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

Anomaly Detection Final Year Projects

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.
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.
Constructing a Meta-Learner for Unsupervised Anomaly Detection

This project focuses on automatically picking the best anomaly detection method for any given dataset without needing labeled data. The researchers created a meta-learning system that looks at characteristics of the dataset to suggest the most suitable algorithm. They tested it on over 10,000 datasets and found it works better than existing methods. The study also shows that while a few dataset features are enough, the choice of the meta-learning model strongly affects performance.
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.
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.
Deep CleanerA Few Shot Image Dataset Cleaner Using Supervised Contrastive Learning

This project focuses on improving the quality of medical images before they are used for AI diagnosis. It automatically removes noisy or unwanted parts of images using a learning model trained on only a few clean examples. The system learns to separate correct images from incorrect ones. After cleaning, the accuracy of disease classification improves significantly.
Learning From Multiple Expert Annotators for Enhancing Anomaly Detection in Medical Image Analysis

This project focuses on improving computer programs that detect problems in medical images, like X-rays. Normally, these programs learn from labels given by doctors, but different doctors may disagree, which lowers accuracy. The researchers developed a method to combine labels from multiple doctors and figure out the most likely “true” label. This makes the program more reliable and accurate at spotting abnormalities in medical images.
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.
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.
CLUE-AI A Convolutional Three-Stream Anomaly Identification Framework for Robot Manipulation

This project focuses on making service robots safer when interacting with humans. It develops a system that helps robots detect and identify unexpected problems during tasks. The system combines what the robot sees, hears, and feels to recognize errors while handling objects. Tests on a Baxter robot show it can accurately detect these problems better than previous methods.
A Novel Mechanism for Misbehavior Detection in Vehicular Networks

This project focuses on making smart traffic systems safer. It studies networks where vehicles communicate with each other and identifies unusual behavior that could signal attacks. The system detects these threats, blocks harmful vehicles, and keeps a record of suspicious activity. Tests show it works accurately with very few mistakes.

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Anomaly Detection Final Year Projects with Source Code