Timing-Based Anomaly Detection in SCADA Networks

2018 ◽  
pp. 48-59 ◽  
Author(s):  
Chih-Yuan Lin ◽  
Simin Nadjm-Tehrani ◽  
Mikael Asplund
Keyword(s):  
Anomaly Detection ◽  
Scada Networks

Unsupervised Time-Series based Anomaly Detection in ICS/SCADA Networks

2021 ◽  
Author(s):  
Ali Tekeoglu ◽  
Korkut Bekiroglu ◽  
Chen-Fu Chiang ◽  
Sam Sengupta
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Scada Networks

scholarly journals HYBRID DEEP NEURAL NETWORK ANOMALY DETECTION SYSTEM FOR SCADA NETWORKS

2021 ◽  
Vol 128 (2) ◽  
pp. 141-191
Author(s):  
Raogo Kabore ◽  
Hyacinthe Kouassi Konan ◽  
Adlès Kouassi ◽  
Yvon Kermarrec ◽  
Philippe Lenca ◽  
...  
Keyword(s):  
Neural Network ◽  
Anomaly Detection ◽  
Deep Neural Network ◽  
Detection System ◽  
Scada Networks ◽  
Network Anomaly Detection ◽  
Anomaly Detection System

scholarly journals A Comparative Analysis of Emulated and Real IEC-104 Spontaneous Traffic in Power System Networks

2021 ◽  
pp. 207-223
Author(s):  
C.-Y. Lin ◽  
Simin Nadjm-Tehrani
Keyword(s):  
Comparative Analysis ◽  
Anomaly Detection ◽  
Real Data ◽  
Power Networks ◽  
Network Characteristics ◽  
Scada Systems ◽  
Significant Research ◽  
Scada Networks ◽  
Real Traffic

AbstractSupervisory and Data Acquisition (SCADA) systems control and monitor modern power networks. As attacks targeting SCADA systems are increasing, significant research is conducted to defend SCADA networks including variations of anomaly detection. Due to the sensitivity of real data, many defence mechanisms have been tested only in small testbeds or emulated traffic that were designed with assumptions on how SCADA systems behave. This work provides a timing characterization of IEC-104 spontaneous traffic and compares the results from emulated traffic and real traffic to verify if the network characteristics appearing in testbeds and emulated traffic coincide with real traffic. Among three verified characteristics, two of them appear in the real dataset but in a less regular way, and one does not appear in the collected real data. The insights from these observations are discussed in terms of presumed differences between emulated and real traffic and how those differences are generated.

Anomaly Detection in Sensor Data

2018 ◽  
Vol 18 (1) ◽  
pp. 20-32 ◽  
Author(s):  
Jong-Min Kim ◽  
Jaiwook Baik
Keyword(s):  
Anomaly Detection ◽  
Sensor Data

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

Proposal of the Anomaly Detection Method of Live-alone by LOF using Multiple Sensors

2015 ◽  
Vol 135 (12) ◽  
pp. 749-755
Author(s):  
Taiyo Matsumura ◽  
Ippei Kamihira ◽  
Katsuma Ito ◽  
Takashi Ono
Keyword(s):  
Anomaly Detection ◽  
Detection Method ◽  
Multiple Sensors
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