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

Guoquan Li; Yulong Fu; Zheng Yan; Weilin Hao

doi:10.1007/978-3-030-30619-9_5

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

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

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Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

1 Sitaatiot (Scopus)

Abstrakti

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.

Alkuperäiskieli	Englanti
Otsikko	Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings
Toimittajat	Xiaofeng Chen, Xinyi Huang, Jun Zhang
Kustantaja	Springer
Sivut	47-62
Sivumäärä	16
ISBN (painettu)	9783030306182
DOI - pysyväislinkit	https://doi.org/10.1007/978-3-030-30619-9_5
Tila	Julkaistu - 1 tammik. 2019
OKM-julkaisutyyppi	A4 Artikkeli konferenssijulkaisussa
Tapahtuma	International Conference on Machine Learning for Cyber Security - Xi'an, Kiina Kesto: 19 syysk. 2019 → 21 syysk. 2019 Konferenssinumero: 2

Julkaisusarja

Nimi	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Vuosikerta	11806 LNCS
ISSN (painettu)	0302-9743
ISSN (elektroninen)	1611-3349

Conference

Conference	International Conference on Machine Learning for Cyber Security
Lyhennettä	ML4CS
Maa/Alue	Kiina
Kaupunki	Xi'an
Ajanjakso	19/09/2019 → 21/09/2019

Rahoitus

National Key R&D Program of China (grant 2016YFB0800700), NSFC (grants 61602359 and 11571281), Fundamental Research Funds for the Central Universities (JB150115) and the 111 project (grant B16037).

Pääsy asiakirjaan

10.1007/978-3-030-30619-9_5

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Link to publication in Scopus

Siteeraa tätä

Li, G., Fu, Y., Yan, Z., & Hao, W. (2019). Quantifiable Network Security Measurement: A Study Based on an Index System. teoksessa X. Chen, X. Huang, & J. Zhang (Toimittajat), Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings (Sivut 47-62). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 11806 LNCS). Springer. https://doi.org/10.1007/978-3-030-30619-9_5

Li, Guoquan ; Fu, Yulong ; Yan, Zheng et al. / Quantifiable Network Security Measurement: A Study Based on an Index System. Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings. Toimittaja / Xiaofeng Chen ; Xinyi Huang ; Jun Zhang. Springer, 2019. Sivut 47-62 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{167972eb88f948ab940e86b32233d0af,

title = "Quantifiable Network Security Measurement: A Study Based on an Index System",

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.",

keywords = "Attack and defense confrontation, Index system, NS3 simulation, Security metric",

author = "Guoquan Li and Yulong Fu and Zheng Yan and Weilin Hao",

year = "2019",

month = jan,

day = "1",

doi = "10.1007/978-3-030-30619-9\_5",

language = "English",

isbn = "9783030306182",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "47--62",

editor = "Xiaofeng Chen and Xinyi Huang and Jun Zhang",

booktitle = "Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings",

address = "Germany",

note = "International Conference on Machine Learning for Cyber Security, ML4CS ; Conference date: 19-09-2019 Through 21-09-2019",

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Li, G, Fu, Y, Yan, Z & Hao, W 2019, Quantifiable Network Security Measurement: A Study Based on an Index System. julkaisussa X Chen, X Huang & J Zhang (toim), Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vuosikerta 11806 LNCS, Springer, Sivut 47-62, International Conference on Machine Learning for Cyber Security, Xi'an, Kiina, 19/09/2019. https://doi.org/10.1007/978-3-030-30619-9_5

Quantifiable Network Security Measurement: A Study Based on an Index System. / Li, Guoquan; Fu, Yulong; Yan, Zheng et al.
Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings. toim. / Xiaofeng Chen; Xinyi Huang; Jun Zhang. Springer, 2019. s. 47-62 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vuosikerta 11806 LNCS).

Tutkimustuotos: Artikkeli kirjassa/konferenssijulkaisussa › Conference article in proceedings › Scientific › vertaisarvioitu

TY - GEN

T1 - Quantifiable Network Security Measurement: A Study Based on an Index System

AU - Li, Guoquan

AU - Fu, Yulong

AU - Yan, Zheng

AU - Hao, Weilin

N1 - Conference code: 2

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

KW - Attack and defense confrontation

KW - Index system

KW - NS3 simulation

KW - Security metric

UR - https://www.scopus.com/pages/publications/85072855174

U2 - 10.1007/978-3-030-30619-9_5

DO - 10.1007/978-3-030-30619-9_5

M3 - Conference article in proceedings

AN - SCOPUS:85072855174

SN - 9783030306182

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 47

EP - 62

BT - Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings

A2 - Chen, Xiaofeng

A2 - Huang, Xinyi

A2 - Zhang, Jun

PB - Springer

T2 - International Conference on Machine Learning for Cyber Security

Y2 - 19 September 2019 through 21 September 2019

ER -

Li G, Fu Y, Yan Z, Hao W. Quantifiable Network Security Measurement: A Study Based on an Index System. julkaisussa Chen X, Huang X, Zhang J, toimittajat, Machine Learning for Cyber Security - 2nd International Conference, ML4CS 2019, Proceedings. Springer. 2019. s. 47-62. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-30619-9_5

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

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