%0 Conference Proceedings
%T Toward Resilient Smart Grid Communications Using Distributed SDN with ML-Based Anomaly Detection
%+ University of Florida [Gainesville] (UF)
%A Starke, Allen
%A Mcnair, Janise
%A Trevizan, Rodrigo
%A Bretas, Arturo
%A Peeples, Joshua
%A Zare, Alina
%Z Part 2: Learning-Based Networking
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B International Conference on Wired/Wireless Internet Communication (WWIC)
%C Boston, MA, United States
%Y Kaushik Roy Chowdhury
%Y Marco Di Felice
%Y Ibrahim Matta
%Y Bo Sheng
%I Springer International Publishing
%3 Wired/Wireless Internet Communications
%V LNCS-10866
%P 83-94
%8 2018-06-18
%D 2018
%R 10.1007/978-3-030-02931-9_7
%K Software defined networks
%K Anomaly detection
%K Machine learning
%K Security
%K Resilience
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Next generation “Smart” systems, including cyber-physical systems like smart grid and Internet-of-Things, integrate control, communication and computation to achieve stability, efficiency and robustness of physical processes. While a great amount of research has gone towards building these systems, security in the form of resilient and fault-tolerant communications for smart grid systems is still immature. In this paper, we propose a hybrid, distributed and decentralized (HDD) SDN architecture for resilient Smart Systems. It provides a redundant controller design for fault-tolerance and fail-over operation, as well as parallel execution of multiple anomaly detection algorithms. Using the k-means clustering algorithm from the machine learning literature, it is shown that k-means can be used to produce a high accuracy (96.9%) of identifying anomalies within normal traffic. Furthermore, incremental k-means produces a slightly lower accuracy (95.6%) but demonstrated an increased speed with respect to k-means and fewer CPU and memory resources needed, indicating a possibility for scaling the system to much larger networks.
%G English
%Z TC 6
%Z WG 6.2
%2 https://inria.hal.science/hal-02269741/document
%2 https://inria.hal.science/hal-02269741/file/470666_1_En_7_Chapter.pdf
%L hal-02269741
%U https://inria.hal.science/hal-02269741
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC6
%~ IFIP-WG6-2
%~ IFIP-WWIC
%~ IFIP-LNCS-10866