VMware, Inc.
GENERATIVE ADVERSARIAL NETWORK BASED PREDICTIVE MODEL FOR COLLABORATIVE INTRUSION DETECTION SYSTEMS

Abstract:

Described herein are embodiments for transferring knowledge of intrusion signatures derived from a number of software-defined data centers (SDDCs), each of which has an intrusion detection system (IDS) with a convolutional neural network (CNN) to a centralized neural network. The centralized neural network is implemented as a generative adversarial neural network (GANN) having a multi-feed discriminator and a generator, which is trained from the discriminator. Knowledge in the GANN is then transferred back to the CNNs in each of the SDDCs. In this manner, each CNN obtains the learning of the CNNs in nearby IDSs of a region so that a distributed attack on each of the CNNs, such as a denial of service attack, can be defended by each of the CNNs.