EPIC: An Electric Power Testbed for Research and Training in Cyber Physical Systems Security

Adepu, Sridhar; Kandasamy, Nandha Kumar; Mathur, Aditya P.

doi:10.1007/978-3-030-12786-2_3

Cited by 40 publications

(24 citation statements)

References 33 publications

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“…According to [30], the communication layout of EPIC is shown in Figure 2, which is composed of a SCADA workstation, historian, programmable logic controller (PLC), intelligent electrical devices (IEDs), access points (APs), and switches (SWs), and redundancy in the ring network is achieved using high availability seamless redundancy (HSR) and media redundancy protocol (MRP).…”

Section: Cskg Structure Definitionmentioning

confidence: 99%

Data-Driven Cybersecurity Knowledge Graph Construction for Industrial Control System Security

Shen

Wang

et al. 2020

Wireless Communications and Mobile Computing

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Industrial control systems (ICS) involve many key industries, which once attacked will cause heavy losses. However, traditional passive defense methods of cybersecurity have difficulty effectively dealing with increasingly complex threats; a knowledge graph is a new idea to analyze and process data in cybersecurity analysis. We propose a novel overall framework of data-driven industrial control network security defense, which integrated fragmented multisource threat data with an industrial network layout by a cybersecurity knowledge graph. In order to better correlate data to construct a knowledge graph, we propose a distant supervised relation extraction model ResPCNN-ATT; it is based on a deep residual convolutional neural network and attention mechanism, reduces the influence of noisy data in distant supervision, and better extracts deep semantic features in sentences by using deep residuals. We empirically demonstrate the performance of the proposed method in the field of general cybersecurity by using dataset CSER; the model proposed in this paper achieves higher accuracy than other models. And then, the dataset ICSER was used to construct a cybersecurity knowledge graph (CSKG) on the basis of analyzing specific industrial control scenarios, visualizing the knowledge graph for further security analysis to the industrial control system.

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Section: Cskg Structure Definitionmentioning

confidence: 99%

Data-Driven Cybersecurity Knowledge Graph Construction for Industrial Control System Security

Shen

Wang

et al. 2020

Wireless Communications and Mobile Computing

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“…The robust satisfaction value can be used to measure the distance of the current trace to the error state. The error state could be an unsafe state that may cause a physical safety problem, such as aircraft collision, water overflowing in a water treatment facility [25]. Or the error state is a state that could avoid the attack detection mechanisms [26].…”

Section: B Threat Modelmentioning

confidence: 99%

CPFuzz: Combining Fuzzing and Falsification of Cyber-Physical Systems

Shang

Wang

et al. 2020

IEEE Access

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Coverage-guided grey-box fuzzing for computer systems has been explored for decades. However, existing techniques do not adequately explore the space of continuous behaviors in Cyber-Physical Systems (CPSs), which may miss safety-critical bugs. Optimization-guided falsification is promising to find violations of safety specifications, but not suitable for identifying traditional program bugs. This article presents a fuzzing process for finding safety violations at the development phase, which is guided by two quantities: a branch coverage metric to explore discrete program behaviors and a Linear Temporal Logic (LTL) robust satisfaction metric to identify undesirable continuous plant behaviors. We implement CPFuzz to demonstrate the utility of the idea and estimate its effectiveness on seven control system benchmarks. The results show up to a better performance in average time to find violations on all benchmarks than S-TaLiRo and six benchmarks than S3CAMX. Finally, we exploit CPFuzz to synthesize the sensor spoofing attack on a DC motor with fixed-point overflow vulnerability as a case study. INDEX TERMSCoverage guided fuzzing, cyber-physical systems, linear temporal logic, optimizationguided falsification VOLUME 4, 2016This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.

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“…The Center for Cybersecurity Research, iTrust [20], also developed several datasets for different ICS. The most important datasets they developed are: Secure Water Treatment (SwaT) [21], Water Distribution (WADI) [22] and Electric Power and Intelligent Control (EPIC) [23]. The three datasets contain normal and malicious samples from false injection attacks.…”

Section: A Datasetsmentioning

confidence: 99%

On the Generation of Anomaly Detection Datasets in Industrial Control Systems

et al. 2019

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In recent decades, Industrial Control Systems (ICS) have been affected by heterogeneous cyberattacks that have a huge impact on the physical world and the people's safety. Nowadays, the techniques achieving the best performance in the detection of cyber anomalies are based on Machine Learning and, more recently, Deep Learning. Due to the incipient stage of cybersecurity research in ICS, the availability of datasets enabling the evaluation of anomaly detection techniques is insufficient. In this paper, we propose a methodology to generate reliable anomaly detection datasets in ICS that consists of four steps: attacks selection, attacks deployment, traffic capture and features computation. The proposed methodology has been used to generate the Electra Dataset, whose main goal is the evaluation of cybersecurity techniques in an electric traction substation used in the railway industry. Using the Electra dataset, we train several Machine Learning and Deep Learning models to detect anomalies in ICS and the performed experiments show that the models have high precision and, therefore, demonstrate the suitability of our dataset for use in production systems. INDEX TERMS Anomaly detection, critical infrastructures, industrial control, industrial control systems, industry applications, machine learning.

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EPIC: An Electric Power Testbed for Research and Training in Cyber Physical Systems Security

Cited by 40 publications

References 33 publications

Data-Driven Cybersecurity Knowledge Graph Construction for Industrial Control System Security

Data-Driven Cybersecurity Knowledge Graph Construction for Industrial Control System Security

CPFuzz: Combining Fuzzing and Falsification of Cyber-Physical Systems

On the Generation of Anomaly Detection Datasets in Industrial Control Systems

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