Deep Learning for Detecting Logic-flaw-exploiting Network Attacks: An End-to-end Approach

Published

August 25, 2022

Author(s)

Qingtian Zou, Anoop Singhal, Xiaoyan Sun, Peng Liu

Abstract

Network attacks have become a major security concern for organizations worldwide. A category of network attacks that exploit the logic (security) flaws of a few widely-deployed authentication protocols has been commonly observed in recent years. Such logic-flaw-exploiting network attacks often do not have distinguishing signatures, and can thus easily evade the typical signature-based network intrusion detection systems. Recently, researchers have applied neural networks to detect network attacks with network logs. 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 based on protocol fuzzing to automatically generate high-quality network data, on which deep learning models can be trained for network attack detection. Our findings show that protocol fuzzing can generate data samples that cover real-world data, and deep learning models trained with fuzzed data can successfully detect the logic-flaw-exploiting network attacks.

Citation

Journal of Computer Security

Volume

30

Pub Type

Journals

Download Paper

https://doi.org/10.3233/JCS-210101

Local Download

Keywords

Network Attack, Data Set, Protocol Fuzzing, Machine Learning

Citation

Zou, Q. , Singhal, A. , Sun, X. and Liu, P. (2022), Deep Learning for Detecting Logic-flaw-exploiting Network Attacks: An End-to-end Approach, Journal of Computer Security, [online], https://doi.org/10.3233/JCS-210101, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=933314 (Accessed October 14, 2025)