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

Abstract

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

Mobile Phone Data: A Survey of Techniques, Features, and Applications

“…Finally, some studies combine two or more algorithms to improve the classifier; in this approach, one algorithm works as a preprocessing mechanism to create a user profile, and the second algorithm is used to take these generated user profile clusters as input to the SVM classifier, as in [ 128 , 130 ]. Another example is studies [ 122 , 124 ], which use multiple algorithms, where unsupervised algorithms such as k-means and PCA are used for feature dimensionality reduction and supervised algorithms such as SVM and RF are selected according to which will achieve the best classification results.…”

“…Fraud detection is used to detect fraudulent activities in telecommunications companies. For example, [ 128 ] and [ 130 ] detected fraudulent calls in mobile telecommunication networks based on extracting mobile subscribers’ calling behaviors. To depict subscribers’ calling behaviors, the authors extracted calling features from CDRs data, such as type of calls, call duration, frequency of a call, and call timestamp, which were later used to help classify mobile subscribers into three categories: genuine, fraudulent, and suspicious.…”

Clustering Mixed Data Based on Density Peaks and Stacked Denoising Autoencoders

“…Let a denote input vector of a data object, which can be encoded to a feature representation b by a nonlinear activation function f θ , as shown in (3).…”

“…Clustering analysis, an important unsupervised data analysis and data mining approaches, has been studied extensively and applied successfully to various domains, such as gene expression analysis [1,2], fraud detection [3], imagine segmentation [4], and document mining [5,6]. The basic clustering algorithms mainly group data objects into different clusters based on the similarities between objects in original data space, making objects in the same cluster are more similar while those in different clusters are more dissimilar.…”

Fraud Detection Call Detail Record Using Machine Learning in Telecommunications Company

“…Telecommunications companies often experience huge financial losses due to fraud incidents caused by their services, and this makes the importance of fraud detection to ASTESJ ISSN: 2415-6698 reduce the impact of this risk [7]. Fraud on telecommunications can be divided into several types, and the most typical is accessing calls using the original customer account to make fraudulent calls [8]. Imagine an unknown call from a local number, and that from a friend or family who lives abroad, it's really strange to receive international calls from a local number, basically this also fraud [9].…”