The Experts below are selected from a list of 5337 Experts worldwide ranked by ideXlab platform
Shiyan Hu - One of the best experts on this subject based on the ideXlab platform.
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Combating Coordinated Pricing Cyberattack and Energy Theft in Smart Home Cyber-Physical Systems
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2018Co-Authors: Yuchen Zhou, Shiyan HuAbstract:The information exchange between the utility company and the smart community is crucial to the smart home cyber-physical systems. Yet the interaction between the two parties is vulnerable to many potential Cyberattacks, among which the most striking ones are pricing Cyberattacks and energy theft. Coordinated Cyberattacks have emerged as an advanced attacking scheme with both pricing attack and energy theft applied in the cooperative manner, which can induce significant impact to smart home systems even if each attack is applied with only moderate strength. Such attacks cannot be effectively detected since the existing techniques are designed for detecting either pricing attack or energy theft without considering the impact due to coordinated attacks. This paper aims at developing the detection framework for coordinated Cyberattacks considering coordinated impacts of various attacking strategies using an advanced continuous state partially observable Markov decision process. Handling coordinated attacks induces drastic increase in time complexity, which motivates us to propose innovative cross entropy state sampling and Fourier belief state approximation for the solving of developed detection framework. Our simulation results demonstrate that the coordinated Cyberattack can reduce his/her electricity bill by 32.65%. In addition, the proposed detection technique can better capture coordinated attacks than the conventional detection technique, resulting in 10.31% increase in the hacker's bill.
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Leveraging Strategic Detection Techniques for Smart Home Pricing Cyberattacks
IEEE Transactions on Dependable and Secure Computing, 2016Co-Authors: Shiyan Hu, Tsung-yi HoAbstract:In this work, the vulnerability of the electricity pricing model in the smart home system is assessed. Two closely related pricing Cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the Cyberattacker and increasing the peak energy usage in the local community. A single event detection technique which uses support vector regression and impact difference for detecting anomaly pricing is proposed. The detection capability of such a technique is still limited since it does not model the long term impact of pricing Cyberattacks. This motivates us to develop a partially observable Markov decision process based detection algorithm, which has the ingredients such as reward expectation and policy transfer graph to account for the cumulative impact and the potential future impact due to pricing Cyberattacks. Our simulation results demonstrate that the pricing Cyberattack can reduce the Cyberattacker's bill by 34.3 percent at cost of the increase of others’ bill by 7.9 percent, and increase the peak to average ratio (PAR) by 35.7 percent. Furthermore, the proposed long term detection technique has the detection accuracy of more than 97 percent with significant reduction in PAR and bill compared to repeatedly using the single event detection technique.
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The Hierarchical Smart Home Cyberattack Detection Considering Power Overloading and Frequency Disturbance
IEEE Transactions on Industrial Informatics, 2016Co-Authors: Shiyan Hu, Albert Y. ZomayaAbstract:The concept of smart home has recently gained significant popularity. Despite that it offers improved convenience and cost reduction, the prevailing smart home infrastructure suffers from vulnerability due to Cyberattacks. It is possible for hackers to launch Cyberattacks at the community level while causing a large area power system blackout through cascading effects. In this paper, the cascading impacts of two Cyberattacks on the predicted dynamic electricity pricing are analyzed. In the first Cyberattack, the hacker manipulates the electricity price to form peak energy loads such that some transmission lines are overloaded. Those transmission lines are then tripped and the power system is separated into isolated islands due to the cascading effect. In the second Cyberattack, the hacker manipulates the electricity price to increase the fluctuation of the energy load to interfere the frequency of the generators. The generators are then tripped by the protective procedures and cascading outages are induced in the transmission network. The existing technique only tackles overloading Cyberattack while still suffering from the severe limitation in scalability. Therefore, based on partially observable Markov decision processes, a hierarchical detection framework exploring community decomposition and global policy optimization is proposed in this work. The simulation results demonstrate that our proposed hierarchical computing technique can effectively and efficiently detect those Cyberattacks, achieving the detection accuracy of above 98%, while improving the scalability.
