TY - JOUR
T1 - Data-stream-based intrusion detection system for advanced metering infrastructure in smart grid
T2 - A feasibility study
AU - Faisal, Mustafa Amir
AU - Aung, Zeyar
AU - Williams, John R.
AU - Sanchez, Abel
N1 - Funding Information:
Jindal et a/used HRV and BFV to demonstrate the effect of medicines particularly material-less medicines like those in Homeopathy, in normal subjects and documented interesting observations [10]. This work drew the attention ofCentral Council for Research in Homeopathy (CCRH). Mumbai regional office of CCRH wanted to conduct a meticulous investigation using this technique with the following protocol:
Publisher Copyright:
© 2015 IEEE.
PY - 2015/3/1
Y1 - 2015/3/1
N2 - As advanced metering infrastructure (AMI) is responsible for collecting, measuring, and analyzing energy usage data, as well as transmitting this information from a smart meter to a data concentrator and then to a headend system in the utility side, the security of AMI is of great concern in the deployment of smart grid. In this paper, we analyze the possibility of using data stream mining for enhancing the security of AMI through an intrusion detection system (IDS), which is a second line of defense after the primary security methods of encryption, authentication, authorization, etc. We propose a realistic and reliable IDS architecture for the whole AMI system, which consists of individual IDSs for three different levels of AMI's components: smart meter, data concentrator, and AMI headend. We also explore the performances of various existing state-of-the-art data stream mining algorithms on a publicly available IDS data set, namely, the KDD Cup 1999 data set. Then, we conduct a feasibility analysis of using these existing data stream mining algorithms, which exhibit varying levels of accuracies, memory requirements, and running times, for the distinct IDSs at AMI's three different components. Our analysis identifies different candidate algorithms for the different AMI components' IDSs, respectively.
AB - As advanced metering infrastructure (AMI) is responsible for collecting, measuring, and analyzing energy usage data, as well as transmitting this information from a smart meter to a data concentrator and then to a headend system in the utility side, the security of AMI is of great concern in the deployment of smart grid. In this paper, we analyze the possibility of using data stream mining for enhancing the security of AMI through an intrusion detection system (IDS), which is a second line of defense after the primary security methods of encryption, authentication, authorization, etc. We propose a realistic and reliable IDS architecture for the whole AMI system, which consists of individual IDSs for three different levels of AMI's components: smart meter, data concentrator, and AMI headend. We also explore the performances of various existing state-of-the-art data stream mining algorithms on a publicly available IDS data set, namely, the KDD Cup 1999 data set. Then, we conduct a feasibility analysis of using these existing data stream mining algorithms, which exhibit varying levels of accuracies, memory requirements, and running times, for the distinct IDSs at AMI's three different components. Our analysis identifies different candidate algorithms for the different AMI components' IDSs, respectively.
KW - Advanced metering infrastructure (AMI)
KW - data stream mining
KW - intrusion detection system (IDS)
KW - smart grid (SG)
UR - http://www.scopus.com/inward/record.url?scp=85028151153&partnerID=8YFLogxK
U2 - 10.1109/JSYST.2013.2294120
DO - 10.1109/JSYST.2013.2294120
M3 - Article
AN - SCOPUS:85028151153
SN - 1932-8184
VL - 9
SP - 31
EP - 44
JO - IEEE Systems Journal
JF - IEEE Systems Journal
IS - 1
M1 - 6720175
ER -