COVID-19 Reporting and Information • HEERF I & II

Cyber-Physical Systems Security

Secure Smart Farming and Anomaly Detection (Lead Maanak Gupta, Student: Sina Sontowski, Mary Adkison)

In this project different attacks and develop anomaly detection solutions for smart farming. We orchestrated a DDOS attack that can hinder the functionality of a smart farm by disrupting deployed on-field sensors. A MakerFocus ESP8266 Development Board WiFiDeauther Monster is used to detach the connected Raspberry Pi from the network and prevent sensor data from being sent to the remote cloud. Additionally, this attack was expanded to include the entire network, obstructing all devices from connecting to the network.

Formal Cyber Security Models and Architectures (Lead - Maanak Gupta, Student: Glen Cathey, Thanh Pham)

In this project, we study and develop a formal access control model and novel architectures for cloud and edge assisted IoT platforms including AWS and Google Cloud, and extend them with more fine-grained attribute-based access control solutions. Further, we adapt these solutions to fit various CPS/IoT domains including smart connected cars, smart farming, Industrial IoT etc.

Transactions Accountability in Wireless Power Transfer Systems (Faculty Lead: Dr. Denis Ulybyshev. Students: Jonah White, James Massengille, Vadim Kholodilo)

Transactions accountability needs to be guaranteed in Wireless Power Transfer (WPT) systems to prevent energy theft and resolve potential disputes between energy sellers and buyers. This project focuses on developing the techniques to provide a trusted verification of any transaction in the WPT system at any time. The transaction data are stored and transferred in a secure way which guarantees data confidentiality and integrity, as well as data origin integrity and failure recovery.

Anomaly Detection and Moving Target Defense in Cyber-Physical Systems (Faculty Leads: Drs. Mike Rogers, Denis Ulybyshev, William Eberle.  Students: M.R.A. Mithu, Rajesh Manicavasagm, Anthony Palmer, Trey Burks, Vadim Kholodilo, Jonah White)

Since cyber-physical systems are widely used in critical infrastructures, such as power grids and water distribution systems, it is very important to protect assets and detect anomalies at early stages to increase systems safety and reduce costs. Many old-generation devices, such as Programmable Logical Controllers (PLCs), Real-Time Automation Controllers (RTACs), relay-protection devices, meters, Remote Terminal Units (RTUs), were initially designed without cyber security in mind. It is expensive to replace the old-generation hardware with the modern one that supports cyber security mechanisms, such as encryption, digital signatures, and authentication. In this project, we aim to develop cyber protection solutions that would work on top of existing hardware and communication protocols, such as Modbus®. We are also developing machine learning-based device feature classification and anomaly detection algorithms, including temporal anomalies, with improved accuracy and precision. Furthermore, our solution can detect the anomaly root causes.   

Privacy-preserving Advanced Metering Infrastructure for Smart Grid (Lead: Siraj)

In the smart grid, consumers are concerned about their privacy that might be violated by a utility company, who has direct access to their energy consumption. The analysis of the energy consumption behavior can expose consumer sensitive information. Such information can include (but not limited to) the appliances the consumer uses or the time interval when the consumer is absent at home. This, in turn, can potentially reveal information that can be misused for a marketing purpose or burglary preparation. In order to resolve this problem, our center has been working on developing a privacy-preserving Advanced Metering Infrastructure specifically designed to protect consumer privacy. It allows energy providers to fulfill a fine-grained energy consumption analysis such as fraud detection and load monitoring. In addition, this solution incorporates time-of-use billing and satisfies computational constraints that Smart Meters are bound by. Also, the developed model of the Advanced Metering Infrastructure provides consumers with the opportunity to select the most suitable utility company based on the time-of-use prices. To sum up, the developed architecture preserves consumer privacy and, at the same time, allows to fully utilize the capabilities of Advanced Metering Infrastructure providing fine-grained energy readings.

Smart Grid Energy Fraud Detection (Lead: Siraj)

Energy fraud detection is one of the crucial parts of the Smart Grid security. In a legacy power grid, it is difficult to detect fraudulent activities (such as tapping to an electricity line or tampering with a power meter) because of lack of data to analyze. However, Smart Grid can report fine-grained energy consumption that can be utilized for an intelligent analysis by applying machine learning techniques. Machine learning have been widely used for analyzing data, learning patterns, and extracting useful information. Graduate and undergraduate students in our center have been working on employing machine learning techniques to detect anomalies corresponding to energy fraud. They have used artificial neural networks and decision trees to successfully identify simulated fraudulent activities with a high detection rate. The machine learning techniques were trained using real-world data provided by the Irish Social Science Data Archive Center. The data comprised of energy measurements and corresponding timestamps. The energy fraud they have worked with can be categorized in two groups. The first group includes fraudulent activities that simulate rogue connections (tapping to an electricity line). The second group simulates reporting less energy than actually was consumed. As a result of applying machine learning techniques mentioned above, it was demonstrated that they can detect energy fraud with a high detection rate.

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