AI-powered Log Intelligence System - Semantic search, anomaly detection, and root cause analysis for logs using RAG techniques. Built with Rust, Axum, NATS, ClickHouse, and Qdrant.

LCPFS: LCP File System - Memory-safe ZFS alternative with GPU compression, AI anomaly detection, and post-quantum cryptography.

This project is an AI-powered anomaly detection system that monitors social media trends, physical environmental conditions, and stock market behavior in real time. It correlates signals across multiple domains using machine learning and LLMs to detect abnormal patterns and explain why anomalies occur and what they may indicate.

An AI-based system utilizing Graph Neural Networks (GNNs) for real-time anomaly detection and fault diagnosis in spacecraft engines. It classifies anomalies and provides actionable recommendations, improving safety and predictive maintenance.

Proactive threat anticipation through AI-driven anomaly detection, seamlessly integrated with SIEM systems, empowering scalable security vigilance Engine.

This project integrates Explainable AI (XAI) techniques for anomaly detection in encrypted network traffic using ML Algorithms. We employ SHAP (SHapley Additive Explanations) to interpret model decisions and enhance transparency in detecting malicious activities. The system is designed to identify suspicious patterns in encrypted traffic.

AUTOMATA02 – An intelligent workspace automation hub built with Python. It combines smart file sorting, UI workflow learning, and autonomous data reporting into one system. Powered by pandas, Streamlit, and AI/ML for intelligent classification, anomaly detection, and personalized dashboards.

AI-based system for anomaly detection and self-healing automation

AI-Based Network Anomaly Detection System

AI-driven system for real-time log monitoring and anomaly detection using python

This project implements an AI-driven network traffic anomaly detection system using machine learning techniques. It provides real-time analysis of network traffic patterns, detects potential security threats, and offers mitigation recommendations.

🛡️ AI-Powered Border Surveillance System | Real-Time Anomaly Detection + YOLOv8 Object Detection + Azure Cloud + Streamlit Dashboard | GTU Internship Project 2026

Develop an AI-based system to detect and respond to cyber threats in real time. This project demonstrates skills in cybersecurity, machine learning, and anomaly detection.

A real-time anomaly detection system for HDFS logs using AI tools including Hugging Face, Chroma, and LangChain. The system includes a modern web interface built with React and Tailwind CSS.

An AI-driven DevOps platform that enables autonomous fault detection and recovery in Kubernetes environments. Uses anomaly detection and predictive failure modeling to identify system instability, trigger automated remediation, optimize resource allocation, and enhance production reliability at scale.

Self-Healing Cybersecurity System 🛡️ An AI-powered cybersecurity system that detects network attacks in real-time and automatically applies countermeasures like IP blocking and logging. Uses machine learning for anomaly detection and integrates with the system firewall for enhanced security. 🚀🔐

Enterprise AI Agent monitoring system for OpenClaw. Features real-time gateway monitoring, cost tracking, multi-channel status, and anomaly detection.

AI-powered fraud detection system for banks using behavioral anomaly detection

The ultimate workbench for research & development of AI-powered anomaly-based Intrusion Detection Systems (IDS)

SentinelSec is a comprehensive, offline-first Intrusion Detection System (IDS) built with Python. It combines real-time packet monitoring, AI-based anomaly detection, CVE vulnerability intelligence, and rule-based threat detection in a single, powerful platform.

This project implements a real-time anomaly detection system using unsupervised machine learning models and AI-driven solutions. It integrates components such as data ingestion from Kafka, model training, anomaly detection, real-time alerting, object detection in CCTV footage using YOLO, and deployment to AWS Lambda or Google Cloud.

AI-based intrusion detection system that analyzes network traffic or system logs to detect potential security threats and anomalous activities. Utilize machine learning algorithms, such as anomaly detection or behavioral analysis, to identify patterns indicative of cyber attacks.

Sentinair is an advanced offline AI-based Intrusion Detection System tailored for isolated environments such as air-gapped military, industrial, or banking systems. It monitors system behavior patterns and flags anomalies using machine learning - all without needing internet connectivity.

POC for real-time monitoring and alert system for home hemodialysis, detecting critical risks like severe hypotension and blood loss. That would Integrate with NxStage setups to provide automated emergency detection, caregiver alerts, and AI-driven anomaly detection ensuring patient safety even without immediate medical assistance.

AI-Powered Modular Security Layer for DeFi Protocols This project was built for the BlockDAG Hackathon. It introduces a real-time, modular security system designed to protect DeFi smart contracts from fraud, flash loan exploits, and vault vulnerabilities using AI-based anomaly detection and on-chain guardrails.

Intrusion Detection and Prevention Systems (IDSs/IPSs) are crucial defenses against evolving network attacks. However, due to a lack of reliable datasets, anomaly-based intrusion detection faces challenges in consistent and accurate performance. We've developed an AI-based system for precise intrusion detection

AI-powered robot monitoring system with real-time telemetry simulation, anomaly detection, and chat insights using FastAPI, MindsDB + Gemini, Angular, and PostgreSQL on AWS EC2 provisioned with Terraform.

Physics-informed machine learning system for anomaly detection and fault diagnosis in nuclear power plants, combining thermodynamic laws with AI to improve safety, reliability, and early fault prediction.

watcher.service It is an anomaly detection system in Linux system using the basic principles of creating this machine and does not use AI for analysis because errors may occur in the code. It uses the principle of Log instead of using AI.

This is an advanced Intrusion Detection System (IDS) that leverages Artificial Intelligence (AI) and Snort to detect network anomalies in real time. The system is designed to enhance cybersecurity by identifying malicious traffic, unauthorized access, and potential cyber threats using machine learning models and signature-based detection.