Spam Call Protection Using Machine Learning

AKASH S; DR N Pughazendi

Spam Call Protection Using Machine Learning

September 2024

Vol-10, Issue-5

Paper ID: 25006

ISSN: 2395-4396

Downloads: 0

Abstract & Details

Research Area

Telecommunication security

Keywords

Robocalling Spam calls Fraudulent calls Telecommunication networks Machine learning (ML) Supervised learning Unsupervised learning Reinforcement learning Call metadata Decision trees Support Vector Machines (SVM) Deep neural networks Clustering Anomaly detection Natural Language Processing (NLP) Voice spam Real-time data analytics False positives Data privacy Model updates Cloud deployment Spam call protection Ethical considerations

Abstract

Robocalling has turned into a Public Enemy No. 1 in the world of telecommunications, costing billions, reducing efficiency and violating privacy. Spam and fraudulent calls are increasing significantly with the expansion of telecommunication networks worldwide, triggering a need for methods that respond adequately to this challenge. In the past, over and above spam calls and brute force attempts at cracking passcodes, traditional types of telemarketing campaign such as blacklisted spam call protection mechanisms, rule-based filtering etc., made short work of banning numbers that violated the auto dialer rules. This is where Machine Learning (ML) shines, using sophisticated algorithms that enable the software to analyse data and learn from it, identify patterns and make decisions. In this paper, we will study the spam call detection and protection problem using machine learning models and various practical challenges and surgeries offered by this new solution. In particular, we cover supervised and unsupervised and reinforcement learning techniques that telecommunication network providers can use to sieve out spam calls. The spam and legitimate calls are classified using supervised learning models like decision trees, SVM, or deep neural networks which is trained on the call metadata (e.g. call frequency, duration, origin) on huge datasets. There are methods like unsupervised learning too, for example clustering or anomaly detection algorithms which make use of the data patterns and abnormality for providing an additional layer of protection invisibly without labels. We also consider how Natural Language Processing (NLP) can provide assistance with the identification of robocalls and voice spam based on patterns in speech and text. By deploying ML models on the cloud for real-time data analytics this is scalable to protect from spam calls. The key challenges discussed were the potential for false positives, data privacy risks, and ongoing requirements for model updates in order to prevent new types of spam. In conclusion, machine learning shows great potential for transforming the spam call protection space but it can only deliver on its promises with a deep robust model design, comprehensive datasets that adequately represent the entire spectrum of users and ethical considerations.

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author Information

#	Name	Institute / Affiliation
1	AKASH S	CMR University
2	DR N Pughazendi	CMR University

How to Cite

Use the following formats to cite this article in your research.

APA Style

S, AKASH & Pughazendi, DR N (2024). Spam Call Protection Using Machine Learning. International Journal of Advance Research and Innovative Ideas In Education, 10(5), 708-712.

MLA Style

S, AKASH, and DR N Pughazendi. "Spam Call Protection Using Machine Learning." International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, 2024, pp. 708-712.

IEEE Style

AKASH S and DR N Pughazendi, "Spam Call Protection Using Machine Learning," International Journal of Advance Research and Innovative Ideas In Education, vol. 10, no. 5, pp. 708-712, 2024.

Vancouver Style

S AKASH, Pughazendi DR N. Spam Call Protection Using Machine Learning. International Journal of Advance Research and Innovative Ideas In Education. 2024;10(5):708-712.

Harvard Style

S, AKASH & Pughazendi, DR N (2024) 'Spam Call Protection Using Machine Learning', International Journal of Advance Research and Innovative Ideas In Education, 10(5), pp. 708-712.

Chicago Style

S, AKASH and DR N Pughazendi. "Spam Call Protection Using Machine Learning." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 708-712.

Turabian Style

S, AKASH and DR N Pughazendi. "Spam Call Protection Using Machine Learning." International Journal of Advance Research and Innovative Ideas In Education 10, no. 5 (2024): 708-712.