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Analysis of production data manipulation attacks in Petroleum Cyber-Physical Systems
2016 IEEE ACM International Conference on Computer-Aided Design (ICCAD), 2016Co-Authors: Xiaodao Chen, Yuchen Zhou, Hong Zhou, Wenchao Li, Shiyan HuAbstract:Petroleum Cyber-Physical System (CPS) marks the beginning of a new chapter of the oil and gas industry. Combining vast computational power with intelligent Computer Aided Design (CAD) algorithms, petroleum CPS is capable of precisely modeling the flow of fluids over the entire petroleum reservoir and leveraging the massive field data remotely collected at the production wells. It provides field operators with valuable insights into the geological structure and remaining reserves of the reservoir for optimizing their operational strategies. Despite such benefits, petroleum CPS is vulnerable to various Cyberattacks that jeopardize the integrity of the field data collected at production wells. Given manipulated field data, CAD software would generate an inaccurate reservoir model which misleads the field operators. This work is the first to analyze potential cybersecurity attacks in a petroleum CPS. In this paper, an intelligent Cyberattack strategy optimization framework is proposed to optimize the malicious manipulation of field data such that the history matching solver generates the most inaccurate reservoir model. Our method is based on the advanced Model Reference Adaptive Search (MRAS) technique, and it can be used to evaluate the worst case impact due to the field data manipulation attacks. Experimental results on a standard petroleum CPS testcase demonstrate that the proposed method can reduce the production quality, measured by the weighted mismatch sum of the bottom hole pressure (BHP), the gas oil ratio (GOR), and the Water Cut (WCT), by up to 99.1% when comparing to a random attack.
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DAC - Impact assessment of net metering on smart home Cyberattack detection
Proceedings of the 52nd Annual Design Automation Conference on - DAC '15, 2015Co-Authors: Shiyan Hu, Jie Wu, Yu Hu, Xiaowei LiAbstract:Despite the increasing popularity of the smart home concept, such a technology is vulnerable to various security threats such as pricing Cyberattacks. There are some technical advances in developing detection and defense frameworks against those pricing Cyberattacks. However, none of them considers the impact of net metering, which allows the customers to sell the excessively generated renewable energy back to the grid. At a superficial glance, net metering seems to be irrelevant to the cybersecurity, while this paper demonstrates that its implication is actually profound. In this paper, we propose to analyze the impact of the net metering technology on the smart home pricing Cyberattack detection. Net metering changes the grid energy demand, which is considered by the utility when designing the guideline price. Thus, Cyberattack detection is compromised if this impact is not considered. It motivates us to develop a new smart home pricing Cyberattack detection framework which judiciously integrates the net metering technology with the short/long term detection. The simulation results demonstrate that our new framework can significantly improve the detection accuracy from 65.95% to 95.14% compared to the state-of-art detection technique.
Jianhui Wang - One of the best experts on this subject based on the ideXlab platform.
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Flexible Machine Learning-Based Cyberattack Detection Using Spatiotemporal Patterns for Distribution Systems
IEEE Transactions on Smart Grid, 2020Co-Authors: Jianhui Wang, Bo ChenAbstract:This letter develops a flexible machine learning detection method for Cyberattacks in distribution systems considering spatiotemporal patterns. Spatiotemporal patterns are recognized by the graph Laplacian based on system-wide measurements. A flexible Bayes classifier (BC) is used to train spatiotemporal patterns which could be violated when Cyberattacks occur. Cyberattacks are detected by using flexible BCs online. The effectiveness of the developed method is demonstrated through standard IEEE 13and 123-node test feeders.
