, wherein the security profiles includes a set of rules and associated mitigation actions, analogous historical network traffic, a probability of infection, a probability of signature match with historical malicious network inputs, and a harm factor; apply one or more mitigation actions based on the predicted security profile.