Computational intelligence algorithms analysis for smart grid cyber security

Yong Wang; Da Ruan; Jianping Xu; Mi Wen; Liwen Deng

doi:10.1007/978-3-642-13498-2_11

Computational intelligence algorithms analysis for smart grid cyber security

Yong Wang
, Da Ruan
, Jianping Xu
, Mi Wen
, Liwen Deng

Research output › peer-review

Abstract

The cyber attack risks are threatening the smart grid security. Malicious worm could spread from meter to meter to take out power in a simulated attack. The North American Electric Reliability Corporation (NERC) has thus developed several iterations of cyber security standards. According to the NERC cyber standards CIP-002-2 requirements, in this paper, we present cyber security risk analysis using computational intelligence methods and review on core methods, such as in risk assessment HHM, IIM, RFRM algorithms, fault analysis FTA, ETA, FMEA, FMECA algorithms, fuzzy sets, intrusion detection systems, artificial neural networks and artificial immune systems. Through the analysis of the core computational intelligence algorithms used in the smart grid cyber security in power system network security lab, we clearly defined existing smart grid research challenges.

Original language	English
Title of host publication	Advances in Swarm Intelligence
Place of Publication	Heidelberg, Germany
Pages	77-84
Number of pages	8
Volume	2
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-13498-2_11
State	Published - 2010
Event	First International Conference in Swarm Intelligence - ICSI, Beijing Duration: 12 Jun 2010 → 15 Jun 2010

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6146 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	First International Conference in Swarm Intelligence
Country/Territory	China
City	Beijing
Period	2010-06-12 → 2010-06-15

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Affordable and clean energy

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-13498-2_11License: Unspecified

Cite this

Wang, Y., Ruan, D., Xu, J., Wen, M., & Deng, L. (2010). Computational intelligence algorithms analysis for smart grid cyber security. In Advances in Swarm Intelligence (PART 2 ed., Vol. 2, pp. 77-84). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6146 LNCS, No. PART 2).. https://doi.org/10.1007/978-3-642-13498-2_11

@inproceedings{97568e8d07e84d1bb4527f4bd2115b99,

title = "Computational intelligence algorithms analysis for smart grid cyber security",

abstract = "The cyber attack risks are threatening the smart grid security. Malicious worm could spread from meter to meter to take out power in a simulated attack. The North American Electric Reliability Corporation (NERC) has thus developed several iterations of cyber security standards. According to the NERC cyber standards CIP-002-2 requirements, in this paper, we present cyber security risk analysis using computational intelligence methods and review on core methods, such as in risk assessment HHM, IIM, RFRM algorithms, fault analysis FTA, ETA, FMEA, FMECA algorithms, fuzzy sets, intrusion detection systems, artificial neural networks and artificial immune systems. Through the analysis of the core computational intelligence algorithms used in the smart grid cyber security in power system network security lab, we clearly defined existing smart grid research challenges.",

keywords = "Smart Grid, Cyber Security, Computational Intelligence",

author = "Yong Wang and Da Ruan and Jianping Xu and Mi Wen and Liwen Deng",

note = "Score = 3; First International Conference in Swarm Intelligence ; Conference date: 12-06-2010 Through 15-06-2010",

year = "2010",

doi = "10.1007/978-3-642-13498-2\_11",

language = "English",

isbn = "3642134971",

volume = "2",

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

number = "PART 2",

pages = "77--84",

booktitle = "Advances in Swarm Intelligence",

edition = "PART 2",

}

Wang, Y, Ruan, D, Xu, J, Wen, M & Deng, L 2010, Computational intelligence algorithms analysis for smart grid cyber security. in Advances in Swarm Intelligence. PART 2 edn, vol. 2, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6146 LNCS, Heidelberg, Germany, pp. 77-84, First International Conference in Swarm Intelligence, Beijing, China, 2010-06-12. https://doi.org/10.1007/978-3-642-13498-2_11

Computational intelligence algorithms analysis for smart grid cyber security. / Wang, Yong; Ruan, Da; Xu, Jianping et al.
Advances in Swarm Intelligence. Vol. 2 PART 2. ed. Heidelberg, Germany, 2010. p. 77-84 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6146 LNCS, No. PART 2).

Research output › peer-review

TY - GEN

T1 - Computational intelligence algorithms analysis for smart grid cyber security

AU - Wang, Yong

AU - Ruan, Da

AU - Xu, Jianping

AU - Wen, Mi

AU - Deng, Liwen

N1 - Score = 3

PY - 2010

Y1 - 2010

N2 - The cyber attack risks are threatening the smart grid security. Malicious worm could spread from meter to meter to take out power in a simulated attack. The North American Electric Reliability Corporation (NERC) has thus developed several iterations of cyber security standards. According to the NERC cyber standards CIP-002-2 requirements, in this paper, we present cyber security risk analysis using computational intelligence methods and review on core methods, such as in risk assessment HHM, IIM, RFRM algorithms, fault analysis FTA, ETA, FMEA, FMECA algorithms, fuzzy sets, intrusion detection systems, artificial neural networks and artificial immune systems. Through the analysis of the core computational intelligence algorithms used in the smart grid cyber security in power system network security lab, we clearly defined existing smart grid research challenges.

AB - The cyber attack risks are threatening the smart grid security. Malicious worm could spread from meter to meter to take out power in a simulated attack. The North American Electric Reliability Corporation (NERC) has thus developed several iterations of cyber security standards. According to the NERC cyber standards CIP-002-2 requirements, in this paper, we present cyber security risk analysis using computational intelligence methods and review on core methods, such as in risk assessment HHM, IIM, RFRM algorithms, fault analysis FTA, ETA, FMEA, FMECA algorithms, fuzzy sets, intrusion detection systems, artificial neural networks and artificial immune systems. Through the analysis of the core computational intelligence algorithms used in the smart grid cyber security in power system network security lab, we clearly defined existing smart grid research challenges.

KW - Smart Grid

KW - Cyber Security

KW - Computational Intelligence

UR - http://ecm.sckcen.be/OTCS/llisapi.dll/open/ezp_106559

U2 - 10.1007/978-3-642-13498-2_11

DO - 10.1007/978-3-642-13498-2_11

M3 - In-proceedings paper

SN - 3642134971

SN - 9783642134975

VL - 2

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

SP - 77

EP - 84

BT - Advances in Swarm Intelligence

CY - Heidelberg, Germany

T2 - First International Conference in Swarm Intelligence

Y2 - 12 June 2010 through 15 June 2010

ER -

Wang Y, Ruan D, Xu J, Wen M, Deng L. Computational intelligence algorithms analysis for smart grid cyber security. In Advances in Swarm Intelligence. PART 2 ed. Vol. 2. Heidelberg, Germany. 2010. p. 77-84. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-13498-2_11