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Machine Learning-Based Anomaly Detection for Load Forecasting Under Cyberattacks
IEEE Transactions on Smart Grid, 2019Co-Authors: Jianhui WangAbstract:Accurate load forecasting can create both economic and reliability benefits for power system operators. However, the Cyberattack on load forecasting may mislead operators to make unsuitable operational decisions for the electricity delivery. To effectively and accurately detect these Cyberattacks, this paper develops a machine learning-based anomaly detection (MLAD) methodology. First, load forecasts provided by neural networks are used to reconstruct the benchmark and scaling data by using the k-means clustering. Second, the Cyberattack template is estimated by the naive Bayes classification based on the cumulative distribution function and statistical features of the scaling data. Finally, the dynamic programming is utilized to calculate both the occurrence and parameter of one Cyberattack on load forecasting data. A widely used symbolic aggregation approximation method is compared with the developed MLAD method. Numerical simulations on the publicly load data show that the MLAD method can effectively detect Cyberattacks for load forecasting data with relatively high accuracy. Also, the robustness of MLAD is verified by thousands of attack scenarios based on Monte Carlo simulation.
Yang Liu - One of the best experts on this subject based on the ideXlab platform.
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Impact assessment of net metering on smart home Cyberattack detection
Proceedings of the 52nd Annual Design Automation Conference on - DAC '15, 2015Co-Authors: Yang Liu, Shiyan Hu, Yier Jin, Yiyu Shi, Jie Wu, Yu Hu, Xiaowei LiAbstract:Despite the increasing popularity of the smart home concept, such a\ntechnology is vulnerable to various security threats such as pricing\nCyberattacks. There are some technical advances in developing detection\nand defense frameworks against those pricing Cyberattacks. However, none\nof them considers the impact of net metering, which allows the customers\nto sell the excessively generated renewable energy back to the grid. At\na superficial glance, net metering seems to be irrelevant to the\ncybersecurity, while this paper demonstrates that its implication is\nactually profound.\nIn this paper, we propose to analyze the impact of the net metering\ntechnology on the smart home pricing Cyberattack detection. Net metering\nchanges the grid energy demand, which is considered by the utility when\ndesigning the guideline price. Thus, Cyberattack detection is\ncompromised if this impact is not considered. It motivates us to develop\na new smart home pricing Cyberattack detection framework which\njudiciously integrates the net metering technology with the short/long\nterm detection. The simulation results demonstrate that our new\nframework can significantly improve the detection accuracy from 65.95%\nto 95.14% compared to the state-of-art detection technique.
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Vulnerability assessment and defense technology for smart home cybersecurity considering pricing Cyberattacks
IEEE ACM International Conference on Computer-Aided Design Digest of Technical Papers ICCAD, 2015Co-Authors: Yang Liu, Shiyan Hu, Tsung-yi HoAbstract:Smart home, which controls the end use of the power grid, has become a critical component in the smart grid infrastructure. In a smart home system, the advanced metering infrastructure (AMI) is used to connect smart meters with the power system and the communication system of a smart grid. The electricity pricing information is transmitted from the utility to the local community, and then broadcast through wired or wireless networks to each smart meter within AMI. In this work, the vulnerability of the above process is assessed. Two closely related pricing Cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the Cyberattacker and increasing the peak energy usage in the local community. A countermeasure technique which uses support vector regression and impact difference for detecting anomaly pricing is then proposed. These pricing Cyberattacks explore the interdependance between the transmitted electricity pricing in the communication system and the energy load in the power system, which are the first such cyber-attacks in the smart home context. Our simulation results demonstrate that the pricing Cyberattack can reduce the attacker's bill by 34.3% at the cost of the increase of others' bill by 7.9% on average. In addition, the pricing Cyberattack can unbalance the energy load of the local power system as it increases the peak to average ratio by 35.7%. Furthermore, our simulation results show that the proposed countermeasure technique can effectively detect the electricity pricing manipulation.
Tsung-yi Ho - One of the best experts on this subject based on the ideXlab platform.
