 |
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
Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (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)
|
Issue: March 2026
Volume 11 | Issue 3
Review Result and Publication of Paper within : 2-3 days
Click Here For more DetailsFor Authors
Forms / Download
Published Issue Details
Editorial Board
Other IMP Links
Facts & Figure
Impact Factor : 8.14
Issue per Year : 12
Volume Published : 11
Issue Published : 118
Article Submitted : 21604
Article Published : 8531
Total Authors : 22438
Total Reviewer : 805
Total Countries : 159
Indexing Partner
Licence
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
|
Published Paper Details
|
|
| Paper Title: | Enhancing Cybersecurity with Cyberus: AI-Powered Risk Assessment for Messages and URLs |
| Authors Name: | Ankit Gajanan Katkhede , Avinash Subhash Bhople , Divya Kashinath Kothalkar , Annapurna Bhagvan Kolte , Vaishali. D. Parihar |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJRTI_201715
|
| Published Paper Id: | IJRTI2503199 |
| Published In: | Volume 10 Issue 3, March-2025 |
| DOI: | |
| Abstract: | Every day, more and more individuals are using mobile devices. Short message service, or SMS, is a text messaging feature that can be found on both smartphones and entry-level phones. As a result, SMS traffic skyrocketed. Additionally, the number of spam communications rose. Spammers attempt to send spam messages in an attempt to gain financial or business advantages, such as information about lottery tickets, credit card details, or market expansion. Therefore, particular attention is paid to spam classification. In this work, we used a variety of deep learning and machine learning methods to detect SMS spam. We developed a spam detection model using a UCI dataset. According to our experimental findings, our LSTM model performs better than earlier models in spam identification, with a 98.5% accuracy rate. Python was utilized for every implementation. We think that learning (ML) techniques for intelligent spam email identification can aid in the creation of suitable defenses. Four components of the email structure that can be utilized for intelligent analysis were examined in this paper: (A) Headers Offer Routing Information: These include mail transfer agents (MTA) that offer details such as the IP address and email address of each sender and receiver, the origin of the email, any stopovers, and the final destination. (B) The SMTP envelope, which includes the identity of the mail exchangers as well as the users of the source and destination domains. (C) The first section of SMTP data, which most email applications display and contains information like from, to, date, and topic (D) The email body, which includes the attachment and text content, is the second section of the SMTP data. |
| Keywords: | Spam Detection, Machine Learning, Spam Email, Spam Filtering, Opinion Spam. |
| Cite Article: | "Enhancing Cybersecurity with Cyberus: AI-Powered Risk Assessment for Messages and URLs", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 3, page no.b665-b670, March-2025, Available :http://www.ijrti.org/papers/IJRTI2503199.pdf |
| Downloads: | 000654 |
| ISSN: |
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 |
| Publication Details: |
Published Paper ID: IJRTI2503199
Registration ID:201715
Published In: Volume 10 Issue 3, March-2025
DOI (Digital Object Identifier):
Page No: b665-b670 Country: Chikhali,Buldhana, Maharashtra, India Research Area: Information Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2503199 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2503199 |
| Share Article: | |
|
Click Here to Download This Article |
|
| Article Preview | |
|
|
|
Major Indexing from www.ijrti.org
| Google Scholar | ResearcherID Thomson Reuters | Mendeley : reference manager | Academia.edu |
| arXiv.org : cornell university library | Research Gate | CiteSeerX | DOAJ : Directory of Open Access Journals |
| DRJI | Index Copernicus International | Scribd | DocStoc |
ISSN Details
 |
 |
|
ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
Journal Starting Year (ESTD) : 2016
DOI (A digital object identifier)
| Providing A digital object identifier by DOI.ONE How to Get DOI? |
Conference
Open Access License Policy
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
Important Details
Join RMS/Earn 300
WhatsApp
Click Here
Click Here
Indexing Partner |
|||
| Copyright © 2026 - All Rights Reserved - IJRTI | |||