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Journal articles on the topic 'False Report Injection Attack'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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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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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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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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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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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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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.

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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.

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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.

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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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11

Nam, Su Man, and Tae Ho Cho. "A Security Method for Multiple Attacks in Sensor Networks: Against False-Report Injection, False-Vote Injection and Wormhole Attacks." Informatics Engineering, an International Journal 2, no. 4 (2014): 13–29. http://dx.doi.org/10.5121/ieij.2014.2402.

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12

Hu, Pengfei, Wengen Gao, Yunfei Li, Minghui Wu, Feng Hua, and Lina Qiao. "Detection of False Data Injection Attacks in Smart Grids Based on Expectation Maximization." Sensors 23, no. 3 (2023): 1683. http://dx.doi.org/10.3390/s23031683.

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The secure operation of smart grids is closely linked to state estimates that accurately reflect the physical characteristics of the grid. However, well-designed false data injection attacks (FDIAs) can manipulate the process of state estimation by injecting malicious data into the measurement data while bypassing the detection of the security system, ultimately causing the results of state estimation to deviate from secure values. Since FDIAs tampering with the measurement data of some buses will lead to error offset, this paper proposes an attack-detection algorithm based on statistical lear

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13

Jung-sub, Ahn, and Cho Tae-ho. "Fuzzy Logic Optimization Method for Energy Efficiency Improvement of CFFS using GA in WSN." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 6 (2020): 474–80. https://doi.org/10.35940/ijeat.F1524.089620.

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Recently, the range of applications for wireless sensor networks has grown. In industrial applications using data-driven approaches, data reliability is particularly important. However, deployed sensor nodes can be easily damaged due to physical damage or node acquisition factors caused by attackers, and false report injection attacks may occur. CFFS with collaborative verification has been proposed to filter out false reports. The proposed CFFS reduces the probability of a successful attack by separating sensor nodes into clusters. The false report filtering performance in the existing scheme

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Jae, Kwan Lee1 Su Man Nam2 and Tae Ho Cho3. "ENSP: ENERGY EFFICIENT NEXT HOP SELECTION IN A PROBABILISTIC VOTING-BASED FILTERING SCHEME USING FUZZY LOGIC." Informatics Engineering, an International Journal (IEIJ) 02, dec (2014): 01–12. https://doi.org/10.5121/ieij.2014.2401.

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In wireless sensor networks, sensor nodes are easily compromised due to their restricted hardware resources. These compromised nodes inject fabricated votes into legitimate reports, and generate false report and false vote injection attacks. These attacks deplete energy resources and block report transmission. A probabilistic voting-based filtering scheme was proposed to detect the bogus votes in reports en-route to protect against attacks. Although this method detects false votes in intermediate nodes, the sensor network needs to be effectively operated in consideration of a node's condit

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15

Elshazly, Amr A., Islam Elgarhy, Ahmed T. Eltoukhy, et al. "False Data Injection Attacks on Reinforcement Learning-Based Charging Coordination in Smart Grids and a Countermeasure." Applied Sciences 14, no. 23 (2024): 10874. http://dx.doi.org/10.3390/app142310874.

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Reinforcement learning (RL) is proven effective in optimizing home battery charging coordination within smart grids. However, its vulnerability to adversarial behavior poses a significant challenge to the security and fairness of the charging process. In this study, we, first, craft five stealthy false data injection (FDI) attacks that under-report the state-of-charge (SoC) values to deceive the RL agent into prioritizing their charging requests, and then, we investigate the impact of these attacks on the charging coordination system. Our evaluations demonstrate that attackers can increase the

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16

M.Sasirekha, Thaseen .I Sumaiya, and Banu.J Saira. "A Defense Mechanism for Credit Card Fraud Detection." International Journal on Cryptography and Information Security (IJCIS) 2, no. 3 (2019): 89–100. https://doi.org/10.5281/zenodo.3461713.

