Comparative analysis of fraud detection systems by phone number

Сергеев, В. В.; Gorbchenko, I. M.; Safronov, Vadim

doi:10.1088/1742-6596/1679/5/052003

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…al [7] argued, in 2021 in the United States, at least an estimated 86 million USD was lost due to fraud via SMS. The fraud can also take the form of spam which is very disturbing to SMS users [8] because it usually has less important information [9]. Spam emails are unsolicited, often irrelevant or malicious messages sent in bulk to a large number of recipients without their consent [10].…”

Section: Introductionmentioning

confidence: 99%

Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS

Astuti,

Sari,

Rachmawanto

et al. 2024

SJI

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Purpose: Fraud is rampant in the current era, especially in the era of technology where there is now easy access to a lot of information. Therefore, everyone needs to be able to sort out whether the information received is the right information or information that is fraudulent. In this research, the process of classifying messages including ham or spam has been carried out. The purpose of this research is to be able to build a model that can help classify messages. The purpose of this research is also to determine which machine learning method can accurately and efficiently perform the ham or spam classification process on messages.Methods: In this research, the ham or spam classification process has been using machine learning methods. The machine learning methods used are the classification process with Random Forest, Logistic Regression, Support Vector Classification, Gradient Boosting, and XGBoost Classifier algorithms. Results: The results obtained after testing in this study are the classification process using the Random Forest algorithm getting an accuracy of 97.28%, Logistic Regression getting an accuracy of 94.67%, with Support Vector Classification getting an accuracy of 97.93%, and using XGBoost Classifier getting an accuracy of 96.47%. The best precision value obtained in this study is 98% when using the random forest algorithm. The best recall value is 94% when using the SVC algorithm. While the best f1-score value is 95% when using the SVC algorithm.Novelty: This research has been compared with several algorithms. In previous research, it is still very rarely done using XGBoost to classify the ham or spam in messages. We focus on giving brief information based con comparison algorithm and show the best algorithm to classify classify the ham or spam in messages. And for the novelty that exists from this research, the machine learning model built gets better accuracy when compared to previous research.

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Section: Introductionmentioning

confidence: 99%

Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS

Astuti,

Sari,

Rachmawanto

et al. 2024

SJI

View full text Add to dashboard Cite

show abstract

“…Many previous researches [103,97,63] have proved that mobile phone usage is highly related to people's emotions and professions, that can be predicted or identified by mining user-generated mobile phone data. Meanwhile, there are some works try to detect fraud and malicious phone numbers by using mobile phone data including Call Detailed Record (CDR) data, network requests and application usage [115,144].…”

Section: Identify User Professions Based On Privacy-preserved Mobile ...mentioning

confidence: 99%

User identification and community exploration via mining big personal data in online platforms

Zhang¹

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User-generated big data mining is vital important for large online platforms in terms of security, profits improvement, products recommendation and system management. Personal attributes recognition, user behavior prediction, user identification, and community detection are the most critical and interesting issues that remain as challenges in many real applications in terms of accuracy, efficiency and data security. For an online platform with tens of thousands of users, it is always vulnerable to malicious users who pose a threat to other innocent users and consume unnecessary resources, where accurate user identification is urgently required to prevent corresponding malicious attempts. Meanwhile, accurate prediction of user behavior will help large platforms provide satisfactory recommendations to users and efficiently allocate different amounts of resources to different users. In addition to individual identification, community exploration of large social networks that formed by online databases could also help managers gain knowledge of how a community evolves. And such large scale and diverse social networks can be used to validate network theories, which are previously developed from synthetic networks or small real networks. In this thesis, we study several specific cases to address some key challenges that remain in different types of large online platforms, such as user behavior prediction for cold-start users, privacy protection for user-generated data, and large scale and diverse social community analysis.

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Comparative analysis of fraud detection systems by phone number

Cited by 2 publications

References 2 publications

Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS

Comparative Study of Machine Learning Algorithms for Performing Ham or Spam Classification in SMS

User identification and community exploration via mining big personal data in online platforms

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