NIDSMobile

Next-Gen Network Security
In Your Pocket

Real-time intrusion detection with machine learning powered by Android mobile technology.

Core Detection Features

Real-time Analysis

Continuous packet capture and analysis with <200ms latency for immediate threat detection.

ML Classification

8-class threat detection using Random Forest and Gradient Boosting with >95% accuracy.

Threat Types

Detects DoS/DDoS, PortScan, BruteForce, Botnet, WebAttack, Infiltration and more.

Push Alerts

Instant notifications with severity levels and actionable recommendations.

Visual Analytics

Interactive dashboards showing traffic patterns and threat visualizations.

Hybrid Detection

Combines signature-based detection with adaptive ML models for comprehensive coverage.

Technical Stack

Mobile Components

Android Platform

Java/Kotlin implementation with Material Design 3 and cyberpunk UI theme

pcap4j Integration

Packet capture library for real-time network traffic analysis

Data Visualization

MPAndroidChart for interactive threat analytics dashboards

Backend & ML

Spring Boot

Java backend with REST APIs for mobile communication

Machine Learning

SMILE/Weka for Random Forest, Gradient Boosting, and DL4J models

Datasets

Trained on CIC-IDS2017 & UNSW-NB15 cybersecurity datasets

Performance Metrics

>95%

Detection Accuracy

Across all threat categories with comprehensive testing

<2%

False Positive Rate

Minimizing unnecessary alerts for operational efficiency

<200ms

Response Time

From packet capture to threat classification

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