Entropy Weight Coefficient Method for Evaluating Intrusion Detection Systems

Tian, Junfeng; Liu, Tao; Hongqiang, Jiao

doi:10.1109/isecs.2008.107

Cited by 17 publications

(10 citation statements)

References 12 publications

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“…Zou et al [19] used an entropy approach to calculate weights for water quality assessment indicators, whereas Malekian and Azarnivand [37] integrated Shannon's entropy with the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) technique to prioritize the flood risk in the Shemshak Watershed of Iran. Tian et al [38] have used an entropy method to calculate factor weights and reduce the influence of subjective judgments on the weight coefficients.…”

Section: Entropy Methodsmentioning

confidence: 99%

Crane Safety Assessment Method Based on Entropy and Cumulative Prospect Theory

Zhao

2017

Entropy

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Assessing the safety status of cranes is an important problem. To overcome the inaccuracies and misjudgments in such assessments, this work describes a safety assessment method for cranes that combines entropy and cumulative prospect theory. Firstly, the proposed method transforms the set of evaluation indices into an evaluation vector. Secondly, a decision matrix is then constructed from the evaluation vectors and evaluation standards, and an entropy-based technique is applied to calculate the index weights. Thirdly, positive and negative prospect value matrices are established from reference points based on the positive and negative ideal solutions. Thus, this enables the crane safety grade to be determined according to the ranked comprehensive prospect values. Finally, the safety status of four general overhead traveling crane samples is evaluated to verify the rationality and feasibility of the proposed method. The results demonstrate that the method described in this paper can precisely and reasonably reflect the safety status of a crane.

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Section: Entropy Methodsmentioning

confidence: 99%

Crane Safety Assessment Method Based on Entropy and Cumulative Prospect Theory

Zhao

2017

Entropy

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“…Each cluster includes one Cluster Head (CH) and several Cluster Members (CM) [4]. The multi-hop routing used in clustering topology to forward the sensed data from source to destination results in the x∈X p(x)log 2 p(x) for 0 ≤ H(X) ≤ 1 [14][15][16]. As we want to utilize the functionality of information entropy in clustering WSN, it should be kept in focus that CH or BS should not be hesitant or irresolute about any of their decisions regarding cluster formation and data fusion.…”

Section: Introductionmentioning

confidence: 99%

“…Keeping in view the opportunistic connection between sensor nodes in heterogeneous clustering, we selected multiple parameters including an asynchronous working-sleeping cycle, status transition frequencies, residual energy, link quality factor in terms of signal-to-noise ratio, distance between sensor node and BS, and number of supported sensor nodes by a potential CH as our attributes of hesitant fuzzy set. Furthermore, we need Multi-Attribute Decision Modeling (MADM) to efficiently utilize our hesitant fuzzy set to generate hesitant fuzzy entropy matrix and determine our entropy weight coefficients [14,15,17,18].…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the hesitant entropy matrix is generated after finding entropy values of each attribute in the hesitant fuzzy set using data standardization process. Subsequently, the entropy weight model [14,15,17] is employed to determine the entropy weight coefficients for each sensor node and, finally, the threshold attribute values are determined and then compared with the original attribute values for making a decision about new CH. This entire process is part of the CH election procedure which is initiated by the BS and continued by every CH for all communication rounds.…”

Section: Introductionmentioning

confidence: 99%

“…In previous studies, several researchers have adopted entropy weight coefficient method for making decision in clustering environment [14][15][16]18]. Entropy weight-based multi-criteria decision routing is a routing technique in which the next hop decision is based on Multi-Criteria Decision Analysis (MCDA) and Multi-Attribute Sensors 2020, 20, 913 5 of 28 Decision Modeling (MADM) using entropy weight coefficients [14,15,17,18]. Qiang et al [16] presented an optimization of objective and subjective weights based on fuzzy MADM routing for selection of next best hop in a heterogeneous WSN.…”

Section: Introductionmentioning

confidence: 99%

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Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

