PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 12, 2026INTERNATIONAL JOURNAL OF RESEARCH AND ANALYTICAL REVIEWS0 citationsOpen Access

Adaptive Multi-Modal Phishing Detection Using Transformer Natural Language Processing, Visual Analysis, and Threat Intelligence

KSKorva SandeepBRB. RamjiHRHalavath Rajashekar

Key Points

  • The aim is to develop a multi-modal framework for detecting phishing attacks using various data sources.
  • Utilized transformer-based natural language processing techniques for text analysis.
  • Incorporated visual analysis to identify deceptive elements in phishing attempts.
  • Employed threat intelligence to enhance detection efficacy and adaptability.
  • Achieved improved detection rates compared to traditional methods.
  • Demonstrated effectiveness across diverse phishing scenarios.
  • Showed potential for real-time application in cybersecurity measures.

Abstract

UGC-CARE list, New UGC-CARE Reference List, UGC CARE Journals, ugc care list of journal, ugc care list, UGC Approved list, list of ugc approved journal, ugc approved journal,IJRAR - international Research Journal,IJRAR.ORG,Ijrar.org, International Journal of Research and Analytical Reviews (IJRAR) , UGC Approved journal, ugc approved,ugc, ugc certify, publish free of cost, free publication, UGC and ISSN Approved , International Peer Reviewed, Open Access Journal , e ISSN 2348 –1269, Print ISSN 2349-5138, ISSN: 2348 –1269, Impact Factor : 5.75 , E- journal, Low Cost INR 500, Free Publication

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Sandeep et al. (2026) studied this question.

synapsesocial.com/papers/69db38274fe01fead37c6602https://doi.org/10.56975/ijrar.v13i1.330555
Ask AI
Helpful
Bookmark
Share
View Full Paper