TY - GEN
T1 - Complex Network of Damage Assessment Using GMM Based FAIR
AU - Park, Mookyu
AU - Joo, Minhee
AU - Seo, Junwoo
AU - Kim, Kyoungmin
AU - Park, Moosung
AU - Lee, Kyungho
N1 - Funding Information:
ACKNOWLEDGMENT This work was supported by Defense Acquisition Program Administration and Agency for Defense Development under the contract. (UD060048AD)
Publisher Copyright:
© 2018 IEEE.
Copyright:
Copyright 2018 Elsevier B.V., All rights reserved.
PY - 2018/5/25
Y1 - 2018/5/25
N2 - With the recent increase in IoT devices, network connectivity devices such as routers or switches are increasing. This increase in equipment could increase the degree of a particular node (hub node or hub router) in terms of network characteristics. In other words, the structure of the network can be changed into a more complicated network form. The convergence of links to these specific nodes provides an opportunity for attackers to enjoy maximum benefit at minimal cost. An attacker can attack only a few hub routers with a high degree of network structure, which can paralyze networks in some areas. This possibility is because the actual network follows the power degree distribution. These research measures threats based on network characteristics and data on exploits of network equipment. The threat measurement is applied to the FAIR(Factor Analysis of Information Risk) model, and the damage caused by the macro network structure is assessed.
AB - With the recent increase in IoT devices, network connectivity devices such as routers or switches are increasing. This increase in equipment could increase the degree of a particular node (hub node or hub router) in terms of network characteristics. In other words, the structure of the network can be changed into a more complicated network form. The convergence of links to these specific nodes provides an opportunity for attackers to enjoy maximum benefit at minimal cost. An attacker can attack only a few hub routers with a high degree of network structure, which can paralyze networks in some areas. This possibility is because the actual network follows the power degree distribution. These research measures threats based on network characteristics and data on exploits of network equipment. The threat measurement is applied to the FAIR(Factor Analysis of Information Risk) model, and the damage caused by the macro network structure is assessed.
KW - Complex Network
KW - Damage Assessment
KW - FAIR
KW - Gaussian Mixture Model(GMM)
UR - http://www.scopus.com/inward/record.url?scp=85048498341&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85048498341&partnerID=8YFLogxK
U2 - 10.1109/BigComp.2018.00111
DO - 10.1109/BigComp.2018.00111
M3 - Conference contribution
AN - SCOPUS:85048498341
T3 - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
SP - 627
EP - 630
BT - Proceedings - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Big Data and Smart Computing, BigComp 2018
Y2 - 15 January 2018 through 18 January 2018
ER -