Abstract:
With the fast development of digital technology, cyber threats by malware and
phishing attacks have gradually grown to be a regular and harmful occurrence. Most of
these attacks focus on vital data, which comprise Personally Identifiable Information (PII),
including Sensitive PII (SPII). The objective of this study is to develop a web-based Multi
Vulnerability Scanner to help users identify threats originating from URLs, files, and
emails in an accessible and user-friendly manner, even for those with minimal technical
background. The system performs threat detection across multiple input types by analyzing
URLs using a supervised machine learning model using XGBoost, inspecting email
components such as domain reputation, suspicious links, and sender Ips using SVM, and
evaluating uploaded files through hash matching and entropy analysis. The system
successfully detects threats and provides detailed results and recommendations to users.
This solution helps increase cybersecurity awareness and offers an effective way to protect
against attacks via .exe files, emails and URLs
