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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Volume 11 | Issue 2
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Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 117
Article Submitted : 21307
Article Published : 8476
Total Authors : 22301
Total Reviewer : 802
Total Countries : 156
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| Paper Title: | Advanced Techniques in Spam Detection: A Comparative Analysis of Traditional and AI-Based Approaches |
| Authors Name: | Mallela Aswartha Sai Kumar Reddy |
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IJRTI_206248
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| Published Paper Id: | IJRTI2509067 |
| Published In: | Volume 10 Issue 9, September-2025 |
| DOI: | |
| Abstract: | Spam detection remains a critical challenge in digital communication systems, with the exponential growth of electronic communications leading to increasingly sophisticated spam techniques. This paper presents a comprehensive analysis of spam detection methodologies, comparing traditional rule-based and statistical approaches with modern artificial intelligence techniques. We examine machine learning algorithms, deep learning architectures, and natural language processing methods for effective spam identification. Our comparative analysis evaluates performance metrics including accuracy, precision, recall, F1-score, and ROC-AUC across multiple datasets. The study reveals that while traditional methods like Naive Bayes and Support Vector Machines achieve respectable performance (85-92% accuracy), deep learning approaches, particularly transformer-based models, demonstrate superior results (94-98% accuracy) in handling complex spam patterns. However, these advanced methods require substantial computational resources and training data. We identify key challenges including concept drift, adversarial attacks, multilingual spam detection, and privacy concerns. Future research directions include federated learning approaches, explainable AI for spam detection, and real-time adaptive systems. This comprehensive review provides researchers and practitioners with insights into current state-of-the-art techniques and guides future development in spam detection systems. |
| Keywords: | Spam detection, machine learning, deep learning, natural language processing, email security, text classification |
| Cite Article: | "Advanced Techniques in Spam Detection: A Comparative Analysis of Traditional and AI-Based Approaches", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 9, page no.a598-a609, September-2025, Available :http://www.ijrti.org/papers/IJRTI2509067.pdf |
| Downloads: | 0001459 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID: IJRTI2509067
Registration ID:206248
Published In: Volume 10 Issue 9, September-2025
DOI (Digital Object Identifier):
Page No: a598-a609 Country: kadapa, andhra pradesh , India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2509067 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2509067 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
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