  Over the last two decades, a lot of work has been done in improving network
security, particularly in intrusion detection systems (IDS) and anomaly
detection. Machine learning solutions have also been employed in IDSs to detect
known and plausible attacks in incoming traffic. Parameters such as packet
contents, sender IP and sender port, connection duration, etc. have been
previously used to train these machine learning models to learn to
differentiate genuine traffic from malicious ones. Generative Adversarial
Networks (GANs) have been significantly successful in detecting such anomalies,
mostly attributed to the adversarial training of the generator and
discriminator in an attempt to bypass each other and in turn increase their own
power and accuracy. However, in large networks having a wide variety of traffic
at possibly different regions of the network and susceptible to a large number
of potential attacks, training these GANs for a particular kind of anomaly may
make it oblivious to other anomalies and attacks. In addition, the dataset
required to train these models has to be made centrally available and publicly
accessible, posing the obvious question of privacy of the communications of the
respective participants of the network. The solution proposed in this work aims
at tackling the above two issues by using GANs in a federated architecture in
networks of such scale and capacity. In such a setting, different users of the
network will be able to train and customize a centrally available adversarial
model according to their own frequently faced conditions. Simultaneously, the
member users of the network will also able to gain from the experiences of the
other users in the network.
