Intrusion Detection System Projects for ME, MTech, Masters, MS abroad, and PhD students. These Intrusion Detection System ieee projects are implemented with future work and extension for final year students with research paper writing and publishing. These research projects guide final year students to learn, practice, and complete their academic submissions successfully. Each project includes complete source code, project report, PPT, a tutorial, documentation, and a research paper.
Latest Intrusion Detection System Projects
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A Situation Based Predictive Approach for Cybersecurity Intrusion Detection and Prevention Using Machine Learning and Deep Learning Algorithms in Wireless Sensor Networks of Industry 4.0
The project aims to improve cybersecurity in wireless sensor networks used in Industry 4.0. It focuses on detecting and preventing cyber-attacks in real time. Machine learning and deep learning algorithms are applied to classify and prioritize threats. The framework uses Decision Tree and MLP models for multi-class intrusion detection and an Autoencoder for binary classification. The goal is to provide accurate, intelligent, and prioritized protection for industrial networks. -
Enhancing DDoS Attack Detection and Mitigation in SDN Using an Ensemble Online Machine Learning Model
The project aims to improve DDoS attack detection in Software Defined Networks using machine learning. It develops an ensemble online learning model that adapts to new and evolving attacks. The model selects features dynamically to enhance detection accuracy. It is tested in SDN simulations and benchmark datasets. The goal is to provide proactive and reliable protection against diverse DDoS threats. -
A Comparative Study of Anomaly Detection Techniques for IoT Security Using Adaptive Machine Learning for IoT Threats
This project presents a system called FusionNet that can automatically find unusual or suspicious data patterns. It combines several machine learning methods to improve accuracy. The model was tested on two datasets and performed better than older methods. FusionNet can be used in areas like security and healthcare to detect problems early and accurately. -
Cloud Network Anomaly Detection Using Machine and Deep Learning Techniques Recent Research Advancements
This project studies ways to keep cloud networks safe from unusual or harmful activity. It looks at how machine learning and deep learning can detect problems like intrusions or attacks. The research compares different methods and suggests better ways to find anomalies. The goal is to make cloud networks more secure and reliable. -
Efficacious Novel Intrusion Detection System for Cloud Computing Environment
This project focuses on improving security in cloud computing by detecting cyber attacks. The researchers created a system that selects the most important data features to make detection faster and more accurate. They combined decision trees and neural networks to identify intrusions. Tests show that their method works better than existing techniques. -
Event-Based Moving Target Defense in Cloud Computing with VM Migration: A Performance Modeling Approach
This project focuses on improving computer security in cloud systems. It uses a method that changes system settings, like moving virtual machines or changing IP addresses, to confuse attackers. The study models these changes to find the best ways to keep systems safe. It shows that using event-based detection works well when the system can accurately detect threats more than half the time. -
Secure Data Dissemination Scheme for Digital Twin Empowered Vehicular Networks in Open RAN
This project focuses on making communication between smart vehicles safer and more reliable. It creates a system where vehicles can verify each other and trust the information they share. It uses virtual models of the network, machine learning to spot unusual activity, and blockchain to confirm data. Tests show that this approach works better than many existing methods. -
A Systematic Analysis of Enhancing Cyber Security Using Deep Learning for Cyber Physical Systems
This project focuses on protecting cyber-physical systems, which are systems where computers control real-world devices. These systems are vulnerable to cyber-attacks, which are hard to detect. The project studies how deep learning can be used to identify attacks effectively. It also reviews existing methods and discusses future challenges in this area. -
IP2FL: Interpretation-Based Privacy-Preserving Federated Learning for Industrial Cyber-Physical Systems
This project focuses on making industrial systems smarter and safer. It develops a model that can detect unusual activities in industrial networks without exposing sensitive data. The approach protects privacy while explaining how decisions are made by the system. Tests show it works well on real industrial data. -
Intrusion Detection in Cyber-Physical Grid Using Incremental ML With Adaptive Moment Estimation
This project develops a smart system to detect cyber-attacks in power grids. Unlike traditional methods, it can learn and adapt to new, unknown attacks without starting from scratch. The system uses a neural network that updates itself with new threat information. Tests show it can detect attacks with very high accuracy in both simulated and real datasets. -
Securing Cyber-Physical Systems: A Decentralized Framework for Collaborative Intrusion Detection With Privacy Preservation
This project focuses on protecting critical systems from cyber-attacks. It studies ways to detect network intrusions using smart learning methods. The approach allows multiple organizations to train a shared detection model without sharing their private data. The results show it can accurately identify attacks while keeping data secure. -
