Quantifiable Network Security Measurement: A Study Based on an Index System

Guoquan Li, Yulong Fu*, Zheng Yan, Weilin Hao

*Corresponding author for this work

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

Abstract

Security Metrics help network administrators master the security status and strengthen security management for many years. Recently, with the usages of many new techniques and network structures, the cyber attacks become complex and the security measurement has received more and more attentions. However, existing methods usually focus on one aspect of security and the indicators used are usually difficult to quantify, which makes it difficult to understand network security status in some real circumstance. In this paper, we consider the network system security from the perspective of attack and defense and the changes of external security environment to propose a comprehensive and quantifiable index system for network security measurement. We illustrate the corresponding theories and the usages of each selected indicators and we also complete the real-time security measurement in various attacks and defenses by using NS3 simulator. The simulation results verify the correctness and rationality of the proposed Security Measurement Index System.

Original languageEnglish
Title of host publicationMachine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings
EditorsXiaofeng Chen, Xinyi Huang, Jun Zhang
Pages47-62
Number of pages16
DOIs
Publication statusPublished - 1 Jan 2019
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Machine Learning for Cyber Security - Xi'an, China
Duration: 19 Sep 201921 Sep 2019
Conference number: 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11806 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Machine Learning for Cyber Security
Abbreviated titleML4CS
CountryChina
CityXi'an
Period19/09/201921/09/2019

Keywords

  • Attack and defense confrontation
  • Index system
  • NS3 simulation
  • Security metric

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