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Discriminating if a network flow could have been created from a given sequence of network packets

Keim, Jens (2022):
Discriminating if a network flow could have been created from a given sequence of network packets. (Publisher's Version)
Darmstadt, Technische Universität, DOI: 10.26083/tuprints-00020630,
[Bachelor Thesis]

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Item Type: Bachelor Thesis
Status: Publisher's Version
Title: Discriminating if a network flow could have been created from a given sequence of network packets
Language: English
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.

Place of Publication: Darmstadt
Collation: 71 Seiten
Classification DDC: 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik
Divisions: 20 Department of Computer Science > Telecooperation
Date Deposited: 09 May 2022 12:01
Last Modified: 29 Jul 2022 12:59
DOI: 10.26083/tuprints-00020630
Corresponding Links:
URN: urn:nbn:de:tuda-tuprints-206306
Referees: Mühlhäuser, Prof. Dr. Max ; Garcia Cordero, Dr. Carlos ; Wainakh, Aidmar
Date of oral examination: 11 September 2020
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/20630
PPN: 495511870
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