A Comparative Study of Lightweight Machine Learning Techniques for Cyber-Attacks Detection in Blockchain-Enabled Industrial Supply Chain
This project focuses on improving the security of industrial supply chains using modern technology. It combines blockchain and smart sensors to protect systems from cyber-attacks. Machine learning models are used to detect threats early. The study compares different models to find the most effective and efficient one for small devices. -
Survey: Intrusion Detection System in Software-Defined Networking
This project studies the security challenges in modern computer networks called Software-Defined Networks (SDN). It looks at how these networks, while flexible and efficient, are vulnerable to attacks like DDoS and SQL injection. The work analyses these threats and suggests ways to build better intrusion detection systems. The goal is to make SDN networks safer and more reliable for future use. -
SIMC 2.0: Improved Secure ML Inference Against Malicious Clients
This project improves the security and speed of machine learning predictions. The goal is to make sure a client only sees the result while the server learns nothing. The researchers developed new methods to make calculations faster and reduce data transfer. Their approach, SIMC 2.0, is much quicker and more efficient than earlier methods. -
A Comprehensive Survey: Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
This project studies how artificial intelligence can help protect against cyberattacks. It explains how machine learning, deep learning, and reinforcement learning can detect threats and improve security. The study also looks at using AI tools like ChatGPT in cybersecurity, both as helpers and as potential risks. Overall, it shows how AI can make systems safer but also highlights challenges and vulnerabilities. -
Rule-Based With Machine Learning IDS for DDoS Attack Detection in Cyber-Physical Production Systems (CPPS)
This project focuses on protecting industrial production systems from cyber attacks. It combines machine learning and rule-based methods to detect harmful network traffic in real time. The system was tested using actual data from a farm-to-fork supply chain. It can identify attacks accurately and provide clear information to help prevent damage. -
Automatic Evasion of Machine Learning-Based Network Intrusion Detection Systems
This project studies ways to bypass modern network security systems that use machine learning. The researchers show that even without knowing the system details, an attacker can trick it by slightly changing network traffic. They tested their method on several security systems and achieved a high success rate. The work also suggests ways to defend against such attacks. -
Evasion Attack and Defense on Machine Learning Models in CyberPhysical Systems: A Survey
This project studies how machine learning in cyber-physical systems can be attacked by hackers. It focuses on a type of attack called evasion attacks, where attackers trick the system by changing data. The work reviews current research on both attacks and defenses and organizes them into clear categories. It also points out gaps and future directions to make these systems safer. -
A Comprehensive Survey Evaluating the Efficiency of Artificial Intelligence and Machine Learning Techniques on Cyber Security Solutions
This project explains how artificial intelligence methods like machine learning, deep learning, and reinforcement learning help in protecting systems from cyberattacks. These methods can find hidden threats and improve security by learning from data. It also studies how tools like ChatGPT can be used both to enhance and to attack cybersecurity systems. The research highlights their benefits, challenges, and future importance in keeping data safe. -
A Novel Approach for Real-Time Server-Based Attack Detection Using Meta-Learning
This project creates a realistic virtual network to collect data on different types of cyberattacks. It uses this data to train an AI model that can detect attacks in real time with very high accuracy. The model combines two machine learning methods to improve prediction performance. The goal is to make network security stronger and provide a useful dataset for future research.
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Our experts guide students all over India, including in Mumbai, Delhi, Bangalore, Hyderabad, Ahmedabad, Chennai, Kolkata, Pune, Jaipur, and Surat. We also assist students in the USA, UK, Canada, Australia, Singapore, Malaysia, and Thailand. They have extensive experience in computer science, electronics, electrical and all engineering domains.
Intrusion Detection System Thesis and Dissertation Writing
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Intrusion Detection System Research Paper Publishing Support
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We help you prepare your project synopsis, including the problem definition, objectives, and motivation for your dissertation. Our team also provides complete PPT, documentation, and tutorials to make your final presentation successful. You can also download complete project resources, including source code, a project report, a PPT, a tutorial, documentation, and a research paper for your Intrusion Detection System final year project.
Intrusion Detection System Research Support for PhD Scholars
UniPhD offers advanced Intrusion Detection System research projects designed specifically for PhD scholars. We provide end-to-end support for your research design, implementation, experimentation, and publication process.
Each project package includes comprehensive documentation, including the research proposal, complete source code, research guidance, documentation, research paper, and thesis writing support, helping you successfully complete your doctoral research and academic publications.