Modeling and Simulation of Data Flow for VLAN-Based Communication in Substations

Zhang, Zhidan; Huang, Xiaoqing; Keune, B.M.; Cao, Yijia; Li, Yong

doi:10.1109/jsyst.2015.2428058

Cited by 26 publications

(28 citation statements)

References 32 publications

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“…The detection time for the CUSUM and GLRT is defined when the conditions in Equations (23) and (25) hold. The delay time is expressed in samples and it represents the number of discrete time samples after which the anomaly is detected.…”

Section: Performance Of the Anomaly Detection (Ad) Methodsmentioning

confidence: 99%

“…In the present work, we will mainly focus on the process level network. There are different types of messages exchanged within the substation and are divided into three types from a data flow perspective [25]. Cyclic data flows are time-driven data that are commonly used for transmitting power measurements from field devices.…”

Section: Characteristics Of the Process Network Trafficmentioning

confidence: 99%

“…Fast or medium speed messages have different characteristics from, for instance, file transfer function or access control command messages. Thus, they can be divided into three different categories based on their data flow characteristics, namely, cyclic, stochastic and burst data [25]. A Pareto distribution for burst data flow was suggested in [25], whereas in [7], a Weibull distribution was used instead as it is claimed to fit better increasing retransmission scheme of bursty GOOSE messages.…”

Section: Distributional Considerations Of the Datamentioning

confidence: 99%

“…Thus, they can be divided into three different categories based on their data flow characteristics, namely, cyclic, stochastic and burst data [25]. A Pareto distribution for burst data flow was suggested in [25], whereas in [7], a Weibull distribution was used instead as it is claimed to fit better increasing retransmission scheme of bursty GOOSE messages. Network in general Internet traffic can be characterized by heavy-tail distributions such as Ethernet bursts [28] and FTP activity.…”

Section: Distributional Considerations Of the Datamentioning

confidence: 99%

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Detection of DoS Attacks Using ARFIMA Modeling of GOOSE Communication in IEC 61850 Substations

et al. 2020

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Integration of Information and Communication Technology (ICT) in modern smart grids (SGs) offers many advantages including the use of renewables and an effective way to protect, control and monitor the energy transmission and distribution. To reach an optimal operation of future energy systems, availability, integrity and confidentiality of data should be guaranteed. Research on the cyber-physical security of electrical substations based on IEC 61850 is still at an early stage. In the present work, we first model the network traffic data in electrical substations, then, we present a statistical Anomaly Detection (AD) method to detect Denial of Service (DoS) attacks against the Generic Object Oriented Substation Event (GOOSE) network communication. According to interpretations on the self-similarity and the Long-Range Dependency (LRD) of the data, an Auto-Regressive Fractionally Integrated Moving Average (ARFIMA) model was shown to describe well the GOOSE communication in the substation process network. Based on this ARFIMA-model and in view of cyber-physical security, an effective model-based AD method is developed and analyzed. Two variants of the statistical AD considering statistical hypothesis testing based on the Generalized Likelihood Ratio Test (GLRT) and the cumulative sum (CUSUM) are presented to detect flooding attacks that might affect the availability of the data. Our work presents a novel AD method, with two different variants, tailored to the specific features of the GOOSE traffic in IEC 61850 substations. The statistical AD is capable of detecting anomalies at unknown change times under the realistic assumption of unknown model parameters. The performance of both variants of the AD method is validated and assessed using data collected from a simulation case study. We perform several Monte-Carlo simulations under different noise variances. The detection delay is provided for each detector and it represents the number of discrete time samples after which an anomaly is detected. In fact, our statistical AD method with both variants (CUSUM and GLRT) has around half the false positive rate and a smaller detection delay when compared with two of the closest works found in the literature. Our AD approach based on the GLRT detector has the smallest false positive rate among all considered approaches. Whereas, our AD approach based on the CUSUM test has the lowest false negative rate thus the best detection rate. Depending on the requirements as well as the costs of false alarms or missed anomalies, both variants of our statistical detection method can be used and are further analyzed using composite detection metrics.

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Section: Performance Of the Anomaly Detection (Ad) Methodsmentioning

confidence: 99%

Section: Characteristics Of the Process Network Trafficmentioning

confidence: 99%

Section: Distributional Considerations Of the Datamentioning

confidence: 99%

Section: Distributional Considerations Of the Datamentioning

confidence: 99%

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Detection of DoS Attacks Using ARFIMA Modeling of GOOSE Communication in IEC 61850 Substations

et al. 2020

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“…According to the IEC 61850, SCN data flow over time could be divided into three groups which are periodic, burst and random data [5]. Considering the functionality of each message, it is distributed specifically in the time domain.…”

Section: A Scn Communication Trafficmentioning

confidence: 99%