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.
DBSec 2021: Data and Applications Security and Privacy XXXV
July 19-20, 2021
35TH Annual WG 11.3 Conference on Data and Applications Security and Privacy (DBSEC'21)
, Singhal, A.
, Sun, X.
and Liu, P.
Deep Learning for Detecting Network Attacks: An End to End approach, DBSec 2021: Data and Applications Security and Privacy XXXV, Virtual, US, [online], https://doi.org/10.1007/978-3-030-81242-3_13, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=930878
(Accessed December 11, 2023)