Готові списки джерел за темами / False Report Injection Attack

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Добірка наукової літератури з теми "False Report Injection Attack"

Автор: Grafiati
Опубліковано: 3 червня 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "False Report Injection Attack"

1

Jungsub, Ahn, and Cho Taeho. "An Enhancement of Cluster-Based False Data Filtering Scheme Through Dynamic Security Selection in Wireless Sensor Networks." International Journal of Computer Networks & Communications (IJCNC) 11, no. 2 (2019): 83–94. https://doi.org/10.5281/zenodo.3233335.

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Анотація:
Today, wireless sensor networks (WSNs) are applied to various industries such as building automation, medical, security, intelligent agriculture, and disaster monitoring. A WSN consists of hundreds to thousands of tiny sensor nodes that perform monitoring tasks. A small sensor node has a limited amount of internal memory and energy resources. Sensor nodes are used to detect a variety of data in specific environmental areas. As a result, WSN should be energy efficient. Sensor nodes are vulnerable to false report injection attacks because they are deployed in an open environment. A false report
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2

Santhosh, Gayathri, and Yogesh Palanichamy. "Rank-Based Report Filtering Scheme (RRFS) for Verifying Phoney Reports in Wireless Sensor Networks." Wireless Communications and Mobile Computing 2020 (September 15, 2020): 1–22. http://dx.doi.org/10.1155/2020/2785960.

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Анотація:
Wireless sensor networks (WSNs) are open to false data injection attack when they are deployed in hostile scenarios. Attackers can easily deceive the sink by compromising sensing nodes or by injecting phoney data into the network. Such attacks can deplete the energy resources of the network by providing wrong information which in turn can affect the proper network functioning or sometimes can shut the network from further functioning. The existing schemes that deal with this problem focus on only a few aspects of the false data injection attack. To resolve this problem, we propose a Rank-based
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3

Sanghyeok, Lim, and Cho Taeho. "Improvement of False Report Detection Performance Based on Invalid Data Detection Using Neural Network in WSN." International Journal of Computer Networks & Communications (IJCNC) 10, no. 6 (2018): 21–34. https://doi.org/10.5281/zenodo.2349310.

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Анотація:
WSN consists of a number of nodes and base stations and is used for event monitoring in various fields such as war situations, forest fires, and home networks. WSN sensor nodes are placed in fields that are difficult for users to manage. It is therefore vulnerable to attackers, and attackers can use false nodes or MAC injection attacks through the hijacked nodes to reduce the lifetime of the network or trigger false alarms. In order to prevent such attacks, several security protocols have been proposed, and all of them have been subjected to MAC-dependent validation, making it impossible to de
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4

Lee, Hyun Woo, and Tae Ho Cho. "An Effective Control Report Based Security Countermeasure against the Joint Attacks of False Report Injection Attack and Selective Forwarding Attack." Wireless Sensor Network 04, no. 08 (2012): 185–90. http://dx.doi.org/10.4236/wsn.2012.48027.

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5

Tae-Ho, Cho, and Ahn Jung-Sub. "METHOD FOR THE PERIOD DETERMINATION OF SECURITY LEVEL UPDATE IN STATISTICAL EN-ROUTE FILTERING." International Journal of Research - Granthaalayah 5, no. 11 (2017): 158–67. https://doi.org/10.5281/zenodo.1069419.

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Анотація:
Energy management of WSN is one of the major issues. Many kind of attacks in WSN paralyze the network by exhausting node energy. Especially false report insertion attack, which is one of the several WSN attacks, is to inform users of false alarms as well as unnecessary energy consumption. F. Ye et al. proposed statistical en-route filtering to prevent false report injection attacks. In order to effectively use their scheme, techniques for determining thresholds using fuzzy logic have been studied. To effectively apply these techniques to the network, an appropriate security level period update
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6

Su, Man Nam1 and Tae Ho Cho2. "A SECURITY METHOD FOR MULTIPLE ATTACKS IN SENSOR NETWORKS: AGAINST FALSE-REPORT INJECTION, FALSE-VOTE INJECTION, AND WORMHOLE ATTACKS." A SECURITY METHOD FOR MULTIPLE ATTACKS IN SENSOR NETWORKS: AGAINST FALSE-REPORT INJECTION, FALSE-VOTE INJECTION, AND WORMHOLE ATTACKS 02, dec (2014): 01–17. https://doi.org/10.5121/ieij.2014.2402.

