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TRAINED ML MODEL SIGNALS

Spot Suspicious Emails.
Understand the Risk.
Stay Alert.

PhishSense analyses email text and links to flag potential phishing risks and help you decide what to check before taking action.

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3Risk Levels
2Trained Models
1Connected Workflow
ABOUT PHISHSENSE

A Safer Inbox
Starts With Awareness

PhishSense helps users review visible email content before they take action. Its trained ML models score email text and visible links; the result supports caution rather than confirming safety.

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Detect.
Understand.
Make Safer Decisions.

Technology that supports people for a more secure digital tomorrow.

OUR FEATURES

PhishSense Features

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User Dashboard

View your scan results, recent activity and saved history.

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Alerts System

See alerts for emails flagged by a scan and review their details.

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User Phishing Reporting

Submit a manual report when an email seems suspicious.

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Historical Analytics

Explore summaries and trends from recorded scan activity.

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Browser Extension

Scan an open Gmail message directly from your browser.

HOW IT WORKS

How an Email Scan Works

Follow an email from the browser extension scan to the result shown in PhishSense.

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1

User Opens Email

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2

Browser Extension Scans Email

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Email Content Analysed

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Detection Result Generated

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Alert Displayed

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Result Stored in Database

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User / Admin Dashboard

SHARE YOUR FEEDBACK

We'd Love to Hear From You

USER REVIEWS

What People Say About PhishSense

★★★★★

“The explanations make it easier to understand why an email was flagged.”

DDemo ReviewExample feedback
★★★★★

“The browser workflow is simple and the risk information is easy to follow.”

DDemo ReviewExample feedback
★★★★★

“Useful for demonstrating phishing awareness and explainable detection.”

DDemo ReviewExample feedback
OUR TEAM

Meet the People Behind PhishSense

Muhammad Ariff Iman bin Hayazi

Muhammad Ariff Iman bin Hayazi

Project Lead

Coordinates the project scope, integration and final delivery.

Madihah Batrisyia binti Fakhrurrazi

Madihah Batrisyia binti Fakhrurrazi

Machine Learning

Works on model evaluation, integration and risk-band logic.

Nur Izzati Qistina binti Noraizam

Nur Izzati Qistina binti Noraizam

Backend

Builds APIs, database flow, history and dashboard integration.

Muhammad Mus’ab bin Zulkarnain

Muhammad Mus’ab bin Zulkarnain

Frontend

Develops the browser experience and user-facing interfaces.

Ms Jumamelissa binti Sani

Ms Jumamelissa binti Sani

Project Supervisor (SV)

Provides academic guidance and supervises the PhishSense project.

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Be Part of a Safer Digital World

Start using PhishSense today and take a step towards a safer inbox.

PHISH SENSE
Machine-Learning Phishing Analysis Prototype
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