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Computer security is one of the key areas where lot of research is being done. Many intrusion detection techniques are proposed to ensure the network security, protect network resources and network infrastructures. Intrusion detection systems (IDS) attempt to detect attacks by gathering network data and analyze the information from various areas to identify the possible intrusions. This paper proposes a defense mechanism combining three approaches such as anomaly, misuse and decision making model to produce better detection accuracy and a decreased false positive rate. The defense mechanism ca

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17

M.Sasirekha, Thaseen .I Sumaiya, and Banu.J Saira. "A Defense Mechanism for Credit Card Fraud Detection." International Journal on Cryptography and Information Security (IJCIS) 2, no. 3 (2019): 89–100. https://doi.org/10.5281/zenodo.3587436.

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Computer security is one of the key areas where lot of research is being done. Many intrusion detection techniques are proposed to ensure the network security, protect network resources and network infrastructures. Intrusion detection systems (IDS) attempt to detect attacks by gathering network data and analyze the information from various areas to identify the possible intrusions. This paper proposes a defense mechanism combining three approaches such as anomaly, misuse and decision making model to produce better detection accuracy and a decreased false positive rate. The defense mechanism ca

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18

Muhammad, K. Shahzad1 and Tae Ho Cho1. "AN ENHANCED DETECTION AND ENERGYEFFICIENT EN-ROUTE FILTERING SCHEME IN WIRELESS SENSOR NETWORKS." Informatics Engineering, an International Journal (IEIJ) 03, sep (2015): 01–16. https://doi.org/10.5121/ieij.2015.3302.

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Wireless sensor networks (WSNs), due to their small size, low cost, and untethered communication over a short-range, have great potential for applications and services. Due to hostile environments and an unattended nature, they are prone to many types of attacks by adversaries. False data injection attacks compromise data accuracy at the sink node and cause undesirable energy depletion at the sink and intermediate nodes. In order to detect and counter false data attacks, a number of en-route filtering schemes have been proposed. However, they lack a strong false report detection capacity or ca

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19

Ye-lim, Kang, and Cho Tae-ho. "Fuzzy-based Dynamic Security Parameters Determination Method to Improve Energy Efficiency." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1952–58. https://doi.org/10.35940/ijeat.D9024.049420.

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Fine dust is a harmful substance that floats in the atmosphere. It is not filtered well in the bronchus and causes health problems when it accumulates in the body. Fine dust that is present in the air outside can flow into buildings through windows and other openings, adversely affecting indoor air quality. It has negative health effects on people who live indoors. As attention to problems associated with fine dust gradually rises, the importance of continuously managing the quality of indoor air through air purification rises. Therefore, recently, research and development into systems to peri

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20

B. Kalaiselvi, B. Kalaiselvi, Mannepalle Sai Chandu, Maridhu Narendra, and Mannepalle Deekshith Kumar. "SQL-Injection Vulnerability Scanning Tool for Automatic Creation of SQL-Injection Attacks." International Journal of Advances in Engineering and Management 7, no. 1 (2025): 577–87. https://doi.org/10.35629/5252-0701577587.

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This research introduces an advanced automated scanning tool for detecting and analyzing SQL injection vulnerabilities in web applications, addressing the critical need for robust security testing mechanisms in modern web development. The proposed tool employs sophisticated dynamic analysis techniques combined with machine learning algorithms to automatically generate, execute, and validate SQL injection attack vectors. By implementing a multilayered detection approach, the system first identifies potential injection points through comprehensive input parameter analysis, followed by intelligen

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21

Shahzad, Muhammad K., S. M. Riazul Islam, Kyung-Sup Kwak, and Lewis Nkenyereye. "AEF: Adaptive En-Route Filtering to Extend Network Lifetime in Wireless Sensor Networks." Sensors 19, no. 18 (2019): 4036. http://dx.doi.org/10.3390/s19184036.

