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Deep Learning for Detecting Network Attacks: An End to End approach



Qingtian Zou, Anoop Singhal, Xiaoyan Sun, Peng Liu


Network attack is still a major security concern for organizations worldwide. Recently, researchers have started to apply neural networks to detect network attacks by leveraging network traÿc data. However, public network data sets have major drawbacks such as limited data sample variations and unbalanced data with respect to malicious and benign samples. In this paper, we present a new end-to-end approach to automatically generate high-quality network data using protocol fuzzing, and train the deep learning models using the fuzzed data to detect the network attacks that exploit the logic faws within the network protocols. Our fndings show that fuzzing generates data samples that cover real-world data and deep learning models trained with fuzzed data can successfully detect real network attacks.
Proceedings Title
DBSec 2021: Data and Applications Security and Privacy XXXV
Conference Dates
July 19-20, 2021
Conference Location
Virtual, US
Conference Title
35TH Annual WG 11.3 Conference on Data and Applications Security and Privacy (DBSEC'21)


Network attack, Protocol fuzzing, Deep learning


Zou, Q. , Singhal, A. , Sun, X. and Liu, P. (2021), Deep Learning for Detecting Network Attacks: An End to End approach, DBSec 2021: Data and Applications Security and Privacy XXXV, Virtual, US, [online],, (Accessed June 17, 2024)


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Created July 19, 2021, Updated November 18, 2021