AI-Driven UAV Collision & Intrusion Detection System enhances drone safety by combining YOLOv8 object detection, synthetic flight path generation, collision prediction, and intrusion alerts. A real-time Streamlit dashboard consolidates risks and events, ensuring safer UAV operations in congested airspaces.

This project pioneers an AI-driven Web Application Firewall (WAF) and Intrusion Prevention System (IPS) to fortify web security, utilizing dynamic clustering for real-time threat detection and mitigation. I contributed by developing programs from scratch and creating comprehensive documentation for the system's functionality.

An AI-driven Network Intrusion Detection System utilizing CNN and LSTM models trained on the UNSW-NB15 dataset to detect and classify malicious traffic with high precision. It features a web-based dashboard for real-time monitoring, offering both binary and multi-class classification to secure networks against diverse cyber threats.

AI-driven network intrusion detection system that leverages multiple machine learning algorithms to identify malicious network activities in real-time.

AI-Driven UAV Collision & Intrusion Detection System enhances drone safety by combining YOLOv8 object detection, synthetic flight path generation, collision prediction, and intrusion alerts. A real-time Streamlit dashboard consolidates risks and events, ensuring safer UAV operations in congested airspaces.

SentinelNet is an AI-driven Network Intrusion Detection System that detects and classifies cyber threats using machine learning. Featuring real-time traffic monitoring, anomaly detection, and an interactive Streamlit dashboard, it provides a modern and accurate solution to network security using the CICIDS 2017 dataset.

AI-driven Intrusion Detection System for IoT networks using XGBoost, trained on CICIoT2023 dataset. Achieves 99.25% accuracy on 34 attack types. Integrated with Suricata for real-time packet logging and Telegram for instant alerts. Built for secure, scalable IoT environments.

SecIDS ist ein leistungsfähiges, KI-gestütztes Echtzeit-IDS (Intrusion Detection System)

AI-Driven Intrusion Detection system using Machine Learning to detect cyber threats. Built with Flask, ML models (Random Forest, AdaBoost, LightGBM, MLP), and features real-time predictions, visualizations, user authentication, and cloud dataset support.

This project presents a Domestic AI-Driven Intrusion Detection System, an intelligent solution that safeguards home and small-scale networks using machine learning-based anomaly detection, threat intelligence, and a real-time dashboard for smart, autonomous, and explainable network defense.

An AI-driven Intrusion Prevention System (IPS) integrated with File Integrity Monitoring (FIM) and Network Traffic Analysis that detects and prevents insider threats through real-time anomaly detection, context-aware analysis, and adaptive automated response using Explainable AI (XAI).

CyberSecure is a real-time AI-driven Intrusion Detection & Automated Response System developed under intense 24-hour constraints at the REDACT Hackathon 2025. The system supports SOC teams by detecting malicious network flows and suggesting immediate mitigation actions — powered by a high-recall ML model.

This project implements an AI-driven Intrusion Detection System (IDS) to detect and classify cyber threats in real-time. By leveraging machine learning and deep learning algorithms, this IDS analyzes network traffic patterns to identify malicious activities such as DDoS, port scanning, and brute-force attacks.

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AI-Driven Cybersecurity Intrusion Detection System Using Machine Learning and Real-Time Web Visualization

AI-driven Network Intrusion Detection System using machine learning for real-time traffic analysis and attack detection

This is a complete AI-powered anomaly detection system that identifies suspicious user activities and automatically responds to threats. AI-Based Intrusion & Behaviour Anomaly Detection System This project implements an AI-driven security system designed to detect behavioural anomalies and potential intrusions in real time.

Build an AI-driven intrusion detection system for tactical networks, identifying zero-day attacks in real-time with blockchain for secure logging in disconnected ops.

AI_IDS is a modern, web-based Intrusion Detection System (IDS) built with Django and enhanced with AI-driven analytics to monitor and detect suspicious network activity in real time.

Developed a Forward Intrusion Detection System using microcontrollers and AI for real-time rogue Wi-Fi detection and threat analysis. Integrated Tensor Flow for AI-driven malware analysis, enhancing cybersecurity defenses. Built a live monitoring dashboard with Flask to visualize threats in real time.

Adaptive AI-Driven Intrusion Detection & Automated Response System (AIDARS) is a learning-focused project that combines cybersecurity, cloud, and AI/ML to detect, classify, and respond to cyber attacks in real time.

IntrusionAI is an AI-driven Network Intrusion Detection System (NIDS) that leverages machine learning to identify potential security threats. Using an XGBoost model, IntrusionAI analyzes network traffic data to detect anomalies and potential intrusions in real-time.

An AI-driven Network Intrusion Detection System (IDS) that uses Suricata for traffic monitoring and a Machine Learning (Random Forest) model to detect and automatically block cyber attacks in real-time using IPTables

An in-depth look at how modern enterprises can leverage AI-native security solutions to enhance system resilience. Highlighting AI-driven anomaly detection and autonomous threat mitigation using technologies such as TensorFlow for real-time intrusion detection and AutoGPT for predictive threat modeling.

Full-stack AI-driven Web App Intrusion Detection System (IDS) with React dashboard. Real-time detection of XSS, SQLi, brute force, and more. Includes analytics, IP blocking, CSV export, and a demo vulnerable app for hands-on security testing.

AI-driven Intrusion Detection System built on kali Linux. Captures live network traffic, extracts key features, and uses machine learning to detect malicious behavior in real-time. Built for research, red-teaming, and advanced threat analysis.

AI-Driven Threat Detection is a mini Intrusion Detection System (IDS) that learns patterns in network/login logs and flags anomalies in near real-time. It combines machine learning (Isolation Forest), a REST API, a mini dashboard, and a streaming log agent.

KEVLAR is an AI-driven intrusion detection and prevention system that analyzes network traffic in real time, detects anomalies using ML, maps threats to the MITRE ATT&CK framework, and autonomously blocks malicious activity while providing actionable insights.