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Static sink-based wireless sensor networks (WSNs) suffer from an energy-hole problem. This incurs as the rate of energy consumption on sensor nodes around sinks and on critical paths is considerably faster. State-of-the-art en-routing filtering schemes save energy by countering false report injection attacks. In addition to their unique limitations, these schemes generally do not examine energy awareness in underlying routing. Mostly, these security methods are based on a fixed filtering capacity, unable to respond to changes in attack intensity. Therefore, these limitations cause network part

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22

Yi, Chuanjun, Geng Yang, Hua Dai, Liang Liu, and Ning Li. "Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks." Sensors 18, no. 11 (2018): 3829. http://dx.doi.org/10.3390/s18113829.

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The existing public key-based en-route filtering schemes are vulnerable to report disruption attacks or selective forwarding attacks, and they fail to consider any measure to detect and punish the malicious nodes. The authors propose a series of public key-based security mechanisms for wireless sensor networks (WSNs) in this paper, including a mechanism for verifying the partial signatures, a substitution mechanism, an effective report forwarding protocol, and a trust value-based mechanism to identify and punish the malicious nodes. Finally, the authors develop a public key-based authenticatio

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23

Nam, Su Man, and Tae Ho Cho. "Fuzzy-Based Energy Efficient Method for Multiple Attacks in Sensor Networks: Against False Vote and Report Injection Attacks." International Journal on Cybernetics & Informatics 2, no. 2 (2013): 1–12. http://dx.doi.org/10.5121/ijci.2013.2201.

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24

Liu, Jizhao, Weidong Yang, Junbao Zhang, and Changlin Yang. "Detecting false messages in vehicular ad hoc networks based on a traffic flow model." International Journal of Distributed Sensor Networks 16, no. 2 (2020): 155014772090639. http://dx.doi.org/10.1177/1550147720906390.

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In vehicular ad hoc networks, inside attackers can launch a false information attack by injecting false emergency messages to report bogus events such as traffic accidents. In this article, a false message detection scheme is proposed and evaluated. First, traffic flow theory is employed to analyze vehicular behavior under a traffic accident scenario. It shows that a “bottleneck” phenomenon is triggered because the road capacity is reduced by blocked lanes at an accident site. The traffic parameters, such as vehicular density, exhibit a distinct statistical property compared to an accident-fre

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25

Shahzad, Muhammad K., S. M. Riazul Islam, Mahmud Hossain, Mohammad Abdullah-Al-Wadud, Atif Alamri, and Mehdi Hussain. "GAFOR: Genetic Algorithm Based Fuzzy Optimized Re-Clustering in Wireless Sensor Networks." Mathematics 9, no. 1 (2020): 43. http://dx.doi.org/10.3390/math9010043.

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In recent years, the deployment of wireless sensor networks has become an imperative requisite for revolutionary areas such as environment monitoring and smart cities. The en-route filtering schemes primarily focus on energy saving by filtering false report injection attacks while network lifetime is usually ignored. These schemes also suffer from fixed path routing and fixed response to these attacks. Furthermore, the hot-spot is considered as one of the most crucial challenges in extending network lifetime. In this paper, we have proposed a genetic algorithm based fuzzy optimized re-clusteri

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26

Briland, Mathieu, and Fabrice Bouquet. "An Approach for Testing False Data Injection Attack on Data Dependent Industrial Devices." JUCS - Journal of Universal Computer Science 27, no. 7 (2021): 774–92. http://dx.doi.org/10.3897/jucs.70326.

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False data injection is an attack in which an attacker injects fabricated data into a system with the objective to change the behaviour and the decision-making of the system. Many industrial data-based devices are vulnerable to such attacks, this work presents an approach for testing False Data Injection Attack. This approach uses a Domain-Specific Language to generate altered data with two objectives, to provide sophisticated attacks scenarios to increase the resilience of vulnerable systems against False Data Injection Attack and to train detection tools.

