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

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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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

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4

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

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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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6

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 a

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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 a

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11

An, Ga-Hyeon, and Tae-Ho Cho. "Wormhole Detection Using Encrypted Node IDs and Hop Counts in the Event Report of Statistical En-Route Filtering." International Journal of Computer Networks and Applications 8, no. 4 (2021): 390. http://dx.doi.org/10.22247/ijcna/2021/209705.

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12

Nam, Su Man, Chung Il Sun, and Tae Ho Cho. "Secure Path Cycle Selection Method Using Fuzzy Logic System for Improving Energy Efficiency in Statistical En-Route Filtering Based WSNs." Wireless Sensor Network 03, no. 11 (2011): 357–61. http://dx.doi.org/10.4236/wsn.2011.311041.

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13

Hae, Young Lee, and Ho Cho Tae. "Fuzzy based Security Threshold Determining for the Statistical En-Route Filtering in Sensor Networks." February 22, 2008. https://doi.org/10.5281/zenodo.1060785.

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In many sensor network applications, sensor nodes are deployed in open environments, and hence are vulnerable to physical attacks, potentially compromising the node's cryptographic keys. False sensing report can be injected through compromised nodes, which can lead to not only false alarms but also the depletion of limited energy resource in battery powered networks. Ye et al. proposed a statistical en-route filtering scheme (SEF) to detect such false reports during the forwarding process. In this scheme, the choice of a security threshold value is important since it trades off detection power

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14

Hong, Taek Ho. "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). https://doi.org/10.5281/zenodo.14227407.

Full text

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.

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15

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." September 30, 2016. https://doi.org/10.5121/avc.2016.3302.

Full text

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 a

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16

"Node Burden-based Load-balancing Management Method for Extended Network Lifetimes of WSNs." International Journal of Engineering and Advanced Technology 9, no. 1 (2019): 5602–7. http://dx.doi.org/10.35940/ijeat.a2248.109119.

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The sensor nodes of a wireless sensor networks (WSNs) are difficult to replace when they fails after deployment. Sensor nodes are small and low-cost due to limited resources and are vulnerable to several types of application attacks. To solve this problem, many security protocols have been meticulously researched considering the energy efficiency and network attacks. The statistical en-route filtering (SEF) scheme has been proposed to detect bogus data and false report injection attacks that reduce network life. SEF performs early filtering of modulated data using the en-route technique. Deplo

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17

CHONG, CHONG TaeKeum. "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). https://doi.org/10.5281/zenodo.14044857.

Full text

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 a

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