PFrauDetector: A Parallelized Graph Mining Approach for Efficient Fraudulent Phone Call Detection

Ying, Josh Jia-Ching; Ji, Zhang; Huang, Chien‐Chang; Chen, Kuan-Ta; Tseng, Vincent S.

doi:10.1109/icpads.2016.0140

Cited by 9 publications

(9 citation statements)

References 11 publications

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“…, n are used simultaneously. [1,2], [1,2,3], [1,2,3,4], [1,2,3,4,5], [1,2,3,4,5,6] five various multi-scale convolution kernels are selected for our experiments, and the number of convolution kernels is 150, 100, 75, 60, and 50 corresponding to them, respectively. The product of the convolution kernel of each scale and the number of kernels is equal to the output size of the hidden layer.…”

Section: Parametric Experimentsmentioning

confidence: 99%

“…There are also researchers using deep learning methods [3] to replace artificial features with automatic features, which improves the recognition accuracy of fraudulent calls and ensures timeliness. Ying et al [4] proposed an efficient fraud phone detection framework based on parallel graph mining, which can automatically label fraudulent phone numbers to generate phone number trust values. However, the above methods rely on a large amount of user behavior data and are unable to detect the semantics of the phone content, which results in the inability to identify a new phone number.…”

Section: Introductionmentioning

confidence: 99%

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Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

Zhou¹,

Xu²,

Zhang³

et al. 2023

Computer Systems Science and Engineering

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Fraud cases have been a risk in society and people's property security has been greatly threatened. In recent studies, many promising algorithms have been developed for social media offensive text recognition as well as sentiment analysis. These algorithms are also suitable for fraudulent phone text recognition. Compared to these tasks, the semantics of fraudulent words are more complex and more difficult to distinguish. Recurrent Neural Networks (RNN), the variants of RNN, Convolutional Neural Networks (CNN), and hybrid neural networks to extract text features are used by most text classification research. However, a single network or a simple network combination cannot obtain rich characteristic knowledge of fraudulent phone texts relatively. Therefore, a new model is proposed in this paper. In the fraudulent phone text, the knowledge that can be learned by the model includes the sequence structure of sentences, the correlation between words, the correlation of contextual semantics, the feature of keywords in sentences, etc. The new model combines a bidirectional Long-Short Term Memory Neural Network (BiLSTM) or a bidirectional Gate Recurrent United (BiGRU) and a Multi-Head attention mechanism module with convolution. A normalization layer is added after the output of the final hidden layer. BiLSTM or BiGRU is used to build the encoding and decoding layer. Multi-head attention mechanism module with convolution (MHAC) enhances the ability of the model to learn global interaction information and multi-granularity local interaction information in fraudulent sentences. A fraudulent phone text dataset is produced by us in this paper. The THUCNews data sets and fraudulent phone text data sets are used in experiments. Experiment results show that compared with the baseline model, the proposed model (LMHACL) has the best experiment results in terms of Accuracy, Precision, Recall, and F1 score on the two data sets. And the performance indexes on fraudulent phone text data sets are all above 0.94.

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Section: Parametric Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

Zhou¹,

Xu²,

Zhang³

et al. 2023

Computer Systems Science and Engineering

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“…It only constructed a classifier for the call behavior features of a fraudulent phone call, but did not analyze the phone number features of the fraudulent phone call, and the accuracy of the algorithm was only 76%. Other researchers like [7,8,19,20] chose to use Decision Tree, Naive Bayesian models, graph mining, and other approaches [21][22][23] to classify and analyze call behavior features.…”

Section: Related Workmentioning

confidence: 99%

Automated Fraudulent Phone Call Recognition through Deep Learning

Xing

Wang

et al. 2020

Wireless Communications and Mobile Computing

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Several studies have shown that the phone number and call behavior generated by a phone call reveal the type of phone call. By analyzing the phone number rules and call behavior patterns, we can recognize the fraudulent phone call. The success of this recognition heavily depends on the particular set of features that are used to construct the classifier. Since these features are human-labor engineered, any change introduced to the telephone fraud can render these carefully constructed features ineffective. In this paper, we show that we can automate the feature engineering process and, thus, automatically recognize the fraudulent phone call by applying our proposed novel approach based on deep learning. We design and construct a new classifier based on Call Detail Records (CDR) for fraudulent phone call recognition and find that the performance achieved by our deep learning-based approach outperforms competing methods. Experimental results demonstrate the effectiveness of the proposed approach. Specifically, in our accuracy evaluation, the obtained accuracy exceeds 99%, and the most performant deep learning model is 4.7% more accurate than the state-of-the-art recognition model on average. Furthermore, we show that our deep learning approach is very stable in real-world environments, and the implicit features automatically learned by our approach are far more resilient to dynamic changes of a fraudulent phone number and its call behavior over time. We conclude that the ability to automatically construct the most relevant phone number features and call behavior features and perform accurate fraudulent phone call recognition makes our deep learning-based approach a precise, efficient, and robust technique for fraudulent phone call recognition.

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“…These bipartite structures are captured by bipartite graphs, which are rich data models that provide full representation without information loss for interactions that naturally occur in one mode (compressed datasets as unipartite graphs [128]), or multiple modes (high order interconnections as hyper graphs [5,57,114]). A prevalent use case is where bipartite graphs capture the interactions of users with entities spanning different domains such as social networks (users-hashtags [122]), web-based services (users-websites, multimedia services, and products [58,102,105,112,117]), financial systems (users-donation campaigns [4]), transportation systems (users-registered vehicles [59]), and communication systems (users-phone calls [120]).…”

Section: Introductionmentioning

confidence: 99%

Scale-Invariant Strength Assortativity of Streaming Butterflies

Sheshbolouki¹,

Özsu²

2021

Preprint

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Bipartite graphs are rich data structures with prevalent applications and identifier structural features. However, less is known about their growth patterns, particularly in streaming settings. Current works study the patterns of static or aggregated temporal graphs optimized for certain down-stream analytics or ignoring multipartite /non-stationary data distributions, emergence patterns of subgraphs, and streaming paradigms. To address these, we perform statistical network analysis over web log streams and identify the governing patterns underlying the bursty emergence of mesoscopic building blocks, 2,2-bicliques known as butterflies, leading to a phenomenon that we call "scale-invariant strength assortativity of streaming butterflie". We provide the graph-theoretic explanation of this phenomenon. We further introduce a set of micro-mechanics in the body of a streaming growth algorithm, sGrow, to pinpoint the generative origins. sGrow supports streaming paradigms, emergence of 4-vertex graphlets, and provides user-specified configurations for the scale, burstiness, level of strength assortativity, probability of out-of-order records, generation time, and time-sensitive connections. Comprehensive Evaluations on pattern reproducing and stress testing validate the effectiveness, efficiency, and robustness of sGrow in realization of the observed patterns independent of initial conditions, scale, temporal characteristics, and model configurations. Theoretical and experimental analysis verify the robust ability of sGrow in generating streaming graphs based on user-specified configurations that affect the scale and burstiness of the stream, level of strength assortativity, probability of-of-order streaming records, generation time, and time-sensitive connections.

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PFrauDetector: A Parallelized Graph Mining Approach for Efficient Fraudulent Phone Call Detection

Cited by 9 publications

References 11 publications

Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

Using Recurrent Neural Network Structure and Multi-Head Attention with Convolution for Fraudulent Phone Text Recognition

Automated Fraudulent Phone Call Recognition through Deep Learning

Scale-Invariant Strength Assortativity of Streaming Butterflies

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