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Briland, Mathieu, and Fabrice Bouquet. "An Approach for Testing False Data Injection Attack on Data Dependent Industrial Devices." JUCS - Journal of Universal Computer Science 27, no. (7) (2021): 774–92. https://doi.org/10.3897/jucs.70326.

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False data injection is an attack in which an attacker injects fabricated data into a system with the objective to change the behaviour and the decision-making of the system. Many industrial data-based devices are vulnerable to such attacks, this work presents an approach for testing False Data Injection Attack. This approach uses a Domain-Specific Language to generate altered data with two objectives, to provide sophisticated attacks scenarios to increase the resilience of vulnerable systems against False Data Injection Attack and to train detection tools.

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28

Rouhani, Seyed Hossein, Hamed Mojallali, and Alfred Baghramian. "Load frequency control in the presence of simultaneous cyber-attack and participation of demand response program." Transactions of the Institute of Measurement and Control 44, no. 10 (2022): 1993–2011. http://dx.doi.org/10.1177/01423312211068645.

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Simultaneous investigation of demand response programs and false data injection cyber-attack are critical issues for the smart power system frequency regulation. To this purpose, in this paper, the output of the studied system is simultaneously divided into two subsystems: one part including false data injection cyder-attack and another part without cyder-attack. Then, false data injection cyber-attack and load disturbance are estimated by a non-linear sliding mode observer, simultaneously and separately. After that, demand response is incorporated in the uncertain power system to compensate t

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Sheng, Jingyuan. "Research on SQL Injection Attack and Defense Technology of Power Dispatching Data Network: Based on Data Mining." Mobile Information Systems 2022 (July 30, 2022): 1–8. http://dx.doi.org/10.1155/2022/6207275.

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In the process of SQL injection attack and defense of power dispatching data network, in order to ensure the accuracy of identification and defense, it is often necessary to build a rule base. However, the scale of the temporarily constructed rule base is limited, which is easy to cause false positives and omissions. Based on the traditional defense, data mining technology is introduced to design a SQL injection attack and defense technology for power dispatching data network. First, the SQL injection attack is analyzed, and the attack flow diagram is obtained as the basis of attack identifica

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Deng, Wenping, Ziyu Yang, Peng Xun, Peidong Zhu, and Baosheng Wang. "Advanced Bad Data Injection Attack and Its Migration in Cyber-Physical Systems." Electronics 8, no. 9 (2019): 941. http://dx.doi.org/10.3390/electronics8090941.

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False data injection (FDI) attack is a hot topic in cyber-physical systems (CPSs). Attackers inject bad data into sensors or return false data to the controller to cause the inaccurate state estimation. Although there exists many detection approaches, such as bad data detector (BDD), sequence pattern mining, and machine learning methods, a smart attacker still can inject perfectly false data to go undetected. In this paper, we focus on the advanced false data injection (AFDI) attack and its detection method. An AFDI attack refers to the attack where a malicious entity accurately and successive

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Ahmed, Mohiuddin. "False Image Injection Prevention Using iChain." Applied Sciences 9, no. 20 (2019): 4328. http://dx.doi.org/10.3390/app9204328.

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The advances in information and communication technology are consistently beneficial for the healthcare sector. A trend in the healthcare sector is the progressive shift in how data are acquired and the storage of such data in different facilities, such as in the cloud, due to the efficiency and effectiveness offered. Digital images related to healthcare are sensitive in nature and require maximum security and privacy. A malicious entity can tamper with such stored digital images to mislead healthcare personnel and the consequences of wrong diagnosis are harmful for both parties. A new type of

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Yu, Xiaoquan, Wei Li, Shuo Li, Yingze Yang, and Jun Peng. "False Data Injection Attack Detection for Virtual Coupling Systems of Heavy-Haul Trains: A Deep Learning Approach." Journal of Advanced Computational Intelligence and Intelligent Informatics 29, no. 1 (2025): 175–86. https://doi.org/10.20965/jaciii.2025.p0175.

