Artificial Intelligence Techniques to Prevent Cyber Attacks on Smart Grids
DOI:
https://doi.org/10.51381/adrs.v3i1.42Keywords:
Smart Grid cybersecurity, Artificial Intelligence protection, Anomaly detectionAbstract
Energy is one of the main elements that allows society to maintain its living standards and continue as usual. For this reason, the energy distribution is both one of the most important and targeted by attacks Critical Infrastructure. Many of the other Critical Infrastructures rely on energy to work reliably. Some states are particularly interested in getting stealth access to -and take control of- energy production and distribution of other Nations. This way they can create huge disruption and get a significant advantage in case of conflict. In the recent past, we could observe some real-life demonstrations of this fact. The introduction of smart grids and ICT in the management of energy infrastructures has great benefits but also introduces new attack surfaces and ways for attackers to gain control. As a benefit, we can also collect more data and metrics to better understand the state of the grid. New techniques based on Artificial Intelligence and machine learning can take advantage of the available data to help the protection of the infrastructures and detect ongoing threats. Smart Meters which are connected intelligent devices spread over the grid and the geographical distribution of the population. For this reason, they can be very useful data collection assets but also a target for attack. In this paper, the authors consider and analyze various innovative techniques that can be used to enhance the security and reliability of Smart Grids.
References
FireEye. (2019). M-Trends 2019. FireEye.
Anderson, R., & Fuloria, S. (2010). Who Controls the off Switch? 2010 First IEEE International Conference on Smart Grid Communications. Gaithersburg, MD: IEEE.
AV-TEST. (n.d.). Malware Statistics & Trends Report. Retrieved from AV-TEST: https://www.av-test.org/en/statistics/malware/
Bernabeu, E. E., & Katiraei, F. (2011). Aurora Vulnerability: Issues & Solutions Hardware Mitigation Devices (HMDs). Quanta Technology.
Blueliv. (2019). Inside the Shamoon3 toolkit. Blueliv.
BRONK, C., & TIKK–RINGAS, E. (2013). Hack or Attack? Shamoon and the Evolution of Cyber Conflict. THE JAMES A.BAKER IIIINSTITUTE FOR PUBLIC POLICY.
Bundock, R. (2015, November 2). Organised crime and EU solidarity – Enel Italy talks cybersecurity. Retrieved from Smart Energy International: https://www.smart-energy.com/interviews/enel-italy-talks-cybersecurity/
Collantes, M. H., & Padilla, A. L. (2015). Protocols and network security in ICS infrastructures. INCIBE.
Dragos. (2017). CRASHOVERRIDE: Analysis of the Threat to Electric Grid Operations. Dragos.
Dragos. (2017). TRISIS Malware: Analysis of Safety System Targeted Malware. Dragos.
Durbhaka, G. K., & Selvaraj, B. (2016). Predictive maintenance for wind turbine diagnostics using vibration signal analysis based on collaborative recommendation approach. 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI). Jaipur: IEEE.
ECSO. (2018). ENERGY NETWORKS AND SMART GRIDS: Cyber security for the energy sector. ECSO.
El Mrabet, Z., Kaabouch, N., El Ghazi, H., & El Ghazi, H. (2018). Cyber-Security in Smart Grid: Survey and Challenges. Computers & Electrical Engineering.
F-Secure. (2019). The state of the station: A report on attackers in the energy industry. F-Secure.
Gebraeel, N., Lawley, M., Liu, R., & Parmeshwaran, V. (2004). Residual life predictions from vibration-based degradation signals: a neural network approach. IEEE Transactions on Industrial Electronics.
Gunduz, M., & Das, R. (2018). Analysis of cyber-attacks on smart grid applications. 2018 International Conference on Artificial Intelligence and Data Processing (IDAP). Malatya: IEEE.
Hacquebord, F., & Pernet, C. (2019). Drilling Deep: A Look at Cyberattacks on the Oil and Gas Industry. Trend Micro.
INCIBE. (2017). Security guide for Industrial Protocols - Smart Grid. INCIBE.
