Cited by Learning to Effectively Identify Reliable Content in Health Social Platforms with Large Language Models

Web-based phishing URL detection model using deep learning optimization techniques DOI

Kousik Barik, Sanjay Misra,

R.S. Mohan

et al.

International Journal of Data Science and Analytics, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 8, 2025

Language: Английский

Citations

Forensics Analysis of Cloud-Computing Traffics DOI

Moayad Almutairi,

Shailendra Mishra, Mohammed Alshehri

et al.

International Journal of Computing and Digital Systems, Journal Year: 2024, Volume and Issue: 15(1), P. 1037 - 1052

Published: March 1, 2024

The objective of forensic analysis cloud computing traffic is to identify any suspicious or malicious behavior in network activities within settings.This includes examining connections, data transfer patterns, and payloads.To effectively analyze associated with IoT devices, it imperative understand infrastructure protocols.The prevalence devices environments necessitates efficient techniques address security incidents.Considering the distinctive attributes decentralized nature environments, this paper investigates challenges considerations related systems incorporating devices.The framework we present comprehensive guidelines about collection, preservation, analysis, IoT-based systems.As well as providing practical case studies, demonstrate how our works real-world scenarios involving computing.Providing tangible solutions specific systems, findings research contribute significantly a better understanding these challenges.

Language: Английский

Citations

A robust approach to authorship verification using siamese deep learning: application in phishing email detection DOI

Mohamed Abdelkarim Remmide, Fatima Boumahdi,

Imane Rebeh Ammar Aouchiche

et al.

International Journal of Speech Technology, Journal Year: 2024, Volume and Issue: 27(2), P. 405 - 412

Published: June 1, 2024

Language: Английский

Citations

Contextual Forensic Analysis of Emails Using Machine Learning Algorithms DOI

K. A. Apoorva,

S. Sangeetha

Published: Feb. 9, 2024

Emails are essential for personal, professional, and commercial communication, their increasing volume has led to the need contextual analysis. Machine learning algorithms offer a more efficient accurate approach understanding meanings, sentiments, intents behind emails. This paper explores efficiency of unsupervised machine models text classification in email data, specifically applying K-means DBSCAN clustering algorithms. The study evaluates performance using silhouette scores, highlighting potential enhancing customer feedback, fraud detection, productivity enhancement context communication.

Language: Английский

Citations

Learning to Effectively Identify Reliable Content in Health Social Platforms with Large Language Models DOI

Caihua Liu,

Hui Zhou,

Lishen Su

et al.

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 55 - 67

Published: Jan. 1, 2024

Language: Английский

Citations