A One-Class NIDS for SDN-Based SCADA Systems

被引:26
作者
da Silva, Eduardo Germano [1 ]
da Silva, Anderson Santos [1 ]
Wickboldt, Juliano Araujo [1 ]
Smith, Paul [2 ]
Granville, Lisandro Zambenedetti [1 ]
Schaeffer-Filho, Alberto [1 ]
机构
[1] Univ Fed Rio Grande do Sul, Inst Informat, Porto Alegre, RS, Brazil
[2] Austrian Inst Technol, Safety & Secur Dept, Vienna, Austria
来源
PROCEEDINGS 2016 IEEE 40TH ANNUAL COMPUTER SOFTWARE AND APPLICATIONS CONFERENCE WORKSHOPS, VOL 1 | 2016年
关键词
SOFTWARE-DEFINED NETWORKING; INTRUSION DETECTION; REQUIREMENTS; SECURITY; ISSUES;
D O I
10.1109/COMPSAC.2016.32
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Power systems are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage system components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this paper, we discuss the benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also present a specific Network-Based Intrusion Detection System (NIDS) for SDN-based SCADA systems, which uses SDN to capture network information and is responsible for monitoring the communication between power grid components. Our approach relies on SDN to periodically gather statistics from network devices, which are then processed by One-Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. Our results indicate that OCC algorithms achieve an approximate accuracy of 98% and can be effectively used to detect cyber-attacks targeted against SCADA systems.
引用
收藏
页码:303 / 312
页数:10
相关论文
共 36 条
[1]   An unsupervised anomaly-based detection approach for integrity attacks on SCADA systems [J].
Almalawi, Abdulmohsen ;
Yu, Xinghuo ;
Tari, Zahir ;
Fahad, Adil ;
Khalil, Ibrahim .
COMPUTERS & SECURITY, 2014, 46 :94-110
[2]  
[Anonymous], 2014, THESIS
[3]  
[Anonymous], 2013, Queue, DOI [10.1145/2559899.2560327, DOI 10.1145/2559899.2560327]
[4]  
[Anonymous], 2002, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
[5]  
Cahn A, 2013, INT CONF SMART GRID, P558, DOI 10.1109/SmartGridComm.2013.6688017
[6]  
Caruana R., 2006, P 23 INT C MACH LEAR, P161, DOI DOI 10.1145/1143844.1143865
[7]  
Cheung S., 2007, P SCADA SECURITY SCI, V46, P1
[8]  
CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411
[9]  
D. Incorporated, 2015, TECH REP
[10]   Identification and Selection of Flow Features for Accurate Traffic Classification in SDN [J].
da Silva, Anderson Santos ;
Machado, Cristian Cleder ;
Bisol, Rodolfo Vebber ;
Granville, Lisandro Zambenedetti ;
Schaeffer-Filho, Alberto .
2015 IEEE 14TH INTERNATIONAL SYMPOSIUM ON NETWORK COMPUTING AND APPLICATIONS (NCA), 2015, :134-141