LogTAD: Unsupervised Cross-system Log Anomaly Detection via Domain Adaptation (CIKM 2021)

There are many studies done to detect anomalies based on logs. Current approaches are mainly divided into three categories: supervised learning methods, unsupervised learning methods, and deep learning methods. Many supervised learning methods are used for log-based anomaly detection.

Semi-supervised and unsupervised anomaly detection by mining numerical workflow relations from system logs (Accepted by Automated Software Engineering 2023)

Unsupervised machine learning for automatic anomaly detection on bro internet net traffic logs

Generative AI-driven Honeypot for cybersecurity. Simulates realistic server logs with AI and detects intrusions using unsupervised anomaly detection (Isolation Forest + NLP embeddings). Showcases synthetic data generation, feature engineering, and end-to-end ML workflow.

Deeplog and loganomaly from LogADEmpirical with my own analysis and modifications

Ladle – an unsupervised method for anomaly detection across log types

Web access logs are generated each time when we visit any website on internet. These contain various important information like IP address, data transferred, timestamp etc. Behaviour of clients/customers can be understood by these logs generated on server. In other words these provide us information about various processes/actions. In our study we are implementing Isolation forest and Local Outlier Factor algorithms for anomaly detection in CDNs.

Built an end-to-end insider threat detection system using unsupervised anomaly detection on enterprise activity logs. Engineered behavioral features, implemented Isolation Forest and One-Class SVM models, designed an explainable risk scoring engine, and developed a SOC-style dashboard for security analysts.

An unsupervised machine learning pipeline for real-time anomaly detection in HDFS logs. Features automated log parsing with Drain3, Isolation Forest classification, and dynamic threshold optimization for high-accuracy system monitoring.

Unsupervised Anomaly Detection System that identifies abnormal behavior in server log data by profiling User-IP activity over time and applying unsupervised machine learning models such as K-Means, Isolation Forest, and One-Class SVM to detect unusual access patterns without labeled data.

DADUA_RL is an advanced hybrid anomaly detection system that integrates Autoencoder-based unsupervised learning with a Reinforcement Learning (RL) agent. Its primary purpose is to detect anomalous Windows event logs in real-time.

TriGuard is an AI-powered anomaly detection system that ensures sensor data integrity using unsupervised ML (Autoencoder, Isolation Forest, LSTM) and blockchain for tamper-proof logging. Ideal for smart cities, it detects faults in real time, ensures transparency, and prevents data manipulation.

This project is a high-fidelity simulation of a real-time anomaly detection system for VPN logs. It uses an unsupervised machine learning model to identify suspicious activity and provides tools for model explainability and interactive feedback, all packaged within a user-friendly Streamlit web application.

Access log anomaly detection using unsupervised machine learning. Was in top 10.

End-to-end unsupervised anomaly detection pipeline for large-scale system logs using TF-IDF feature extraction and models such as Isolation Forest and PCA, validated on the HDFS dataset.

A system that detects unusual SQL strings in an unsupervised way

Unsupervised anomaly detection for microservice log data using Transformer embeddings, graph-based modeling, and ensemble scoring.

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Server Log Anomaly Detection using Unsupervised Clustering

ML Based Unsupervised Anomaly Detection in System Logs

Unsupervised Anomaly Detection AI for Windows Event Logs

Anomaly detection using unsupervised learning with BRO logs

Log anomaly detection system using unsupervised machine learning

Experimenting with different unsupervised log anomaly detection techniques

Paper: K4 – Online Log Anomaly Detection via Unsupervised Typicality Learning

CECO-LAD: Cloud-Edge Collaboration for Unsupervised Log Anomaly Detection

Unsupervised NLP-based log intelligence system for anomaly detection and incident discovery.

Unsupervised anomaly detection on HDFS logs using Isolation Forest with MLflow tracking

Anomaly Detection in the logs of wireless transmissions /IP Profiling through Unsupervised Learning.