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Cooperative control for virtual coupling systems of multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the false data injection attack for the virtual coupling system is a serious obstacle, which will lead to imprecise train operation control. To address this issue, a deep learning-based false data injection attack (FDIA) detection for virtual coupling systems of heavy-haul trains is proposed. First, the cyber-physical model of the virtual coupling system is established. Second, a cooperative control law is designed for the virtual c

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Hua, Yi, Feng Chen, Shuwei Deng, Shukai Duan, and Lidan Wang. "Secure distributed estimation against false data injection attack." Information Sciences 515 (April 2020): 248–62. http://dx.doi.org/10.1016/j.ins.2019.12.016.

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Mitropoulos, Dimitris, and Diomidis Spinellis. "Fatal injection: a survey of modern code injection attack countermeasures." PeerJ Computer Science 3 (November 27, 2017): e136. http://dx.doi.org/10.7717/peerj-cs.136.

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With a code injection attack (CIA) an attacker can introduce malicious code into a computer program or system that fails to properly encode data that comes from an untrusted source. A CIA can have different forms depending on the execution context of the application and the location of the programming flaw that leads to the attack. Currently, CIAs are considered one of the most damaging classes of application attacks since they can severely affect an organisation’s infrastructure and cause financial and reputational damage to it. In this paper we examine and categorize the countermeasures deve

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Wu, Po, Jiangnan Zhang, Shengyao Luo, Yanlou Song, Jiawei Zhang, and Yi Wang. "A Fusion Adaptive Cubature Kalman Filter Approach for False Data Injection Attack Detection of DC Microgrids." Electronics 13, no. 9 (2024): 1612. http://dx.doi.org/10.3390/electronics13091612.

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With the widespread application of information technology in microgrids, microgrids are evolving into a class of power cyber–physical systems (CPSs) that are deeply integrated with physical and information systems. Due to the high dependence of microgrids’ distributed cooperative control on real-time communication and system state information, they are increasingly susceptible to false data injection attacks (FDIAs). To deal with this issue, in this paper, a novel false data injection attack detection method for direct-current microgrids (DC MGs) was proposed, based on fusion adaptive cubature

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Lu, Rentao, and Jie Wang. "Distributed Control for AC Microgrids with False Data Injection Attacks and Time Delays." E3S Web of Conferences 194 (2020): 03023. http://dx.doi.org/10.1051/e3sconf/202019403023.

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Distributed control is widely used in AC microgrids to maintain frequency and voltage stability and internal power balance. However, distributed control need realizes information interaction through communication network, which makes microgrid vulnerable to network attack. A distributed control strategy based on consensus theory is proposed in this paper to enhance the resilience of microgrid to attack. An attack detection and localization method is designed for the false data injection attack. The Artstein’s transformation is introduced to process the delay data and the performance of control

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Tang, Xu, Jing Wang, Xiaohong Ran, Kaipei Liu, and Liang Qin. "False Data Injection Attack Against the Optimal Energy Management of Active Distribution Networks: A Bilevel Programming Model." Journal of Physics: Conference Series 2662, no. 1 (2023): 012006. http://dx.doi.org/10.1088/1742-6596/2662/1/012006.

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Abstract With the wide integration of advanced measurement equipment and communication networks into active distribution networks (ADNs), the ADN cyber-physical system (CPS) is facing the risk of false data injection attacks (FDIAs). This paper proposes a bilevel attack model to obtain optimal false data, which maximizes power losses, photovoltaic (PV) curtailment, and load shedding while ensuring the concealment of the attack. Different from the FDIA for the transmission networks, the FDIA targeting ADNs not only injects false data into load demand but also further injects false data into PV

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Chaudhary, Pooja, B. B. Gupta, and Shashank Gupta. "A Framework for Preserving the Privacy of Online Users Against XSS Worms on Online Social Network." International Journal of Information Technology and Web Engineering 14, no. 1 (2019): 85–111. http://dx.doi.org/10.4018/ijitwe.2019010105.