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Анотація:
In a large-scale wireless sensor network, damage spreads rapidly in the network when under false report injection, false votes injection, or wormhole attacks. These attacks cause finite energy resources to be drained, legitimate reports to be dropped, and data to be intercepted by adversary nodes. A probabilistic voting-based filtering scheme (PVFS) and localized encryption and authentication protocol (LEAP) can be used to cope with these attacks. When multiple attacks occur simultaneously, PVFS and LEAP should be operated together. But the concurrent application of PVFS and LEAP provides inef
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7

Nam, Su Man, and Tae Ho Cho. "A Security Method for Multiple Attacks in Sensor Networks: Against the False Report Injection Attack and the Sinkhole Attack." International Journal on Information Theory 2, no. 4 (2013): 15–33. http://dx.doi.org/10.5121/ijit.2014.2402.

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8

Sang-Hyeok, Lim, and Cho Tae-Ho. "WSN Lifetime Extension Using GA Optimised Fuzzy Logic." International Journal of Computer Science & Information Technology (IJCSIT) 9, no. 5 (2017): 1–14. https://doi.org/10.5121/ijcsit.2017.9501.

Повний текст джерела
Анотація:
A wireless sensor network (WSN) consists of multiple sensor nodes and base stations that collect information from widely deployed sensors. However, the disadvantage is that WSNs are randomly distributed in an open environment, which makes them difficult to manage individually and more easily found and compromised by an attacker. An attacker can execute a false report insertion or invalid vote insertion attack through a compromised node. The Probabilistic Voting Filtering System (PVFS) is a system that prevents these two types of attacks. Before sending a report, the proposed method probabilist
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9

Cho, Tae-Ho, and Jung-Sub Ahn. "METHOD FOR THE PERIOD DETERMINATION OF SECURITY LEVEL UPDATE IN STATISTICAL EN-ROUTE FILTERING." International Journal of Research -GRANTHAALAYAH 5, no. 11 (2017): 158–67. http://dx.doi.org/10.29121/granthaalayah.v5.i11.2017.2340.

Повний текст джерела
Анотація:
Energy management of WSN is one of the major issues. Many kind of attacks in WSN paralyze the network by exhausting node energy. Especially false report insertion attack, which is one of the several WSN attacks, is to inform users of false alarms as well as unnecessary energy consumption. F. Ye et al. proposed statistical en-route filtering to prevent false report injection attacks. In order to effectively use their scheme, techniques for determining thresholds using fuzzy logic have been studied. To effectively apply these techniques to the network, an appropriate security level period update
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10

Yi, Chuanjun. "En-Route Message Authentication Scheme for Filtering False Data in WSNs." Security and Communication Networks 2021 (September 11, 2021): 1–18. http://dx.doi.org/10.1155/2021/4068507.

Повний текст джерела
Анотація:
In wireless sensor networks, the adversary can easily control the compromised nodes to inject false data reports. En-route filtering is an effective mechanism to resist such attacks, where the forwarding nodes of the reports can identify and drop the false reports. However, the existing en-route filtering strategies are vulnerable to report disruption attacks and selective forwarding attacks, and the probabilities and efficiencies of en-route filtering false reports are low. To address these problems, a precheck mechanism performed by the CoS (Center-of-Stimulus) node is presented to resist re
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Більше джерел

Дисертації з теми "False Report Injection Attack"

1

Lu, Jingyang. "A Study on False Information Injection Attack on Dynamic State Estimation in Multi-Sensor Systems." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3789.

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Анотація:
In this thesis, the impact of false information injection is investigated for linear dynamic systems with multiple sensors. It is assumed that the Kalman filter system is unaware of the existence of false information and the adversary is trying to maximize the negative effect of the false information on the Kalman filter's estimation performance. First, a brief introduction to the Kalman filter is shown in the thesis. We mathematically characterize the false information attack under different conditions. For the adversary, many closed-form results for the optimal attack strategies that maximiz
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2

Nemanja, Živković. "Detekcija malicioznih napada na elektroenergetski sistem korišćenjem sinergije statičkog i dinamičkog estimatora stanja." Phd thesis, Univerzitet u Novom Sadu, Fakultet tehničkih nauka u Novom Sadu, 2019. https://www.cris.uns.ac.rs/record.jsf?recordId=108186&source=NDLTD&language=en.

