Survey On Phisnet Ai - Proactive Defense Against Malicious Url’s

• Author(s):

  A.Nandhini | G.Gokulkannan | M.Sabeena | J.Saranya | J.Shehara Banu

• Keywords:

  Phishing URL Detection, Machine Learning, WAF, Bot Detection, IDS/IPS, OAuth/JWT, TLS Encryption

• Abstract:

  This Project Presents PhishNet AI, A Proactive And Intelligent Mobile-based Security Framework Designed To Detect And Prevent Phishing Attacks Caused By Malicious URLs. With The Rapid Increase In Digital Communication Through SMS, Emails, And Social Media Platforms, Phishing Attacks Have Become A Major Cybersecurity Threat. The Proposed System Analyzes URL-based, HTML-based, And Derived Features Using Advanced Feature Engineering And Machine Learning Models To Classify URLs As Legitimate Or Malicious Before User Interaction. Unlike Traditional Systems That Rely On Reactive Detection, PhishNet AI Performs Real-time Analysis And Blocks Harmful Links At An Early Stage. The System Integrates Multiple Security Mechanisms Such As Web Application Firewall (WAF), Intrusion Detection And Prevention Systems (IDS/IPS), Bot Detection, And Secure Communication Using TLS And OAuth/JWT Authentication. It Is Optimized For Mobile Devices, Ensuring Low Latency, Minimal Resource Consumption, And High Accuracy.

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Other Details

• Paper id:

  IJSARTV12I3104784

• Published in:

  Volume: 12 Issue: 3 March 2026

• Publication Date:

  2026-03-26

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