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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | NEXT-GEN CYBERSECURITY: AI-POWERED SMS SPAM DETECTION |
| Authors Name: | Devika e p , Dr. Deepa A |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_217009
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| Published Paper Id: | IJNRD2403653 |
| Published In: | Volume 9 Issue 3, March-2024 |
| DOI: | |
| Abstract: | The proliferation of SMS spam poses a significant challenge to mobile users, necessitating advanced detection mechanisms. In this research, we present an innovative AI-powered SMS spam detection system, integrating state-of-the-art machine learning techniques. The system begins with preprocessing SMS datasets, extracting key features such as message content, sender information, and metadata. These features serve as inputs to various machine learning algorithms, including Support Vector Machines (SVM), Naive Bayes, and Random Forest. Additionally, we incorporate natural language processing (NLP) methods to analyze the semantic content of messages, improving the model's ability to differentiate between spam and legitimate messages. Extensive experimentation is conducted on diverse datasets to evaluate the system's performance across multiple metrics, including accuracy, precision, recall, and computational efficiency. Results demonstrate the effectiveness of our approach in accurately identifying spam messages while minimizing false positives. Furthermore, our system exhibits scalability and adaptability to dynamic spamming techniques, making it suitable for real-time deployment. The study underscores the critical role of AI and machine learning in combating SMS spam, ensuring a secure and hassle-free communication environment for mobile users worldwide. Our research contributes to the ongoing efforts to enhance the resilience of mobile networks against emerging spamming threats, thereby safeguarding user privacy and experience. |
| Keywords: | Sms spam detection, Artificial Intelligence, Algorithm |
| Cite Article: | "NEXT-GEN CYBERSECURITY: AI-POWERED SMS SPAM DETECTION", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.9, Issue 3, page no.g441-g446, March-2024, Available :http://www.ijnrd.org/papers/IJNRD2403653.pdf |
| Downloads: | 00040 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 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:IJNRD2403653 Registration ID: 217009 Published In: Volume 9 Issue 3, March-2024 DOI (Digital Object Identifier): Page No: g441-g446 Country: Kozhikode, Kerala, India Research Area: Master of Computer Application Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2403653 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2403653 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
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