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Анотація:
U ovoj doktorskoj disertaciji predložena je nova metoda za detekciju malicioznih napada injektiranjem loših merenja na elektroenergetski sistem. Predloženi algoritam baziran je na sinergiji statičke i dinamičke estimacije stanja, i u stanju je da detektuje ovaj tip napada u realnom vremenu, za najkritičniji scenario gde napadač ima potpuno znanje o sistemu, i neograničen pristup resursima.<br>This PhD thesis proposes a novel method for detection of malicious false datainjection attacks on power system. The proposed algorithm is based onsynergy between static and dynamic state estimators, and i
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3

Zong-HanYu and 余宗翰. "False Data Injection Attack Using PCA Approximation Method in Smart Grid." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/27281861886127354515.

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Анотація:
碩士<br>國立成功大學<br>工程科學系碩博士班<br>101<br>In smart grid, the security problems become more and more important owing to the vulnerability of two-way communication. Among them, the false data injection attack (FDIA) is the most common issue. Some people try to tackle these problems from protectors’ perspective, while others stand on the opposite side. Recently, advanced FDIA algorithms were proposed without needing the prior knowledge of the power system. But they still have some restrictions. For example, the states of power system need to be non-Gaussian distributed. This restriction limits advance
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4

Chun-HungLee and 李駿宏. "False Data Injection Attack using Linear Regression under AC and DC State Estimation in Smart Grid." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/bvck9r.

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Анотація:
碩士<br>國立成功大學<br>工程科學系<br>104<br>The information protection issues are becoming increasingly important in smart grid. In addition to explore the protective methods, some scholars explore attacking methods. One of the most representative of those methods is the false data injection attacks (false data injection attack, FDIA). Someone suggested that the use of principal component analysis for the advanced false data injection attacks─do not need to know the architecture of the power system and the status of signal is arbitrary distribution. However, this method is still has some limitations, such
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5

Manandhar, Kebina. "Enhanching Security in the Future Cyber Physical Systems." 2015. http://scholarworks.gsu.edu/cs_diss/96.

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Анотація:
Cyber Physical System (CPS) is a system where cyber and physical components work in a complex co-ordination to provide better performance. By exploiting the communication infrastructure among the sensors, actuators, and control systems, attackers may compromise the security of a CPS. In this dissertation, security measures for different types of attacks/ faults in two CPSs, water supply system (WSS) and smart grid system, are presented. In this context, I also present my study on energy management in Smart Grid. The techniques for detecting attacks/faults in both WSS and Smart grid system adop
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6

"Reliability Enhancements for Real-Time Operations of Electric Power Systems." Doctoral diss., 2017. http://hdl.handle.net/2286/R.I.46341.

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Анотація:
abstract: The flexibility in power system networks is not fully modeled in existing real-time contingency analysis (RTCA) and real-time security-constrained economic dispatch (RT SCED) applications. Thus, corrective transmission switching (CTS) is proposed in this dissertation to enable RTCA and RT SCED to take advantage of the flexibility in the transmission system in a practical way. RTCA is first conducted to identify critical contingencies that may cause violations. Then, for each critical contingency, CTS is performed to determine the beneficial switching actions that can reduce post-con
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Частини книг з теми "False Report Injection Attack"

1

Kokkonda, Jayasree, and M. Jahnavi. "Controllable False Data Injection Attack." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8043-3_157.

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2

Mouli, K., P. Arun, R. Vaitheeswari, and S. Sridhar. "False Data Injection Attack Prediction Using Federated Deep Learning Approach." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88237-1_6.

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3

Anwar, Adnan, and Abdun Naser Mahmood. "Vulnerabilities of Smart Grid State Estimation Against False Data Injection Attack." In Renewable Energy Integration. Springer Singapore, 2014. http://dx.doi.org/10.1007/978-981-4585-27-9_17.

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4

Pati, Alok Kumar, and Debidasi Mohanty. "Fuzzy-Based Resilient Control of Microgrid Under False Data Injection Attack." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. https://doi.org/10.1007/978-981-97-7018-2_16.

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5

Ren, Wanwan, Jun Peng, Xiaoquan Yu, et al. "False Data Injection Attack Detection for Virtual Coupling Heavy-Haul Trains." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3332-3_15.

