Anomaly Detection projects for M.E., M.Tech, Masters, MS abroad, and PhD students. These Anomaly Detection projects are designed for final year project submissions, research work, and publishing research papers. These research projects guide 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 Anomaly Detection Projects
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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. -
LogFiT: Log Anomaly Detection Using Fine-Tuned Language Models
This project is about detecting unusual events in computer system logs. The proposed system, called LogFiT, can learn normal log patterns on its own without needing labeled data. It uses a smart language model to understand log content and identify when something abnormal happens. Tests show that LogFiT is more accurate and flexible than existing methods. -
Physical Layer Spoof Detection and Authentication for IoT Devices Using Deep Learning Methods
This project focuses on making Internet of Things (IoT) devices more secure. It uses a smart system to recognize each device by its unique radio signals. The method works without making the devices more complex. Tests show it can detect fake devices and correctly identify real ones with over 90% accuracy. -
Application of Statistical Analysis and Machine Learning to Identify Infants Abnormal Suckling Behavior
This project studies how babies suck in the first month of life. Researchers used a simple device to measure how hard and often babies suck without feeding. They applied computer analysis and machine learning to spot babies with unusual sucking patterns. This can help doctors identify feeding problems early and guide breastfeeding support or treatment. -
Abnormality Detection in Chest X-Ray via Residual-Saliency From Normal Generation
This project develops a method to detect diseases in chest X-rays by first creating a “normal” version of each X-ray. The system learns to erase abnormalities using synthetic image pairs. It then finds differences between the original and normal images to highlight disease areas. The method improves detection by training the model with both real and artificially generated images. -
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. -
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. -
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. -
A Review of Recent Advances Challenges and Opportunities in Malicious Insider Threat Detection Using Machine Learning Methods
This project focuses on detecting threats that come from within an organization, such as employees misusing their access. It reviews traditional and modern methods used to identify such insider threats. The study shows that deep learning and language-based models are more effective in recognizing unusual or harmful behavior. It also suggests using time-based data to improve future threat detection. -
A Review on the Evaluation of Feature Selection Using Machine Learning for Cyber-Attack Detection in Smart Grid
This project studies how to protect the smart power grid from cyber-attacks. It explains the weak points in the system and how hackers can target them. The study compares different methods to detect attacks, such as using rules, patterns, and machine learning. It also explores how new technologies like AI and blockchain can make the grid more secure in the future. -
Bearing Fault Detection and Recognition From Supply Currents With Decision Trees
This project uses machine learning to detect faults in electric motor bearings by analyzing current signals. It focuses on using decision trees, which can explain how they make decisions in a simple way. The method works well even when tested on new and unseen motor load conditions. It achieved more than 90% accuracy in identifying bearing faults. -
Comparative Analysis of Predictive Algorithms for Performance Measurement
This project studies how computers can use past data to predict future outcomes more accurately. It compares many types of machine learning methods to find which ones work best for different kinds of data. The study shows that advanced models like ROBERTA, ResNet, Random Forest, and K-means give better results. This helps researchers choose the right algorithm for their prediction tasks. -
Identification of Congenital Valvular Murmurs in Young Patients Using Deep Learning-Based Attention Transformers and Phonocardiograms
This project uses artificial intelligence to detect heart problems in newborns and children. It listens to heart sounds recorded from the chest and learns patterns linked to heart defects. The model can identify abnormal heart murmurs early and accurately without needing expensive machines or expert doctors. This makes it easier and faster to check many children, especially in low-resource areas.
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