Karimipour, H., Geris, S., Dehghantanha, A., & Leung, H. (2019). Intelligent Anomaly Detection for Large-scale Smart Grids. 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) (pp. 1-4). Edmonton, AB, Canada: IEEE.
Kaspersky Lab. (2017). From Shamoon to StoneDrill. Kaspersky.
Kesler, B. (2011). The Vulnerability of Nuclear Facilities to Cyber Attack. Strategic Insights.
Kolias, C., Kambourakis, G., Stavrou, A., & Voas, J. (2017). DDoS in the IoT: Mirai and Other Botnets. Computer.
Langner, R. (2013). To Kill a Centrifuge. Langner Group.
Lee, R. M., Assante, M. J., & Conway, T. (2016). Analysis of the Cyber Attack on the Ukrainian Power Grid. E-ISAC.
Lopez, C., Sargolzaei, A., Santana, H., & Huerta, C. (2015). Smart Grid Cyber Security: An overview of Threats and Countermeasures. Journal of Power and Energy Engineering.
Marino, D. L., Wickramasinghe, C. S., Amarasinghe, K., Challa, H., Richardson, P., A. Jillepalli, A., Manic, M. (2019). Cyber and Physical Anomaly Detection in Smart-Grids. 2019 Resilience Week (RWS). San Antonio, TX, USA: IEEE.
Mattioli, R., & Levy-Bencheton, C. (2014). Methodologies for the identification of Critical Information Infrastructure assets and services. ENISA.
Mohsenian-Rad, A.-H., & Leon-Garcia, A. (2011). Distributed Internet-Based Load Altering Attacks Against Smart Power Grids. EEE Transactions on Smart Grid.
Nelson, N. (2016). The Impact of Dragonfly Malware on Industrial Control Systems. SANS.
Otuoze, A., Mustafa, M., & Larik, R. (2018). Review Smart grids security challenges: Classification by sources of threats. Journal of Electrical Systems and Information Technology.
Ponemon. (2019). The Cost of Cybercrime. Accenture.
Reed, T. C. (2004). At the Abyss: An Insider's History of the Cold War. Presidio Pr.
Rossi, B., Chren, S., Buhnova, B., & Pitner, T. (2016). Anomaly detection in Smart Grid data: An experience report. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Budapest: IEEE.
Sgouras, K. I., Birda, A. D., & Labridis, D. P. (2014). Cyber Attack Impact on Critical Smart Grid Infrastructures. ISGT 2014. Washington, DC: IEEE.
Shereen, E., & Dán, G. (2020). Model-Based and Data-Driven Detectors for Time Synchronization Attacks Against PMUs. IEEE Journal on Selected Areas in Communications.
Simonov, M., Bertone, F., & Goga, K. (2019). Detecting the Manipulation of Demand via IoT. 2019 5th International Conference on Event-Based Control, Communication, and Signal Processing (EBCCSP). Vienna, Austria: IEEE.
Simonov, M., Bertone, F., Goga, K., & Terzo, O. (2018). Cyber Kill Chain Defender for Smart Meters. Complex, Intelligent, and Software Intensive Systems. CISIS 2018. Springer.
Simonov, M., Chicco, G., & Zanetto, G. (2017). Event-driven energy metering: Principles and applications. IEEE Transactions on Industry Applications.
Soltan, S., Mittal, P., & Poor, H. V. (2018). BlackIoT: IoT Botnet of High Wattage Devices Can Disrupt the Power Grid. USENIX Security Symposium 2018.
Tofan, D., NIKOLAKOPOULOS, T., & Darra, E. (2016). The cost of incidents affecting CIIs. ENISA.
Vanraj, Goyal, D., Saini, A., Dhami, S. S., & Pabla, B. S. (2016). Intelligent predictive maintenance of dynamic systems using condition monitoring and signal processing techniques — A review. 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA). Dehradun: IEEE.
Wallace, B., & McClure, S. (2014). Operation Cleaver. Cylance.