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6

Fu, Chengli, Lin Zhou, Siyuan Chen, Yi Wu, and Yong Wang. "False Data Injection Attack Detection Method Based on Long Time Series Prediction." In Communications in Computer and Information Science. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-8746-3_14.

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7

Niu, Luyao, and Andrew Clark. "A Framework for Joint Attack Detection and Control Under False Data Injection." In Lecture Notes in Computer Science. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32430-8_21.

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8

Ning, Chuanyi, and Zhiyu Xi. "Window Type Detector for Stealthy False Data Injection Attack in Cyber-Physical Systems." In Lecture Notes in Electrical Engineering. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-6613-2_223.

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9

Guo, Xiaoli, Shiyuan Wang, Yuhan Sun, Tieli Sun, Li Feng, and Zhexing Jin. "Detection of False Data Injection Attack in Power Grid Based on Machine Learning." In Advances in Intelligent Information Hiding and Multimedia Signal Processing. Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-6420-2_45.

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10

Anwar, Adnan, Abdun Naser Mahmood, and Mohiuddin Ahmed. "False Data Injection Attack Targeting the LTC Transformers to Disrupt Smart Grid Operation." In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-23802-9_20.

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Тези доповідей конференцій з теми "False Report Injection Attack"

1

Kukadiya, Purna, Trapti Jain, and Neminath Hubballi. "Weibull Distribution based False Data Injection Attack Detection." In 2024 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 2024. http://dx.doi.org/10.1109/pesgm51994.2024.10688506.

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2

Yan, Minrui, Zhaoxiang Ma, Yiqing Liu, Lingxia Lu, Zhejing Bao, and Miao Yu. "Interval Estimation-Based Detection of the False Data Injection Attack." In 2024 11th International Conference on Power and Energy Systems Engineering (CPESE). IEEE, 2024. https://doi.org/10.1109/cpese62584.2024.10840764.

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3

Yang, Nachuan, Xiaoxu Lyu, and Ling Shi. "Model-Free False Data Injection Attack via Eavesdropping: An Online Approach." In 2024 18th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2024. https://doi.org/10.1109/icarcv63323.2024.10821499.

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4

Singh, Kamal, and M. Sailaja Kumari. "Voltage Stability Assessment in Smart Electric Grid Under False Data Injection Attack." In 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT). IEEE, 2024. http://dx.doi.org/10.1109/icccnt61001.2024.10725537.

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5

Fu, Yiping, Jianfeng Yang, Chengyang Jia, and Jiawei Wang. "False data injection attack detection of power grid based on IWOA-BiLSTM." In 2024 6th International Conference on Energy, Power and Grid (ICEPG). IEEE, 2024. https://doi.org/10.1109/icepg63230.2024.10775723.

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6

Zhou, Tai, Qingle Pang, Songyi Han, and Feng Zhang. "Detection of False Data Injection Attack in Smart Grid Based on ASTCKF." In 2024 8th International Conference on Electrical, Mechanical and Computer Engineering (ICEMCE). IEEE, 2024. https://doi.org/10.1109/icemce64157.2024.10862293.

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7

Yang, Yingze, Yihan Tang, Rui Zhang, et al. "GAN based Resilience Recovery for False Data Injection Attack in Smart Grids." In 2024 IEEE International Conference on High Performance Computing and Communications (HPCC). IEEE, 2024. https://doi.org/10.1109/hpcc64274.2024.00238.

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8

Kang, Yelim, and Taeho Cho. "False Report Injection And Remote Attack Detection Method using Security Trajectory in WSN-based Fire Detection IoT." In AICCC 2023: 2023 6th Artificial Intelligence and Cloud Computing Conference. ACM, 2023. http://dx.doi.org/10.1145/3639592.3639616.

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9

Han, Wen, Hong Wen, Peiyao Wang, and Yanxu Zhu. "Microgrid Scheduling Optimization under False Data Injection Attack." In 2022 International Conference on Computing, Communication, Perception and Quantum Technology (CCPQT). IEEE, 2022. http://dx.doi.org/10.1109/ccpqt56151.2022.00033.

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Huang, Qiushi, and Chenye Wu. "Boosting False Data Injection Attack Detection with Structural Knowledge." In 2022 American Control Conference (ACC). IEEE, 2022. http://dx.doi.org/10.23919/acc53348.2022.9867695.

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