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

Author:

Hapase Devendra Sambhaji,Patil Lalit Vasantrao

Publisher

Springer Science and Business Media LLC

Reference30 articles.

1. Hilas CS, Mastorocostas PA (2008) An application of supervised and unsupervised learning approaches to telecommunications fraud detection. Knowl-Based Syst 21(7):721–726

2. Jain V (2017) Perspective analysis of telecommunication fraud detection using data stream analytics and neural network classification based data mining. Int J Inf Technol 9(3):303–310

3. Hila CS, Sahalos JN (2007) An application of decision trees for rule extraction towards telecommunications fraud detection. In Knowledge-Based Intelligent Information and Engineering Systems: 11th International Conference, KES 2007, XVII Italian Workshop on Neural Networks, Vietri sul Mare, Italy, September 12–14, 2007. Proceedings, Part II 11 Springer Berlin Heidelberg:1112–1121. https://link.springer.com/chapter/10.1007/978-3-540-74827-4_139

4. Hilas CS, Kazarlis SA, Rekanos IT, Mastorocostas PA (2014) A genetic programming approach to telecommunications fraud detection and classification. In Proc. 2014 Int. Conf. Circuits, Syst. Signal Process. Commun. Comput pp 77–83. https://www.researchgate.net/profile/Constantinos-Hilas/publication/261191177_A_Genetic_Programming_Approach_to_Telecommunications_Fraud_Detection_and_Classification/links/0046353441f851ac2a000000/A-Genetic-Programming-Approach-to-Telecommunications-Fraud-Detection-and-Classification.pdf

5. Estévez PA, Held CM, Perez CA (2006) Subscription fraud prevention in telecommunications using fuzzy rules and neural networks. Expert Syst Appl 31(2):337–344

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