Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networks

Subudhi, Sharmila; Panigrahi, Suvasini

doi:10.1504/ijsn.2016.075069

Cited by 18 publications

(9 citation statements)

References 29 publications

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“…However, the proposed FDS exhibits optimal performance in Profile 3 by claiming maximum Specificity = 88.46% (i.e., least false acceptance rate). It is to be noted that gaining high Sensitivity and Specificity is desirable for achieving effective classification result [40]. Similarly, Table 7 gives an insight into the comparative performance of our approach, KC_FDS and HAC_FDS in terms of Accuracy, Precision and F-Score measured in %.…”

Section: Comparative Performance Studymentioning

confidence: 96%

“…An approach proposed in [40] has used FCM and SVM on the past call records of each user for detecting fraudulent calls. The FCM clustering technique has been applied to certain calling features for user profile construction.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A comparative analysis of the proposed FDS has been done in this section with two other mobile phone fraud detection approaches found in the literature [21,40]. Experiments are done on the Reality Mining dataset [13] while considering the above-mentioned performance metrics.…”

Section: Comparative Performance Studymentioning

confidence: 99%

“…Moreover, by attaining the highest Precision = 93.33%, we conclude that the current model captures the subscriber's behavior more accurately in all profiles than KC_FDS and HAC_FDS. In another work [40], the authors have suggested using FCM clustering and support vector machine (SVM) for identification of fraudulent behavior through user profile building and hence named as FCMSVM_FDS. Initially, the past call records of a subscriber are given as input to FCM and calling behavioral profiles are generated for each user via cluster formation.…”

Section: Comparative Performance Studymentioning

confidence: 99%

See 3 more Smart Citations

A hybrid mobile call fraud detection model using optimized fuzzy C-means clustering and group method of data handling-based network

Subudhi

Panigrahi

2018

Vietnam J Comput Sci

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A novel two-stage fraud detection system in mobile telecom networks has been presented in this paper that identifies the malicious calls among the normal ones in two stages. Initially, a genetic algorithm-based optimized fuzzy c-means clustering is applied to the user's historical call records for constructing the calling profile. Thereafter, the identification of the fraudulent calls occurs in two stages. In the first stage, each incoming call is passed to the clustering module that identifies the call as genuine, malicious or suspicious. This is done by comparing the distance value of the new calling instance from the profile cluster centers against two predefined threshold values. The calls detected as genuine or malicious are not further processed. However, the call records that are found to be suspicious are additionally scrutinized in the second stage by a previously trained group method of data handling model for final decision making. The legitimate and forged labeled call records generated out of the clustering module are utilized for training the supervised classifier. Experimentation is done on a real-world call dataset to exhibit the effectiveness of the proposed model. A comparative analysis of the current approach with one of our earlier propositions and another recent fraud detection system clearly illustrates the efficacy of the developed model. Keywords Fraud detection • Mobile phone call records • Genetic algorithm • Fuzzy C-means • Group method of data handling

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Section: Comparative Performance Studymentioning

confidence: 96%

Section: Literature Reviewmentioning

confidence: 99%

Section: Comparative Performance Studymentioning

confidence: 99%

Section: Comparative Performance Studymentioning

confidence: 99%

See 2 more Smart Citations

A hybrid mobile call fraud detection model using optimized fuzzy C-means clustering and group method of data handling-based network

Subudhi

Panigrahi

2018

Vietnam J Comput Sci

Self Cite

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“…Least squares twin multi-class SVM UCI dataset Tang et al [30] 2019 Regular simplex SVM Benchmark datasets Yang and Dong [31] 2019 SVM with generalized quantile loss UCI dataset Okwuashi and Ndehedehe [32] 2020 Deep SVM Hyperspectral image Furthermore, some literature has combined the fuzzy c-means (FCM) with SVM for improving the performance of the classifier ( [33][34][35][36][37][38]). These studies used the clustering label of FCM as the preprocess mechanism for improving the SVM classifier.…”

Section: Author(s)mentioning

confidence: 99%

Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

Chang

Lin

2020

Mathematics

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Classification problems are very important issues in real enterprises. In the patent infringement issue, accurate classification could help enterprises to understand court decisions to avoid patent infringement. However, the general classification method does not perform well in the patent infringement problem because there are too many complex variables. Therefore, this study attempts to develop a classification method, the support vector machine with new fuzzy selection (SVMFS), to judge the infringement of patent rights. The raw data are divided into training and testing sets. However, the data quality of the training set is not easy to evaluate. Effective data quality management requires a structural core that can support data operations. This study adopts new fuzzy selection based on membership values, which are generated from fuzzy c-means clustering, to select appropriate data to enhance the classification performance of the support vector machine (SVM). An empirical example based on the SVMFS shows that the proposed SVMFS can obtain a superior accuracy rate. Moreover, the new fuzzy selection also verifies that it can effectively select the training dataset.

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Study of Combating Technology Induced Fraud Assault (TIFA) and Possible Solutions: The Way Forward

Dadhich

Hiran

Rao

et al. 2022

Communications in Computer and Information Science

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Use of fuzzy clustering and support vector machine for detecting fraud in mobile telecommunication networks

Cited by 18 publications

References 29 publications

A hybrid mobile call fraud detection model using optimized fuzzy C-means clustering and group method of data handling-based network

A hybrid mobile call fraud detection model using optimized fuzzy C-means clustering and group method of data handling-based network

Developing Support Vector Machine with New Fuzzy Selection for the Infringement of a Patent Rights Problem

Study of Combating Technology Induced Fraud Assault (TIFA) and Possible Solutions: The Way Forward

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