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AI-Powered Threat Prediction Dashboard

Why Choose This Project

In modern cybersecurity operations, predicting potential threats before they occur is critical to preventing breaches and minimizing damage. Traditional reactive approaches often leave organizations vulnerable to attacks. This project leverages AI and machine learning to analyze historical security events, network traffic, and behavioral patterns to forecast potential threats and provide actionable insights. It is ideal for security teams, SOC analysts, and enterprises aiming for proactive defense.

What You Get

A web-based threat prediction dashboard that collects security logs, analyzes anomalies using AI/ML models, and visualizes potential threats. Users can receive real-time alerts, view predictive analytics, and monitor risk trends. The system provides detailed reporting for proactive decision-making and incident prevention.

Key Features

Feature	Description
User Authentication	Secure login for admins and analysts with role-based access
Data Ingestion	Collect logs from network devices, servers, and applications
AI/ML Threat Analysis	Apply machine learning models to predict suspicious activity and potential attacks
Anomaly Detection	Identify unusual patterns in user behavior, network traffic, or system events
Real-Time Alerts	Notify admins via email/SMS for predicted high-risk events
Dashboard Visualization	Interactive graphs, heatmaps, and trend charts for threat predictions
Risk Scoring	Assign risk levels (low, medium, high, critical) to predicted threats
Reporting Module	Generate predictive reports for security audits and proactive mitigation
API Integration	Integrate with SIEM, firewall, and other monitoring tools
Historical Analysis	Track past incidents and compare with AI predictions to improve model accuracy

Technology Stack

Layer	Technology
Frontend Layer	HTML, CSS, JavaScript, Bootstrap, optional React.js or Vue.js for dynamic dashboards
Backend Layer	Node.js (Express) / Java Spring Boot / Python Flask
Database Layer	MongoDB / MySQL / PostgreSQL for storing logs, alerts, and predictive data
AI/ML Layer	Python (scikit-learn, TensorFlow, PyTorch) for predictive analytics and anomaly detection
Security Layer	HTTPS, JWT/OAuth2 for authentication and role-based access
Optional Libraries & APIs	Chart.js/D3.js for visualizations, Email/SMS APIs for alerts, REST APIs for integration

Working Flow

Data Collection – Gather security logs from multiple sources (servers, firewalls, applications).
Preprocessing & Feature Extraction – Clean and transform data for AI/ML models.
Threat Prediction – Machine learning models analyze patterns and predict potential threats.
Risk Scoring & Categorization – Assign risk levels based on threat probability and impact.
Alert & Notification – Real-time notifications sent to admins for high-risk predictions.
Visualization Dashboard – Display predicted threats, risk scores, and trends in interactive charts.
Reporting & Analysis – Generate detailed reports for decision-making and compliance review.

Main Modules

Authentication Module → Secure admin and analyst login
Data Ingestion Module → Collect and normalize logs from multiple sources
AI/ML Engine Module → Analyze patterns, predict threats, and score risk
Alert Module → Send notifications via email/SMS for high-risk predictions
Dashboard Module → Visualize threat predictions, trends, and analytics
Reporting Module → Generate reports for incidents, risk scores, and historical trends
API Module → Integrate predictions with SIEM and monitoring tools