LinkedInGuard: A Personal Web-Based AI Security Assistant for Detecting Threats
Document Type
Conference Proceeding
Source of Publication
2026 17th Student Research Conference on Applied Computing Src 2026
Publication Date
4-22-2026
Abstract
The rapid growth of professional networking platforms such as LinkedIn has increased users' exposure to cybersecurity threats, including phishing, fake job offers, impersonation, and malicious content. While existing platform defenses are often reactive or designed for organizational use, there is a lack of accessible tools that help individual users evaluate suspicious content before becoming victims. This paper presents LinkedInGuard, an artificial intelligence-driven web application that leverages open-source large language models to analyze user-submitted LinkedIn text, posts, or files and classify potential risks such as phishing, fake job offers, and other suspicious activity. The system presents results through a user-friendly dashboard that explains risks and suggests safer actions. Full background automation was not implemented due to platform access restrictions and associated costs, which were beyond the scope of this student project. Experimental testing shows that LinkedInGuard can effectively assist users in identifying harmful content and improving their online decision-making, contributing to enhanced trust, awareness, and digital safety on professional networking platforms.
DOI Link
ISBN
[9798319510167]
Publisher
IEEE
Disciplines
Computer Sciences
Keywords
cybersecurity, fake profiles, LinkedIn security, machine learning, social engineering
Scopus ID
Recommended Citation
Almehrzi, Reem; Alrahma, Shouq; and Belqasmi, Fatna, "LinkedInGuard: A Personal Web-Based AI Security Assistant for Detecting Threats" (2026). All Works. 8220.
https://zuscholars.zu.ac.ae/works/8220
Indexed in Scopus
yes
Open Access
no