PREUNN: Protocol Reverse Engineering using Neural Networks
Valentin Kiechle, Matthias Börsig, Sven Nitzsche, Ingmar Baumgart, Jürgen Becker · 2022
The ability of neural networks to universally approximate any function enables them to learn relationships between arbitrary kinds of data. This offers great potential in information security topics such as protocol reverse engineering (PRE), which has seen little usage of neural networks (NNs) so far. In this paper, we provide a novel approach for implementing PRE with solely NNs, demonstrating a simple yet effective reverse engineering of text-based protocols. This approach is modular by design and allows for the exchange of neural network models at any step with better performing models. The architectures used include a convolutional neural network (CNN), an autoencoder (AE), a generative adversarial net (GAN), a long short-term memory (LSTM), and a self-organizing map (SOM). All of these models combine for a new protocol reverse engineering approach. The results show that the widespread application layer protocols HTTP and FTP can successfully be mimicked by artificial intelligen