Optimized Fuzzy Adaptive Filtering for Ubiquitous Sensor Networks

Lee, Hae Young; Cho, Tae Ho

doi:10.1587/transcom.e94.b.1648

Cited by 13 publications

(17 citation statements)

References 10 publications

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“…If the output is SEF or IHA, it activates the chosen countermeasure against the attack simultaneously. Figure 3 shows the fuzzy membership functions, which were optimized by the genetic algorithm-based optimization method (GAOM) [5] with the consideration of the characteristics of a given network. Note that the membership functions for OF are fixed (i.e., target-independent) and thus not determined by GAOM.…”

Section: Fuzzy-based Adaptive Countering Methodsmentioning

confidence: 99%

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Fuzzy-Based Adaptive Countering Method against False Data Injection Attacks in Wireless Sensor Networks

Lee

2015

IEICE Trans. Inf. & Syst.

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SUMMARYThis letter presents a method to adaptively counter false data injection attacks (FDIAs) in wireless sensor networks, in which a fuzzy rule-based system detects FDIAs and chooses the most appropriate countermeasures. The method does not require en-route verification processes and manual parameter settings. The effectiveness of the method is shown with simulation results. key words: wireless sensor networks, network security, intrusion detection, false data injection attack, fuzzy logic

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Section: Fuzzy-based Adaptive Countering Methodsmentioning

confidence: 99%

“…Thanks to the adaptive selection of countermeasures, it can save extra energy resources also in the case of FDIAs. Moreover, the membership functions of its fuzzy system can be determined automatically [5], so that it does not need manual parameter settings. Simulation results show the effectiveness of the method.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Adaptive Countering Method against False Data Injection Attacks in Wireless Sensor Networks

Lee

2015

IEICE Trans. Inf. & Syst.

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“…However, it is not possible to guarantee that these parameters will always be selected by some "omniscient" experts for every WSN application. By contrast, a major benefit of the proposed method is that the two fuzzy systems, which are the essential components used for FEIA detection and countermeasure selection, can be optimized automatically by combining a simulation and a genetic algorithm [16]. To optimize the fuzzy systems for a WSN, users only have to build a simulation model of the WSN, which can be obtained using a GUI-based tool.…”

Section: Optimization Of Fuzzy Membership Functionsmentioning

confidence: 99%

“…The proposed method has the following merits compared with ACS, which is another centralized solution. (1) The fuzzy systems can be optimized automatically by combining a genetic algorithm with a simulation [16], which virtually eliminates the manual parameter settings used in ACS. (2) Fewer input factors are required to detect FEIAs and to select the countermeasures, which reduces the space requirements and computation complexity.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy-Based Adaptive Countering Method against False Endorsement Insertion Attacks in Wireless Sensor Networks

Lee

2015

International Journal of Distributed Sensor Networks

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Wireless sensor networks (WSNs) are vulnerable to false endorsement insertion attacks (FEIAs), where a malicious adversary intentionally inserts incorrect endorsements into legitimate sensing reports in order to block notifications of real events. A centralized solution can detect and adaptively counter FEIAs while conserving the energy of the forwarding nodes because it does not make the nodes verify reports using cryptographic operations. However, to apply this solution to a WSN, the users must carefully select 10 or more security parameters, which are used to determine the occurrences of FEIAs. Thus, an inappropriate choice of a single parameter might result in the misinterpretation of or misdetection of FEIAs. Therefore, the present study proposes a fuzzybased centralized method for detecting and adaptively countering FEIAs in dense WSNs, where two fuzzy rule-based systems are used to detect an FEIA and to select the most effective countermeasure against the FEIA. A major benefit of the proposed method is that the fuzzy systems can be optimized automatically by combining a genetic algorithm and a simulation. Thus, users only need to write a model of the WSN to apply the proposed method to a WSN. The improved performance with this method is demonstrated by simulation results.

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“…These spurious packets are launched into the network using the compromised nodes with the goal of changing routing paths and stealing data using a compromised node disguised as a base station, as shown in Fig. 1 [18]. In this scenario, it is possible that the compromised node will reveal the keys of a sensor node [15].…”

Section: Introductionmentioning

confidence: 99%