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Leveraging Strategic Detection Techniques for Smart Home Pricing Cyberattacks
IEEE Transactions on Dependable and Secure Computing, 2016Co-Authors: Shiyan Hu, Tsung-yi HoAbstract:In this work, the vulnerability of the electricity pricing model in the smart home system is assessed. Two closely related pricing Cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the Cyberattacker and increasing the peak energy usage in the local community. A single event detection technique which uses support vector regression and impact difference for detecting anomaly pricing is proposed. The detection capability of such a technique is still limited since it does not model the long term impact of pricing Cyberattacks. This motivates us to develop a partially observable Markov decision process based detection algorithm, which has the ingredients such as reward expectation and policy transfer graph to account for the cumulative impact and the potential future impact due to pricing Cyberattacks. Our simulation results demonstrate that the pricing Cyberattack can reduce the Cyberattacker's bill by 34.3 percent at cost of the increase of others’ bill by 7.9 percent, and increase the peak to average ratio (PAR) by 35.7 percent. Furthermore, the proposed long term detection technique has the detection accuracy of more than 97 percent with significant reduction in PAR and bill compared to repeatedly using the single event detection technique.
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Vulnerability assessment and defense technology for smart home cybersecurity considering pricing Cyberattacks
IEEE ACM International Conference on Computer-Aided Design Digest of Technical Papers ICCAD, 2015Co-Authors: Yang Liu, Shiyan Hu, Tsung-yi HoAbstract:Smart home, which controls the end use of the power grid, has become a critical component in the smart grid infrastructure. In a smart home system, the advanced metering infrastructure (AMI) is used to connect smart meters with the power system and the communication system of a smart grid. The electricity pricing information is transmitted from the utility to the local community, and then broadcast through wired or wireless networks to each smart meter within AMI. In this work, the vulnerability of the above process is assessed. Two closely related pricing Cyberattacks which manipulate the guideline electricity prices received at smart meters are considered and they aim at reducing the expense of the Cyberattacker and increasing the peak energy usage in the local community. A countermeasure technique which uses support vector regression and impact difference for detecting anomaly pricing is then proposed. These pricing Cyberattacks explore the interdependance between the transmitted electricity pricing in the communication system and the energy load in the power system, which are the first such cyber-attacks in the smart home context. Our simulation results demonstrate that the pricing Cyberattack can reduce the attacker's bill by 34.3% at the cost of the increase of others' bill by 7.9% on average. In addition, the pricing Cyberattack can unbalance the energy load of the local power system as it increases the peak to average ratio by 35.7%. Furthermore, our simulation results show that the proposed countermeasure technique can effectively detect the electricity pricing manipulation.
Runhe Huang - One of the best experts on this subject based on the ideXlab platform.
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A study on association rule mining of darknet big data
2015 International Joint Conference on Neural Networks (IJCNN), 2015Co-Authors: Tao Ban, Shanqing Guo, Masato Eto, Koji Nakao, Daisuke Inoue, Runhe HuangAbstract:Global darknet monitoring provides an effective way to observe cyber-attacks that are significantly threatening network security and management. In this paper, we present a study on characterization of Cyberattacks in the big stream data collected in a large scale distributed darknet using association rule learning. The experiment shows that association rule learning in the darknet stream data can support strategic Cyberattack countermeasure in the following ways. First, statistics computed from malware-specific rules can lead to better understanding of the global trend of Cyberattacks in the Internet. Second, strong association rules can lead to further insights into the nature of the attacking tools and hence expedite the diagnosis. Then, the discovery of emerging new attacks may lead to early detection and prompt prevention of pandemic incidents, preventing damage to the IT infrastructure and extensive financial loss. Finally, exploring the knowledge in the frequent attacking patterns can enable accurate prediction of future attacks from analyzed hosts, which could improve the performance of honeypot systems to collect more pertinent malware information using limited system and network resources.
