DETECTING AND CLASSIFYING FRAUDULENT SMS AND EMAIL WITH A ROBUST MACHINE LEARNING APPROACH
Main Article Content
Abstract
Spam is an unwanted message or SMS sent onmobile phones whose content may bemalicious. Scammers sendfake text messages to trick people into responding to their SMSand they may hack personal information, password, accountnumber, etc. To avoid being tricked by scammers, proposed amodel based on Machine learning Algorithms. The proposedmodel is implemented using the Naïve Bayes algorithm and termfrequency-inverse document frequency vectorizer. Obtainedthe dataset from Kaggle and trained the model using it. Thismodel consists of a local host website which is obtained throughPyCharm IDE. Obtained results show that the model accuracyof 95% and a precision of 100%
Downloads
Metrics
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format for any purpose, even commercially.
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
- The licensor cannot revoke these freedoms as long as you follow the license terms.
Under the following terms:
- Attribution — You must give appropriate credit , provide a link to the license, and indicate if changes were made . You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
Notices:
You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation .
No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material.
References
Lutfun Nahar Lota et al. “A Systematic Literature Review on SMSSpam Detection Techniques”, I.J. Information
Technology andComputer Science, 2017, 7, 42-50.
P. Sethi et al. “SMS spam detection and comparison of variousmachine learning algorithms,” International
Conference on Computingand Communication Technologies for Smart Nation (IC3TSN), 2017,pp. 28-31.
S. M. Abdulhamid et al. “A Review on Mobile SMS Spam Filtering Techniques,” IEEE Access, vol. 5, pp. 15650-
, 2017.
M.Rubin Julis et al. “Spam Detection in SMS Using Machine Learning Through Text Mining”, International journal
of scientific & technology research, vol 9, Issue 02, 2020.
A. Alzahrani et al. “Comparative Study of Machine Learning Algorithms for SMS Spam Detection,” SoutheastCon,
, pp. 1-6.
N. Nisar et al. “Voting-Ensemble Classification for Email Spam Detection,” International Conference on
Communication information and Computing Technology (ICCICT), 2021, pp. 1-6.
S. Agarwal et al. “SMS spam detection for Indian messages,” International Conference on Next Generation
Computing Technologies (NGCT), 2015, pp. 634-
Michael Crawford et al. “Survey of Review spam detection using machine learning techniques”, Journal of Big Data,
Anju Radhakrishnan et al. “Email Classification using Machine learning algorithms”, International Journal of
Engineering and Technology (IJET), 2017.pp.335-340.
N. Govil et al. “A Machine Learning based Spam Detection Mechanism,” International Conference on Computing
Methodologies and Communication (ICCMC), 2020, pp. 954-957. [11] Shirani-Mehr et al. “SMS spam detection using
machine learning approach” 2013,1-4.