Intrusion Detection System Final Year Projects with Source Code

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Intrusion Detection System Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Intrusion Detection System 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.

Intrusion Detection System 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. A Novel Two-Stage Deep Learning Model for Network Intrusion Detection LSTM-AE
    This project focuses on improving computer systems’ ability to detect cyber-attacks automatically. It uses a combination of two advanced deep learning methods, LSTM and Auto-Encoders, to create a flexible and accurate intrusion detection system. The model is tested on publicly available datasets to find the best settings and compare its performance with other deep learning approaches. Results show that the proposed system can effectively detect attacks in modern network environments.
  3. A Quantitative Logarithmic Transformation-Based Intrusion Detection System
    This project focuses on building a system to detect network attacks without using complex machine learning. It analyzes network behavior using simple statistical methods, making it fast and easy to run in real time. The system can identify different types of attacks in both real and simulated network traffic. Tests show it detects threats accurately, even when data is limited.
  4. An Attention-Based Convolutional Neural Network for Intrusion Detection Model
    This project focuses on improving network security by detecting intrusions quickly and accurately. It uses a type of artificial intelligence called convolutional neural networks with attention mechanisms. The method organizes network data into images in a smart way to make the detection process faster. Experiments show that this approach can identify threats efficiently while keeping high accuracy.
  5. Comparative Analysis of Intrusion Detection Systems and Machine Learning-Based Model Analysis Through Decision Tree
    This project focuses on improving computer network security by detecting cyber-attacks more accurately. It reviews different intrusion detection methods, datasets, and challenges faced by researchers. Machine learning and deep learning are used to identify threats and reduce false alarms. The study proposes using a decision tree model to create an efficient system for spotting unusual activity in networks.
  6. Applying One-Class Algorithms for Data Stream-Based Insider Threat Detection
    This project focuses on detecting insider threats, which are harmful actions by people who have authorized access to an organization’s network. Traditional security tools often fail, so the study uses machine learning to improve detection. It combines supervised and semi-supervised learning, analyzes data streams, and retrains models periodically. The best results were achieved using the Isolation Forest algorithm, showing good accuracy in identifying both harmful and safe activities.
  7. 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.
  8. A Novel Energy-Efficient Scheme for RPL Attacker Identification in IoT Networks Using Discrete Event Modeling
    This project focuses on making IoT networks more secure. The researchers created a system that can detect hidden attacks in IoT devices that are hard to spot. Their method uses smart checking and a special model to tell normal activity from attacks. Tests show it works accurately, uses little energy, and can find the harmful devices without complex setup.
  9. An Intelligent Approach to Improving the Performance of Threat Detection in IoT
    This project focuses on making Internet of Things (IoT) systems more secure. It uses machine learning and data analysis techniques to detect attacks that try to overwhelm the system, known as DDoS attacks. The researchers tested their approach using real datasets and measured how well the system could detect attacks and how fast it could learn. Overall, their method improved both detection accuracy and training speed.
  10. Anomaly-Based Intrusion on IoT Networks Using AIGAN-a Generative Adversarial Network
    This project studies how cyber attackers can trick smart security systems that protect computer networks. It focuses on poisoning attacks, which feed fake data to these systems to make them fail. The researchers use a type of deep learning called GAN to create realistic fake data and test how well security systems can detect it. Their experiments show that many machine learning models used in network security can be fooled by such attacks.
  11. 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.
  12. Enhancing DDoS Attack Detection Using Snake Optimizer With Ensemble Learning on Internet of Things Environment
    This project focuses on protecting Internet of Things (IoT) devices from DDoS cyber-attacks that can overload them with traffic. It uses machine learning to detect attacks by selecting the most important data features. The proposed method combines a “snake optimizer” for feature selection with three deep learning models to improve detection. Tests show that this approach works better than existing methods in identifying attacks accurately.
  13. Enhancing Intrusion Detection in IoT Communications Through ML Model Generalization With a New Dataset IDSAI
    This project focuses on improving computer security in networks of connected devices, like IoT systems. The researchers created a new dataset of real attacks to train and test machine learning models. They found that certain AI models can accurately detect both simple and multiple types of attacks, reaching over 90% accuracy. This work helps make network security smarter and more reliable.
  14. Modeling of Blockchain Assisted Intrusion Detection on IoT Healthcare System Using Ant Lion Optimizer With Hybrid Deep Learning
    This project focuses on making healthcare IoT systems more secure. It uses blockchain to protect sensitive medical data and detects unauthorized access using smart algorithms. The system combines advanced deep learning methods with optimization techniques to improve accuracy in spotting intrusions. Tests show it works better than existing methods for keeping IoT healthcare systems safe.
  15. Securing IoT With Deep Federated Learning A Trust-Based Malicious Node Identification Approach
    This project builds a system that helps smart devices identify which devices in a network can be trusted. It uses learning from many devices together without sharing their data. The system can detect unusual or harmful behavior in the network. This helps improve safety and privacy in Internet of Things environments.
  16. Toward Secured IoT-Based Smart Systems Using Machine Learning
    This project studies how smart systems like smart cities and early warning systems use sensors and devices to collect data. Machine learning is applied to this data to make predictions and improve decision-making. The research also examines security methods to keep these systems safe. Two case studies on smart campuses and earthquake warning systems show how this works in practice.
  17. A Bayesian Deep Learning Approach With Convolutional Feature Engineering to Discriminate Cyber-Physical Intrusions in Smart Grid Systems
    This project focuses on protecting smart power grids from cyber attacks. It combines a Bayesian method with deep neural networks to detect unusual or harmful activities in the grid. The system can tell normal events from attacks more accurately than traditional methods. Tests on real industrial data show it works better than older approaches.
  18. 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.
  19. 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.
  20. An Efficient Approach for the Detection and Prevention of Gray-Hole Attacks in VANETs
    This project focuses on improving the security of vehicular networks, where vehicles communicate with each other for safety and traffic management. The researchers studied a type of cyberattack called Gray-Hole Attack, where malicious vehicles drop data packets. They proposed a new method to detect and prevent these attacks by monitoring unusual packet behavior. Simulations showed that their method improved data delivery, reduced delays, and performed better than existing approaches.
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