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Relevant bibliographies by topics / Statistical En-route Filtering

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Academic literature on the topic 'Statistical En-route Filtering'

Author: Grafiati

Published: 3 June 2025

Last updated: 22 June 2025

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Journal articles on the topic "Statistical En-route Filtering"

1

Akram, Muhammad, Muhammad Ashraf, and Tae Ho Cho. "Enhancing the Statistical Filtering Scheme to Detect False Negative Attacks in Sensor Networks." Sukkur IBA Journal of Computing and Mathematical Sciences 1, no. 1 (2017): 52. http://dx.doi.org/10.30537/sjcms.v1i1.7.

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In this paper, we present a technique that detects both false positive and false negative attacks in statistical filtering-based wireless sensor networks. In statistical filtering scheme, legitimate reports are repeatedly verified en route before they reach the base station, which causes heavy energy consumption. While the original statistical filtering scheme detects only false reports, our proposed method promises to detect both attacks.

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2

Fan Ye, H. Luo, Songwu Lu, and Lixia Zhang. "Statistical en-route filtering of injected false data in sensor networks." IEEE Journal on Selected Areas in Communications 23, no. 4 (2005): 839–50. http://dx.doi.org/10.1109/jsac.2005.843561.

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3

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 should be set according to the network environments. In this paper, we propose a security period update method using fuzzy logic in order to improve the lifetime of the network in the statistical en-route filtering approach based on a wireless sensor network of the cluster environment. Normally SEF thresholds should be changed by a user according to the network environment. Our proposed method allows automatically setting the effective threshold for the environment by fuzzy logic. The experimental results show that the energy efficiency increased by 26.5%.

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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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Abstract:

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 should be set according to the network environments. In this paper, we propose a security period update method using fuzzy logic in order to improve the lifetime of the network in the statistical en-route filtering approach based on a wireless sensor network of the cluster environment. Normally SEF thresholds should be changed by a user according to the network environment. Our proposed method allows automatically setting the effective threshold for the environment by fuzzy logic. The experimental results show that the energy efficiency increased by 26.5%.

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5

Watanabe, Yuji. "Performance evaluation of immunity-based statistical en-route filtering in wireless sensor networks." Artificial Life and Robotics 16, no. 3 (2011): 422–25. http://dx.doi.org/10.1007/s10015-011-0969-x.

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Ahn, Jung-Sub, and Tae-Ho Cho. "A CORRELATION ANALYSIS OF THE MAC LENGTH IN STATISTICAL EN-ROUTE FILTERING BASED WSNS." International Journal of Advanced Research 4, no. 8 (2016): 1844–50. http://dx.doi.org/10.21474/ijar01/1399.

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7

Nam, SuMan, and TaeHo Cho. "CONTEXT-AWARE ARCHITECTURE FOR STATISTICAL EN-ROUTE FILTERING TO IDENTIFY COMPROMISED NODES IN WIRELESS SENSOR NETWORKS." International Journal of Advanced Research 4, no. 8 (2016): 1662–68. http://dx.doi.org/10.21474/ijar01/1377.

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8

Nam, Su-Man, Chung-Il Sun, and Tae-Ho Cho. "The Secure Path Cycle Selection Method for Improving Energy Efficiency in Statistical En-route Filtering Based WSNs." Journal of the Korea Society for Simulation 20, no. 4 (2011): 31–40. http://dx.doi.org/10.9709/jkss.2011.20.4.031.

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9

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 attack in the SEF-based WSN. Our proposed method effectively detects the false positive and false negative attacks in the sensor network through a simulation model. The experimental results indicate that the proposed method increase detection power while maintaining the energy consumption of the network against the false positive and false negative attacks.

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10

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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Abstract:

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 attack in the SEF-based WSN. Our proposed method effectively detects the false positive and false negative attacks in the sensor network through a simulation model. The experimental results indicate that the proposed method increase detection power while maintaining the energy consumption of the network against the false positive and false negative attacks.

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Book chapters on the topic "Statistical En-route Filtering"

1

Yang, Feng, Xuehai Zhou, and Qiyuan Zhang. "Multi-Dimensional Resilient Statistical En-Route Filtering in Wireless Sensor Networks." In Advances in Grid and Pervasive Computing. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13067-0_17.

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2

Watanabe, Yuji. "An Immunity-Based Scheme for Statistical En-route Filtering in Wireless Sensor Networks." In Knowledge-Based and Intelligent Information and Engineering Systems. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15393-8_74.

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3

Choi, Hyeon Myeong, and Tae Ho Cho. "Energy Efficient MAC Length Determination Method for Statistical En-Route Filtering Using Fuzzy Logic." In Emerging Intelligent Computing Technology and Applications. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04070-2_74.

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Yang, Xinyu, Jie Lin, Wei Yu, Xinwen Fu, Genshe Chen, and Erik P. Blasch. "On Situational Aware En-Route Filtering against Injected False Data in Cyber Physical Systems." In Situational Awareness in Computer Network Defense. IGI Global, 2012. http://dx.doi.org/10.4018/978-1-4666-0104-8.ch015.

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Cyber-physical systems (CPS) are systems with a tight coupling of the cyber aspects of computing and communications with the physical aspects of dynamics and engineering that abide by the laws of physics. The real-time monitoring provided by wireless sensor networks (WSNs) is essential for CPS, as it provides rich and pertinent information on the condition of physical systems. In WSNs, the attackers could inject false measurements to the controller through compromised sensor nodes, which not only threaten the security of the system, but also consume significant network resources and pose serious threats to the lifetime of sensor networks. To mitigate false data injection (FDI) measurement attacks, a number of situation aware en-route filtering schemes to filter false data inside the networks have been developed. In this book chapter, the authors first review those existing situation aware en-route filter mechanisms such as: Statistical En-route Filtering (SEF), Location-Based Resilient Secrecy (LBRS), Location-ware End-to-end Data Security (LEDS), and Dynamic En-route Filtering Scheme (DEFS). The authors then compare the performance of those schemes via both the theoretical analysis and simulation study. These extensive simulations validate findings that most of the schemes can filter out false data within few hops, and the filtering efficiency increases as the number of hops increases and the filtering efficiency of most schemes decreases rapidly as the number of compromised nodes increases.

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Conference papers on the topic "Statistical En-route Filtering"

1

Yu, L., and J. Li. "Grouping-Based Resilient Statistical En-Route Filtering for Sensor Networks." In 2009 Proceedings IEEE INFOCOM. IEEE, 2009. http://dx.doi.org/10.1109/infcom.2009.5062098.

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Watanabe, Yuji. "An analysis of immunity-based statistical en-route filtering in wireless sensor networks." In the International Conference. ACM Press, 2011. http://dx.doi.org/10.1145/2077489.2077536.

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3

Hyuk Park, Chung Il Sun, and Tae Ho Cho. "A secure path determination method for statistical en-route filtering based wireless sensor network." In 2010 3rd International Conference on Advanced Computer Theory and Engineering (ICACTE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icacte.2010.5579490.

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4

Ahn, Jung-Sub, and Tae-Ho Cho. "A Security Period Update Method Using Evaluation Function for Improving Energy Efficiency of Statistical En-Route Filtering Based WSNs." In 3rd International Conference on Artificial Intelligence and Soft Computing. Academy & Industry Research Collaboration Center (AIRCC), 2017. http://dx.doi.org/10.5121/csit.2017.71009.

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