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

Abstract

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.

Early Attack Detection for Securing GOOSE Network Traffic

“…To thoroughly describe the characteristics of the GOOSE network traffic, an ARFIMA model is presented in [7]. The ARFIMA model is a generalization of the integer order in an autoregressive integral moving average (ARIMA) model.…”

“…Analysis of the GOOSE network traffic using relevant invariants of the communication network presented in [33] shows presence of Long-Range Dependency (LRD) characteristics [7] that can modeled using a state-space representation of an ARFIMA model as presented in Section II-B. In the present section, estimation of the parameter vector of the considered model is tackled.…”

“…1) Analysis and modeling of the network traffic: Analysis of the characteristics of the network traffic in IEC 61850 substations show presence of Long-Range Dependency (LRD) properties that can be described by an ARFIMA model [7] to represent the network x est [k]. An ARFIMA model is an extension of the autoregressive fractionally integrated moving average (ARIMA) model that allows use of non-integer values of the differencing parameter d. More details can be found in the pioneer works of [37] and [38].…”

“…Denial of Service (DoS) attacks resulting from GOOSE poisoning attacks are a considerable threat to the availability of the data [7]. To counter it, use of Intrusion Detection System (IDS) is suggested in the IEC 62351 standard where different recommendations to enhance the security of smart grids including electrical substations, are presented.…”

“…Third, to the best of our knowledge, none of the existing work proposes an early anomaly detection approach of GOOSE attacks in IEC 61850 substations. In fact, the model in [7] corresponds to an accurate representation of the network in IEC 61850. However it cannot provide an early detection of advanced DoS attacks since j-step ahead predictions cannot be computed from the proposed model.…”