AI-Powered Cybersecurity Response System Development

Development of an Automated Incident Response System Using AI/ML and XDR Technology Project Description: We are looking for a skilled team or individual to design and develop an Automated Incident Response System that uses Artificial Intelligence (AI), Machine Learning (ML), and Extended Detection and Response (XDR) technology to enhance organizational cybersecurity. The goal is to build a system capable of real-time threat detection, analysis, and automated response to minimize the impact of sophisticated cyberattacks like ransomware, phishing, and Distributed Denial of Service (DDoS) attacks. This project involves backend development, AI/ML model creation, frontend dashboard design, and system integration. The completed system should be scalable, user-friendly, and capable of responding to incidents autonomously. Key Deliverables: Backend Development: Build a backend using Node.js that integrates with AI/ML models and external tools like XDR and Security Information and Event Management (SIEM) systems. APIs for: Log collection. Forwarding data to the ML model. Triggering incident responses such as isolating endpoints or blocking IPs. AI/ML Model: Create a Machine Learning model (using Python) to analyze logs and detect suspicious activities. Train the model on a dataset (e.g., network logs) to classify events as benign or threats. Implement the model using a Flask API to enable real-time predictions. Frontend Dashboard: Develop a responsive web-based dashboard using React.js to: Display logs and detected incidents in real-time. Allow users to view system status and trigger manual responses. Provide configuration options for response playbooks. Integration with XDR Tools: Connect the system to third-party XDR solutions (e.g., SentinelOne, CrowdStrike) to fetch logs and automate responses. Automation of Incident Response: Create predefined playbooks to automate actions like: Blocking malicious IP addresses. Isolating infected endpoints. Sending alerts to administrators via email, Slack, or SMS. Deployment: Containerize the system using Docker for easy deployment. Provide setup instructions for deploying on Kubernetes or cloud platforms like AWS/Azure. Key Requirements: Backend: Node.js, [login to view URL], RESTful API development. Integration with XDR and SIEM tools. Frontend: React.js or Angular.js for building the dashboard. AI/ML: Python libraries such as scikit-learn, TensorFlow, or PyTorch for training and deploying the ML model. Experience with anomaly detection and supervised/unsupervised learning. Database: MongoDB/MySQL for storing logs and incident history. Deployment: Knowledge of Docker and Kubernetes for containerization and scalability. Dataset: If you have access to any network logs or cybersecurity datasets, you can use them for training the ML model. If not, the freelancer will need to either use publicly available datasets (e.g., UNSW-NB15, CICIDS2017) or generate synthetic logs using Python or log generation tools.