Anees

Zhang

Baig

et al. 2020

Sensors

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Limited energy resources of sensor nodes in Wireless Sensor Networks (WSNs) make energy consumption the most significant problem in practice. This paper proposes a novel, dynamic, self-organizing Hesitant Fuzzy Entropy-based Opportunistic Clustering and data fusion Scheme (HFECS) in order to overcome the energy consumption and network lifetime bottlenecks. The asynchronous working-sleeping cycle of sensor nodes could be exploited to make an opportunistic connection between sensor nodes in heterogeneous clustering. HFECS incorporates two levels of hierarchy in the network and energy heterogeneity is characterized using three levels of energy in sensor nodes. HFECS gathers local sensory data from sensor nodes and utilizes multi-attribute decision modeling and the entropy weight coefficient method for cluster formation and the cluster head election procedure. After cluster formation, HFECS uses the same techniques for performing data fusion at the first hierarchical level to reduce the redundant information flow from the first-second hierarchical levels, which can lead to an improvement in energy consumption, better utilization of bandwidth and extension of network lifetime. Our simulation results reveal that HFECS outperforms the existing benchmark schemes of heterogeneous clustering for larger network sizes in terms of half-life period, stability period, average residual energy, network lifetime, and packet delivery ratio.Sensors 2020, 20, 913 2 of 28 overall decrease in energy consumption and also reduces the interference among sensor nodes due to specific allocation of timeslots for communication purposes [4,5]. The clustering topology in WSN can effectively optimize the data redundancy by significantly reducing the size of the collected data using data aggregation and data fusion techniques at CH level. The aggregated or fused data can then be forwarded to the Base Station (BS) for further processing and accurate decision making of interested events [4][5][6].The recent literature shows that researchers have proposed a working-sleeping cycle strategy in WSN to save battery power in case the sensor nodes are idle and not performing any of the designated tasks. Alfayez et al. [7] discussed that the sensor nodes go to sleep to save their battery power and wake up before performing their routine operations. These node scheduling techniques can be categorized as synchronous and asynchronous working-sleeping scheduling. These node scheduling techniques are designed in accordance with the scenario to prolong the network lifetime and improve energy utilization by creating an opportunistic node connection between sensor nodes. According to [8][9][10][11][12], Opportunistic Routing (OR) is a paradigm for wireless networks that benefits from broadcast characteristics of a wireless medium by selecting multiple sensor nodes as candidate forwarders. In [10-12], a set of nodes were selected as potential forwarders that transmitted the data packet according to some special criteria after receiving data packet from their neighb...

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Increasing the Reach of Government Social Media: A Case Study in Modeling Government-Citizen Interaction on Facebook

Gonçalves¹,

Yong²,

Xiao³

et al. 2015

Policy &Amp; Internet

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Facebook posts compete for human attention in a zero-sum game; this makes it a challenge for government organizations to engage with their citizens through this medium. In a large-scale longitudinal study we investigate what makes Facebook posts popular (seen by many) and effective (commented, liked, or shared by many) in a nonprofit context: the official Facebook page of a midsized city (Oulu, Finland). We model the competition dynamics that shape the fate of Facebook posts using Structural Equation Modeling. Our analysis reveals that audience demographics, the timing of posts, and the media type of post are significant factors in post reach and effectiveness, and argue that our method can be applied to other contexts to determine which factors can lead to improved government-citizen communication and engagement. Finally, we argue for more actionable research to be conducted on the use of social media by government.

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Entropy Weight Coefficient Method for Evaluating Intrusion Detection Systems

Cited by 17 publications

References 12 publications

Crane Safety Assessment Method Based on Entropy and Cumulative Prospect Theory

Crane Safety Assessment Method Based on Entropy and Cumulative Prospect Theory

Hesitant Fuzzy Entropy-Based Opportunistic Clustering and Data Fusion Algorithm for Heterogeneous Wireless Sensor Networks

Increasing the Reach of Government Social Media: A Case Study in Modeling Government-Citizen Interaction on Facebook

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