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This article presents a hybrid framework i.e. OXSSD (Online Social Network-Based XSS-Defender) that explores cross-site scripting (XSS) attack vectors at the vulnerable points in web applications of social networks. Initially, during training phase, it generates the views for each request and formulates the access control list (ACL) which encompasses all the privileges a view can have. It also ascertains all possible injection points for extracting malicious attack vectors. Secondly, during recognition phase, after action authentication XSS attack vectors are retrieved from the extracted injec

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Ye, Jun. "Estimation of False Data Injection Attacks for Load Frequency Control Systems." Journal of Physics: Conference Series 2076, no. 1 (2021): 012093. http://dx.doi.org/10.1088/1742-6596/2076/1/012093.

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Abstract False data injection attacks (FDIAs) against the load frequency control (LFC) system can lead to unstable operation of power systems. In this paper, the estimation problem of the FDIAs for the LFC system in the presence of external disturbances is investigated. The LFC system model under FDIAs against frequency and tie-line power measurements is established. Then an attack estimation scheme combining the attack observer (AO) and technique is proposed to minimize the effect of external disturbance on estimation errors. The uniform boundedness of the state and attack estimation errors i

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Narasimhan, Shilpa, Matthew J. Ellis, and Nael H. El-Farra. "Detection of Multiplicative False Data Injection Cyberattacks on Process Control Systems via Randomized Control Mode Switching." Processes 12, no. 2 (2024): 327. http://dx.doi.org/10.3390/pr12020327.

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A fundamental problem at the intersection of process control and operations is the design of detection schemes monitoring a process for cyberattacks using operational data. Multiplicative false data injection (FDI) attacks modify operational data with a multiplicative factor and could be designed to be detection evading without in-depth process knowledge. In a prior work, we presented a control mode switching strategy that enhances the detection of multiplicative FDI attacks in processes operating at steady state (when process states evolve within a small neighborhood of the steady state). Con

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Al-Marshoud, Mishri Saleh, Ali H. Al-Bayatti, and Mehmet Sabir Kiraz. "Improved Chaff-Based CMIX for Solving Location Privacy Issues in VANETs." Electronics 10, no. 11 (2021): 1302. http://dx.doi.org/10.3390/electronics10111302.

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Safety application systems in Vehicular Ad-hoc Networks (VANETs) require the dissemination of contextual information about the scale of neighbouring vehicles; therefore, ensuring security and privacy is of utmost importance. Vulnerabilities in the messages and the system’s infrastructure introduce the potential for attacks that lessen safety and weaken passengers’ privacy. The purpose of short-lived anonymous identities, called “pseudo-identities”, is to divide the trip into unlinkable short passages. Researchers have proposed changing pseudo-identities more frequently inside a pre-defined are

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Xu, Aidong, Tengyue Zhang, Liang Chen, et al. "Research on False Data Injection Attack in Smart Grid." IOP Conference Series: Earth and Environmental Science 693, no. 1 (2021): 012010. http://dx.doi.org/10.1088/1755-1315/693/1/012010.

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Li, Xiaohang, Chunlin Song, Shaokun Liu, and Jinlong Li. "Adaptive Compensation Control under Constant False Data Injection Attack." IOP Conference Series: Materials Science and Engineering 782 (April 15, 2020): 032072. http://dx.doi.org/10.1088/1757-899x/782/3/032072.

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Lei, L., W. Yang, C. Yang, and H. B. Shi. "False data injection attack on consensus-based distributed estimation." International Journal of Robust and Nonlinear Control 27, no. 9 (2016): 1419–32. http://dx.doi.org/10.1002/rnc.3645.

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Tian, Jiwei, Buhong Wang, and Xia Li. "Data-Driven and Low-Sparsity False Data Injection Attacks in Smart Grid." Security and Communication Networks 2018 (September 26, 2018): 1–11. http://dx.doi.org/10.1155/2018/8045909.

