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AI Enhanced Automatic Response System for Resisting Network Threats

  • Song Xia
  • , Meikang Qiu
  • , Meiqin Liu
  • , Ming Zhong
  • , Hui Zhao
  • Wuhan University
  • Shenzhen University
  • Harrisburg University of Science and Technology
  • Zhejiang University
  • Henan University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

Network threats are malicious attacks that endanger network security. With terabits of information stored in the network and much of this information being confidential, cyber security turns to be very important. Most network protection mechanisms are based on firewall and Intrusion Detection System (IDS). However, with the diversification of cyber-attacks, traditional defense mechanisms cannot fully guarantee the security of the network. In this paper, we propose an automatic network threat response system based on machine learning and deep learning. It comprises three sub-modules: threat detection module, threat identification module and threat mitigation module. The experimental results show that the proposed system can handle 22 types of network threats in the KDD99 dataset and the rate of successful response is over 97%, which is much better than the traditional ways.

Original languageEnglish
Title of host publicationSmart Computing and Communication - 4th International Conference, SmartCom 2019, Proceedings
EditorsMeikang Qiu
PublisherSpringer
Pages221-230
Number of pages10
ISBN (Print)9783030341381
DOIs
StatePublished - 2019
Externally publishedYes
Event4th International Conference on Smart Computing and Communications, SmartCom 2019 - Birmingham, United Kingdom
Duration: 11 Oct 201913 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11910 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference4th International Conference on Smart Computing and Communications, SmartCom 2019
Country/TerritoryUnited Kingdom
CityBirmingham
Period11/10/1913/10/19

Keywords

  • Artificial intelligence
  • Automatic response system
  • Cyber security
  • Threat detection
  • Threat identification
  • Threat mitigation

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