Towards more realistic honeypots with synthetic network traffic injection

Honeypots and honeynets need to be realistic to attract and convince attackers to reveal their techniques. Several work on realistic file systems, but realistic local network communication is still an open question. In this work, we propose to generate synthetic network traffic using generative machine learning techniques and inject it into the network. This presentation entails some recent, ongoing work on this subject.