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Recent researches on data-driven and low-sparsity data injection attacks have been presented, respectively. To combine the two main goals (data-driven and low-sparsity) of research, this paper presents a data-driven and low-sparsity false data injection attack strategy. The proposed attacking strategy (EID: Eliminate-Infer-Determine) is divided into three stages. In the first step, the intercepted data is preprocessed by sparse optimization techniques to eliminate the outliers. The recovered data is then exploited to learn about the system matrix based on the parallel factorization algorithm i

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Shang, Fute, Buhong Wang, Fuhu Yan, and Tengyao Li. "Multidevice False Data Injection Attack Models of ADS-B Multilateration Systems." Security and Communication Networks 2019 (March 3, 2019): 1–11. http://dx.doi.org/10.1155/2019/8936784.

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Location verification is a promising approach among various ADS-B security mechanisms, which can monitor announced positions in ADS-B messages with estimated positions. Based on common assumption that the attacker is equipped with only a single device, this mechanism can estimate the position state through analysis of time measurements of messages using multilateration algorithm. In this paper, we propose the formal model of multidevice false data injection attacks in the ATC system against the location verification. Assuming that attackers equipped with multiple devices can manipulate the ADS

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Hu, Jiamin, Xiaofan Yang, and Luxing Yang. "A Novel Diagnosis Scheme against Collusive False Data Injection Attack." Sensors 23, no. 13 (2023): 5943. http://dx.doi.org/10.3390/s23135943.

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The collusive false data injection attack (CFDIA) is a false data injection attack (FIDA), in which false data are injected in a coordinated manner into some adjacent pairs of captured nodes of an attacked wireless sensor network (WSN). As a result, the defense of WSN against a CFDIA is much more difficult than defense against ordinary FDIA. This paper is devoted to identifying the compromised sensors of a well-behaved WSN under a CFDIA. By establishing a model for predicting the reading of a sensor and employing the principal component analysis (PCA) technique, we establish a criterion for ju

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Su, Man Nam, and Ho Cho Tae. "A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS." Advances in Vision Computing: An International Journal (AVC) 3, no. 3 (2016): 09–16. https://doi.org/10.5281/zenodo.3518809.

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In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative a

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Su, Man Nam, and Ho Cho Tae. "A METHOD FOR DETECTING FALSE POSITIVE AND FALSE NEGATIVE ATTACKS USING SIMULATION MODELS IN STATISTICAL ENROUTE FILTERING BASED WSNS." Advances in Vision Computing: An International Journal (AVC) 3, no. 3 (2016): 09–16. https://doi.org/10.5281/zenodo.3689099.

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In wireless sensor networks, adversaries compromise sensor nodes to damage the network though potential threats such as false positive and false negative attacks. The false positive attacks cause energy drain and false alarms, and false negative attacks generate information loss. To address the false positive attacks in the sensor network, a statistical en-route filtering (SEF) detects the false report in intermediate nodes. Even though the scheme detects the false report against the false positive attack, it is difficult to detect false MACs in a legitimate report against the false negative a

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Xia, Meng, Dajun Du, Minrui Fei, Xue Li, and Taicheng Yang. "A Novel Sparse Attack Vector Construction Method for False Data Injection in Smart Grids." Energies 13, no. 11 (2020): 2940. http://dx.doi.org/10.3390/en13112940.

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To improve the security of smart grids (SGs) by finding the system vulnerability, this paper investigates the sparse attack vectors’ construction method for malicious false data injection attack (FDIA). The drawbacks of the existing attack vector construction methods include avoiding discussing the feasible region and validity of the attack vector. For the above drawbacks, this paper has three main contributions: (1) To construct the appropriate attack evading bad data detection (BDD), the feasible region of the attack vector is proved by projection transformation theory. The acquisition of th

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