Cyber-Physical Systems Final Year Projects with Source Code

Byadmin Final Year Projects

Cyber-Physical Systems Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Cyber-Physical Systems projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Cyber-Physical Systems Final Year Projects

  1. A Network Intrusion Detection System for Building Automation and Control Systems
    This project focuses on improving security for building automation systems, like smart lighting and HVAC controls. The researchers designed a network intrusion detection system that can detect attacks across different building system protocols, not just one. They built and tested a working version for KNX, a common building automation protocol, using a real installation to show it works. The system aims to make smart buildings safer from cyber threats.
  2. Attack Detection for Medical Cyber-Physical SystemsA Systematic Literature Review
    This project looks at cyber attacks in hospitals, focusing on medical cyber-physical systems, which include devices connected to hospital networks. The researchers reviewed existing studies to understand how intrusions are detected, what datasets are used, and what gaps exist. They found that most work focuses on detecting unusual activity at the network level, often targeting insider threats. The study suggests creating specialized hospital datasets, improving standards, and developing methods that use medical context to better prevent cyber attacks and protect patients.
  3. An Integrated Scalable Framework for Cloud and IoT Based Green Healthcare System
    This project focuses on creating a smart healthcare system using IoT and cloud technology. Patients can send their health data from wearable devices, and doctors can view it in real-time. The system uses advanced algorithms to analyze the data and provides an easy-to-use interactive interface. It also emphasizes efficiency, scalability, and making healthcare more environmentally friendly.
  4. APT Adversarial Defence Mechanism for Industrial IoT Enabled Cyber-Physical System
    This project focuses on detecting advanced cyberattacks in industrial systems connected through the Internet of Things. It uses a special machine learning method called Graph Attention Networks to identify hidden attacks more accurately than traditional methods. The approach was tested on real datasets and achieved over 95% detection accuracy in just around 20 seconds. Overall, it improves cybersecurity in smart industrial systems.
  5. Event Detection Through Differential Pattern Mining in Cyber-Physical Systems
    This project focuses on finding important events in sensor networks, like detecting damage in buildings or vehicles. It creates a system called DPminer that analyzes sensor data efficiently. The system looks for meaningful patterns across sensors while using less communication and computation. Tests show it detects events better than traditional methods.
  6. Scalable Uncertainty-Aware Truth Discovery in Big Data Social Sensing Applications for Cyber-Physical Systems
    This project focuses on improving how we find the truth in data collected from people about real-world events. It develops a method that checks both the reliability of the people reporting and the correctness of their data while considering uncertainty. The method is designed to work very fast using GPUs, making it suitable for large-scale events. Tests on real Twitter data show it is more accurate and much faster than existing approaches.
  7. Modified Red Fox Optimizer With Deep Learning Enabled False Data Injection Attack Detection
    This project focuses on protecting modern power systems that use many sensors and produce huge amounts of data. It aims to detect false data attacks that can harm energy efficiency. The method combines deep learning with an optimization algorithm to accurately identify and classify these attacks. Experiments show that this approach works better than existing methods.
  8. Quantum Dwarf Mongoose Optimization With Ensemble Deep Learning Based Intrusion Detection in Cyber-Physical Systems
    This project focuses on protecting smart systems that connect computers and physical devices. It uses a new method to detect attacks or intrusions in these systems. The approach selects important data features and combines multiple deep learning models to identify threats. Tests show it works better than traditional methods in detecting intrusions.
  9. 5G Aviation Networks Using Novel AI Approach for DDoS Detection
    This project develops an intelligent system to detect cyberattacks at airports using 5G networks. It converts network data into images and uses a combination of convolutional and recurrent neural networks to identify threats. The system achieves high accuracy in detecting attacks and performs well on multiple benchmark datasets. This approach helps improve security in modern smart airport infrastructures.
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