Deep Learning for Detecting Network Attacks: An End-to-End Approach

Qingtian Zou, Anoop Singhal, Xiaoyan Sun, Peng Liu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 traffic 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 flaws within the network protocols. Our findings 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.

Original languageEnglish (US)
Title of host publicationData and Applications Security and Privacy XXXV - 35th Annual IFIP WG 11.3 Conference, DBSec 2021, Proceedings
EditorsKen Barker, Kambiz Ghazinour
PublisherSpringer Science and Business Media Deutschland GmbH
Pages221-234
Number of pages14
ISBN (Print)9783030812416
DOIs
StatePublished - 2021
Event35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2021 - Virtual, Online
Duration: Jul 19 2021Jul 20 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12840 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference35th Annual IFIP WG 11.3 Conference on Data and Applications Security and Privacy, DBSec 2021
CityVirtual, Online
Period7/19/217/20/21

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint

Dive into the research topics of 'Deep Learning for Detecting Network Attacks: An End-to-End Approach'. Together they form a unique fingerprint.

Cite this