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Abstract: Power systems are undergoing an intense process of modernization, and becoming highly dependent on networked systems used to monitor and manage power system components. These so-called Smart Grids comprise energy generation, transmission, and distribution subsystems, which are monitored and managed by Supervisory Control and Data Acquisition (SCADA) systems. In this paper, we discuss the benefits of using Software-Defined Networking (SDN) to assist in the deployment of next generation SCADA systems. We also present a specific Network-Based Intrusion Detection System (NIDS) for SDNbased SCADA systems, which uses SDN to capture network information and is responsible for monitoring the communication between power grid components. Our approach relies on SDN to periodically gather statistics from network devices, which are then processed by a specific type of Machine Learning algorithm, more precisely One-Class Classification (OCC) algorithms. Given that attack traces in SCADA networks are scarce and not publicly disclosed by utility companies, the main advantage of using OCC algorithms is that they do not depend on known attack signatures to detect possible malicious traffic. Our results indicate that OCC algorithms achieve an approximate accuracy of 98% and can be effectively used to detect cyber-attacks targeted against SCADA systems.

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Abstract: ICT and SCADA systems will play an increasingly operationally critical role in the smart grid. Cyber-attacks to these systems have the potential to result in outcomes in the physical domain. For example, power systems equipment could be damaged, reduced power quality could occur – potentially leading to blackouts – and, in extreme cases, result in safety- related incidents. Recently developed cybersecurity risk assess- ment guidelines for the smart grid reflect this understanding. However, they provide limited input about how these risks should be analysed. In this paper, we present how event trees – an inductive modelling technique – can be used to explore the potential impact of a cyber-attack to information assets in the smart grid. Using event tree analysis, we have examined cyber- attacks in the context of a voltage control use case, indicating how power grid protection measures may influence the outcome of such an attack and its physical impact to a smart grid.

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Abstract: Electric power grids are undergoing a modernization process. By relying on the ICT infrastructure and on Internet connectivity, these so-called Smart Grids are now able to provide new functionalities and to become more efficient. However, despite the existence of a few standards that aim to specify the secure operation of Smart Grids, utility companies do not have a comprehensive set of metrics and evaluation tools for assessing security properties in these infrastructures. Thus, it is necessary to develop new toolsets to provide support for vulnerability analysis in Smart Grids. This paper proposes ASTORIA, a framework developed to allow the simulation of attacks and the evaluation of their impact on Smart Grid infrastructures, using closely-related real devices and real topologies comprising both power grid elements as well as ICT and networking equipment. We anticipate that ASTORIA can be used by Smart Grid operators not only to analyze the impact of malicious attacks and other security threats in different components, but also to permit the development and evaluation of anomaly detection techniques in a simulation environment. Further, we present evaluation scenarios illustrating customizable Smart Grid topologies, comprising sensors, master and remote stations, and using an extensible set of attack profiles.

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Abstract:Smart grids and smart homes improve energy management by coupling communication capabilities to their devices. Due to computational constraints of these devices, employment of simplified communication protocols is necessary. In this paper, we investigate the use of communication protocols based on CoAP and 6LoWPAN in smart home environments. Specifically, we analyze the vulnerabilities a smart home employing CoAP and 6LoWPAN may be susceptible to. We also present a performance analysis of the use of these protocols for ensuring secure communications in smart homes.

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Abstract: Power grids are responsible for the transmission and distribution of electricity to end-users. These systems are undergoing a modernization process through the use of Information and Communication Technology (ICT), transforming the electric system into Smart Grids. In this context, Supervisory Control and Data Acquisition (SCADA) systems are responsible for the management and monitoring of substations and field devices. In this paper, we investigate the use of SDN as an approach to assist in the modernization of SCADA systems. We discuss its possible benefits, such as simplified management of power system resources. Moreover, SDN can facilitate the creation of new network applications that previously, with traditional networks, were more complex to be implemented. To illustrate the benefits of the use of SDN in SCADA, we designed a mechanism that aims to prevent a possible eavesdropper from fully capturing communication flows between SCADA components. The mechanism was implemented as an SDN-based application for SCADA systems that uses multipath routing, which relies on SDN features to frequently modify communication routes between SCADA devices. Further, we performed an experimental evaluation to verify the impact and performance of the mechanism in the SCADA network.

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Abstract: Smart grids are power distribution networks that include a significant communication infrastructure, which is used to collect usage data and monitor the operational status of the grid. As a consequence of this additional infrastructure, power networks are at an increased risk of cyber-attacks. In this letter, we address the problem of detecting and attributing anomalies that occur in the sub-meter power consumption measurements of a smart grid, which could be indicative of malicious behavior. We achieve this by clustering a set of statistical features of power measurements that are determined using the Smoothed Pseudo Wigner Ville (SPWV) energy Time-Frequency (TF) distribution. We show how this approach is able to more accurately distinguish clusters of energy consumption than simply using raw power measurements. Our ultimate goal is to apply the principles of profiling power consumption measurements as part of an enhanced anomaly detection system for smart grids.

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Abstract: Smart grids are power distribution networks characterised by an increased level of automation of the infrastructure, sensors and actuators connected to monitoring and control centres, and are strongly supported by information and communication technology (ICT). Consequently, smart grids are more vulnerable to cyber-attacks. In this position paper, we advocate the need for management patterns that capture best-practices for ensuring the resilience of smart grids to cyber-attacks and other related challenges. Management patterns are akin to software design patterns in the sense that patterns promote the use of well-established solutions to recurring problems. These patterns describe how to orchestrate the cyber-physical behaviour of ICT, industrial control systems and human resources in a safe manner, in response to cyber-attacks.

Institution: UFRGS
Email: rjmartins@inf.ufrgs.br
Home Page: http://www.inf.ufrgs.br/~rjmartins/

Rafael de Jesus Martins is a Computer Engineering undergraduate student at the Federal University of Rio Grande so Sul (UFRGS), in Brazil. His current research interests include artificial intelligence and information security.

Undergrad Student

Institution: UFRGS
Email: agwermann@inf.ufrgs.br
Home Page: http://www.inf.ufrgs.br/~agwermann/

Alexandre Gustavo Wermann is a Computer Engineering undergraduate student at the Federal University of Rio Grande so Sul (UFRGS), in Brazil.