Phishnet: Intelligent Threat Analysis System Against Phishing Attacks

• Author(s):

  Chobe Pranali | Ubale Anjali | Malani Krishi | Jangam Atharv | Prof. Bhalchandra Ban

• Keywords:

  Phishing Detection, Artificial Intelligence, Machine Learning, NLP, Cybersecurity, Threat Intelligence System.

• Abstract:

  Digital Chat’s Fast Rise Makes Smart Online Protection More Urgent, Especially To Spot And Stop Fake Message Scams On Different Apps. PHISHNET Uses AI To Check Threats In Many Channels, Combining Learning Machines, Number Crunching, And Human Behavior Clues Into One Working Setup. It Runs Sharp Codes To Catch Phishing Faster And Sort It Automatically By Studying Links, Inbox Notes, Or Texts Using Models That Get The Situation. Instead Of Just Reading Words, NLP Figures Out What Messages Really Mean; At The Same Time, Tools Like Gradient Boosting Plus Random Forest Flag Shady Traces And Odd Actions. Behind The Scenes, Flask Parts Made With Python Team Up With A Flexible Mongo Storage Unit So Info Flows Quick And Adjusts Easy. In Online Spaces, This Approach Boosts Toughness While Lifting Precision And Cutting Down On Manual Fixes. Coming Updates Will Support Smart Adaptation Using Mixed Algorithms, Secure Tracking Via Decentralized Ledgers, Also Foresight Into Emerging Risks - Showing How Machine-driven Systems Can Shift Cyber Protection Toward Self-running, Clear, Ahead-of-time Shields. Spotting Fake Messages, Smart Computers That Learn By Doing, Teaching Machines How To Understand Human Talk, Keeping Online Spaces Safe, Tools That Catch Digital Dangers Before They Strike

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Other Details

• Paper id:

  IJSARTV11I11104281

• Published in:

  Volume: 11 Issue: 11 November 2025

• Publication Date:

  2025-11-12

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