Keim, Jens (2022)
Discriminating if a network flow could have been created from a given sequence of network packets.
Technische Universität Darmstadt
doi: 10.26083/tuprints-00020630
Bachelorarbeit, Erstveröffentlichung, Verlagsversion

Kurzbeschreibung (Abstract)

This thesis aims to design a neural network (NN), that is capable of discriminating if a network flow could have been created based on a sequence of packets and can be used as a discriminative network (DN) for a Generative Adversarial Network (GAN) in future work.

For this, we first determined the features of network flows and packets alike, which are relevant to this task. We then created a dataset by extracting the relevant features from well-known network traffic datasets from the field of network intrusion detection (NID), as well as falsifying said datapoints to provide negative samples. We also provide a pipeline for the process of creating such datasets.

For our NN model we compared available architectures of recurrent neural networks (RNNs): simple RNN (simpleRNN), Long Short Term Memory (LSTM), and Gated Recurrent Units (GRUs). Furthermore our model uses a special kind of RNN called a conditional RNN (condRNN), which already has provided good results for a mixture of conditional and sequential input in the field of image region classification. This is necessary as a flow is the conditional counterpart to a sequence of packets. We aim to test the effectiveness of the different RNN architectures in regards to our problem and in the context of condRNNs.

Frage zum Eintrag

Redaktionelle Details anzeigen