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Telecommunication fraud resilient framework for efficient and accurate detection of SMS phishing using artificial intelligence techniques

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Abstract

One of the telecommunications’ most popular forms of fraud is the short message service (SMS). Mobile users have a valid fear about SMS spam, which disturbs telecoms network operators since it impacts their clients and costs them money. For that, the existing research utilized an artificial intelligence approach to detect SMS phishing in telecommunication. Since SMS text data is unstructured and contains complicated, nonlinear relationships, this process could be difficult. Therefore, this research developed a Fraud Resilient Framework using Enhanced CNN-based SMS Phishing detection. Telecommunication fraud-related datasets are collected. Firstly, the data are preprocessed and cleaned using stemming, tokenization, and the TF-IDF approach. Moreover, to extract the features, the existing research utilized the information gain technique, which is time-consuming. So to overcome these flaws, this research introduces Assimilated Pearson Correlation Coefficient Principal Component Analysis (PCC-PCA) for feature extraction. This research introduces an enhanced Convolutional Neural Network (Enhanced CNN) in which, overcome the exploding gradients, this research introduces Parameterized ReLU which minimizes architecture complexity, regularizing, and early stopping. Then, the retrieved features are used in Enhanced CNN to categorize the ham and spam in the telecommunication network. As a result, when matched to cutting-edge techniques, this proposed solution offers great accuracy and efficiency.

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Data sharing not applicable to this article as no datasets were generated or analyzed during the current study.

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Authors and Affiliations

  1. Department of Computer Engineering, Smt.Kashibai Navale College of Engineering, Vadgaon(BK), Pune, 411041, India

    Devendra Sambhaji Hapase & Lalit Vasantrao Patil

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  1. Devendra Sambhaji Hapase
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  2. Lalit Vasantrao Patil
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Correspondence to Devendra Sambhaji Hapase.

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Hapase, D.S., Patil, L.V. Telecommunication fraud resilient framework for efficient and accurate detection of SMS phishing using artificial intelligence techniques. Multimed Tools Appl (2024). https://doi.org/10.1007/s11042-024-19020-2

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