Project Title: Enhancement and Debugging of a DRL-FL-IDS-IoT Model -- 2

- Overview: We are seeking a skilled Python programmer with specialized experience in machine learning to undertake a pivotal project that involves a two-phase enhancement of an Intrusion Detection System (IDS) for Internet of Things (IoT) devices. The project will first focus on refining and debugging Deep Reinforcement Learning (DRL) and Federated Learning (FL) components. Following this, we will implement privacy enhancements using Secure Aggregation techniques to improve data privacy. - Project Phases: Phase 1 - DRL and FL Implementation: Debug and optimize existing Python code focusing on DRL and FL for robust anomaly detection. Implement and test algorithms based on specific academic research to ensure the model adheres to the latest methodologies in IoT security. Provide regular updates and progress reports, ensuring all developmental milestones are met within the stipulated timelines. Phase 2 - Privacy Enhancement: After establishing a robust DRL-FL framework, integrate Security techniques to enhance data privacy. Adjust and test the existing model to incorporate privacy features without compromising the anomaly detection capabilities. Continue to provide detailed documentation and regular progress updates throughout this phase. - Ideal Candidate: 1- Proficient in Python with a strong background in both Deep Reinforcement Learning and Federated Learning. 2- Experienced in numeric data classification and handling security-related tasks in IoT environments. 3- Knowledgeable about privacy-enhancing technologies, especially in the context of federated learning systems. 4- Capable of following a phased development approach, with a strong emphasis on achieving high accuracy in anomaly detection and privacy. - Additional Requirements: 1- Demonstrable experience with libraries like PyTorch is highly desirable. 2- Prior work on IoT security projects, particularly involving data privacy, is advantageous. 3- Excellent problem-solving abilities and capable of providing comprehensive documentation and regular communication. Deliverables: 1- A comprehensive code repository with all debugging solutions and enhancements made during both phases. 2- Detailed documentation explaining all code changes and the integration of privacy technologies. 3- Periodic progress reports highlighting milestones achieved and issues encountered. Each phase is expected to be completed within 1 month.