A method for network security situation prediction based on LSTM

Wendi Zhang; Tian Bai; Fuqiang Sun; Tongmin Jiang

doi:10.3850/978-981-11-2724-3_0128-cd

A method for network security situation prediction based on LSTM

Wendi Zhang
, Tian Bai
, Fuqiang Sun^*
, Tongmin Jiang

^*Corresponding author for this work

School of Reliability and Systems Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

The network security situation awareness technology could improve the ability of the network security administrators to deal with the network threat events and reduce the occurrence of security events. One of the key technologies is how to improve the accuracy of the network security prediction. In this paper, based on deep learning theory, a novel security situation prediction method using long short-term memory (LSTM) network is proposed. Firstly, the network situation is extracted from the raw data set and the situation values are calculated using the hierarchical analysis method. Secondly, the LSTM network is utilized to model and predict the network security situation, which can better combine historical data information and reduce the impact of subjective consciousness on the results. Finally, a case study on the DARPA data set is conducted to demonstrate the effectiveness of the proposed method.

Original language	English
Title of host publication	Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019
Editors	Michael Beer, Enrico Zio
Publisher	Research Publishing Services
Pages	3936-3942
Number of pages	7
ISBN (Electronic)	9789811127243
DOIs	https://doi.org/10.3850/978-981-11-2724-3_0128-cd
State	Published - 2020
Event	29th European Safety and Reliability Conference, ESREL 2019 - Hannover, Germany Duration: 22 Sep 2019 → 26 Sep 2019

Publication series

Name	Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019

Conference

Conference	29th European Safety and Reliability Conference, ESREL 2019
Country/Territory	Germany
City	Hannover
Period	22/09/19 → 26/09/19

Keywords

Deep learning
Long short-term memory network
Network security
Particle swarm optimization
Situation evaluation
Situation prediction

Access to Document

10.3850/978-981-11-2724-3_0128-cd

Cite this

Zhang, W., Bai, T., Sun, F., & Jiang, T. (2020). A method for network security situation prediction based on LSTM. In M. Beer, & E. Zio (Eds.), Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019 (pp. 3936-3942). (Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019). Research Publishing Services. https://doi.org/10.3850/978-981-11-2724-3_0128-cd

@inproceedings{aae8f392a2d84439868b11acd3882e1e,

title = "A method for network security situation prediction based on LSTM",

abstract = "The network security situation awareness technology could improve the ability of the network security administrators to deal with the network threat events and reduce the occurrence of security events. One of the key technologies is how to improve the accuracy of the network security prediction. In this paper, based on deep learning theory, a novel security situation prediction method using long short-term memory (LSTM) network is proposed. Firstly, the network situation is extracted from the raw data set and the situation values are calculated using the hierarchical analysis method. Secondly, the LSTM network is utilized to model and predict the network security situation, which can better combine historical data information and reduce the impact of subjective consciousness on the results. Finally, a case study on the DARPA data set is conducted to demonstrate the effectiveness of the proposed method.",

keywords = "Deep learning, Long short-term memory network, Network security, Particle swarm optimization, Situation evaluation, Situation prediction",

author = "Wendi Zhang and Tian Bai and Fuqiang Sun and Tongmin Jiang",

note = "Publisher Copyright: {\textcopyright} 2019 European Safety and Reliability Association. Published by Research Publishing, Singapore.; 29th European Safety and Reliability Conference, ESREL 2019 ; Conference date: 22-09-2019 Through 26-09-2019",

year = "2020",

doi = "10.3850/978-981-11-2724-3\_0128-cd",

language = "英语",

series = "Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019",

publisher = "Research Publishing Services",

pages = "3936--3942",

editor = "Michael Beer and Enrico Zio",

booktitle = "Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019",

}

Zhang, W, Bai, T, Sun, F & Jiang, T 2020, A method for network security situation prediction based on LSTM. in M Beer & E Zio (eds), Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019, Research Publishing Services, pp. 3936-3942, 29th European Safety and Reliability Conference, ESREL 2019, Hannover, Germany, 22/09/19. https://doi.org/10.3850/978-981-11-2724-3_0128-cd

A method for network security situation prediction based on LSTM. / Zhang, Wendi; Bai, Tian; Sun, Fuqiang et al.
Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. ed. / Michael Beer; Enrico Zio. Research Publishing Services, 2020. p. 3936-3942 (Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A method for network security situation prediction based on LSTM

AU - Zhang, Wendi

AU - Bai, Tian

AU - Sun, Fuqiang

AU - Jiang, Tongmin

PY - 2020

Y1 - 2020

N2 - The network security situation awareness technology could improve the ability of the network security administrators to deal with the network threat events and reduce the occurrence of security events. One of the key technologies is how to improve the accuracy of the network security prediction. In this paper, based on deep learning theory, a novel security situation prediction method using long short-term memory (LSTM) network is proposed. Firstly, the network situation is extracted from the raw data set and the situation values are calculated using the hierarchical analysis method. Secondly, the LSTM network is utilized to model and predict the network security situation, which can better combine historical data information and reduce the impact of subjective consciousness on the results. Finally, a case study on the DARPA data set is conducted to demonstrate the effectiveness of the proposed method.

AB - The network security situation awareness technology could improve the ability of the network security administrators to deal with the network threat events and reduce the occurrence of security events. One of the key technologies is how to improve the accuracy of the network security prediction. In this paper, based on deep learning theory, a novel security situation prediction method using long short-term memory (LSTM) network is proposed. Firstly, the network situation is extracted from the raw data set and the situation values are calculated using the hierarchical analysis method. Secondly, the LSTM network is utilized to model and predict the network security situation, which can better combine historical data information and reduce the impact of subjective consciousness on the results. Finally, a case study on the DARPA data set is conducted to demonstrate the effectiveness of the proposed method.

KW - Deep learning

KW - Long short-term memory network

KW - Network security

KW - Particle swarm optimization

KW - Situation evaluation

KW - Situation prediction

UR - https://www.scopus.com/pages/publications/85089193122

U2 - 10.3850/978-981-11-2724-3_0128-cd

DO - 10.3850/978-981-11-2724-3_0128-cd

M3 - 会议稿件

AN - SCOPUS:85089193122

T3 - Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019

SP - 3936

EP - 3942

BT - Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019

A2 - Beer, Michael

A2 - Zio, Enrico

PB - Research Publishing Services

T2 - 29th European Safety and Reliability Conference, ESREL 2019

Y2 - 22 September 2019 through 26 September 2019

ER -

Zhang W, Bai T, Sun F, Jiang T. A method for network security situation prediction based on LSTM. In Beer M, Zio E, editors, Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019. Research Publishing Services. 2020. p. 3936-3942. (Proceedings of the 29th European Safety and Reliability Conference, ESREL 2019). doi: 10.3850/978-981-11-2724-3_0128-cd

A method for network security situation prediction based on LSTM

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this