(MLSys' 21) An Acceleration System for Large-scare Unsupervised Heterogeneous Outlier Detection (Anomaly Detection)

PyTorch implementation of Deeplog: Anomaly detection and diagnosis from system logs through deep learning

LogLLM: Log-based Anomaly Detection Using Large Language Models (system log anomaly detection)

Federated learning-based fraud detection system enabling secure, privacy-preserving model training across financial institutions without sharing raw data. Supports real-time and batch processing, anomaly detection, and modular integration with external APIs.

PyTorch implements "DeepLog: Anomaly Detection and Diagnosis from System Logs through Deep Learning"

ThirdEye is an integrated tool for realtime monitoring of time series and interactive root-cause analysis. It enables anyone inside an organization to collaborate on effective identification and analysis of deviations in business and system metrics. ThirdEye supports the entire workflow from anomaly detection, over root-cause analysis, to issue resolution and post-mortem reporting.

System for network traffic analysis and anomaly detection.

:star: An anomaly-based intrusion detection system.

Real-time Intrusion Detection System implementing Machine Learning. We combine Supervised Learning (RF) for detecting known attacks from CICIDS 2018 & SCVIC-APT datasets, and Unsupervised Learning (AE) for anomaly detection.

A self-tuning anomaly detection system to address the challenges of method selection and hyper-parameter tuning without access to a sufficient number of human supplied, ground truth labels.

Unified PHM framework for Remaining Useful Life (RUL) prediction, fault diagnosis, fault detection, and anomaly detection for bearings, turbofan engines, and other industrial systems.

Machine learning based Intrusion detection system (IDS)

Welcome this is a comprehensive repository dedicated to advancing Network Intrusion Detection Systems (NIDS) through the power of Machine Learning (ML) and Deep Learning (DL). This project aims to develop, evaluate, and optimize intelligent models capable of accurately detecting and mitigating a wide array of network threats and anomalies.

Real-time thermal fault detection system using an AMG8831 IR sensor and ESP32. The system monitors temperature patterns, detects hotspots and anomalies, sends alert notifications, and visualizes live thermal heatmaps through a web dashboard for early fault detection and preventive maintenance.

Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.

Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).Coronavirus disease 2019 (COVID-19) is an infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). It was first identified in December 2019 in Wuhan, China, and has since spread globally, resulting in an ongoing pandemic. Long Short Term Memories(LSTMs) can solve numerous tasks not solvable by previous learning algorithms for recurrent neural networks (RNNs). LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition and anomaly detection in network traffic or IDS's (intrusion detection systems). LSTMs can also be efficiently applied for time-series predictions. In this project, its shows a four stacked LSTM network for early prediction new Coronavirus dissease infections in some of the mentioned affected countries (India, USA, Czech Republic and Russia) , which is based on real world data sets which are analyzed using various perspectives like day-wise number of confirmed cases, number of Cured cases, death cases. This attempt has been done to help the concerned authorities to get some early insights into the probable devastation likely to be effected by the deadly pandemic.

This is an official implementation of “A Masked Reverse Knowledge Distillation Method Incorporating Global and Local Information for Image Anomaly Detection” (MRKD) with PyTorch, accepted by knowledge-based systems.

AI-powered Log Intelligence System - Semantic search, anomaly detection, and root cause analysis for logs using RAG techniques. Built with Rust, Axum, NATS, ClickHouse, and Qdrant.

Andrew Ng's Machine Learning Class Projects Description: Ex1 - Gradient Descent, Newton's Method, Linear Regression Ex2 - Sigmoid Kernels Ex3 - Logistic Regression Implementation Ex4 - Neural Networks implementation for Digit Recognition Ex5 - Regularized Linear Regression, Polynomial Regression Ex6 - SVM (Kernel implementation) for Spam Classification Ex8 - Recommender System (Collaborative Filtering) and Anomaly Detection

“Anomaly Detection with Bootstrapping” is a software system that applies a bootstrapped statistical workflow to pinpoint and quantify data abnormalities—such as measurement errors, rounding artifacts, mis-recorded values, mislabeled records, and out-of-distribution samples—within large-scale datasets.

Code for KDD' 21 paper: Time Series Anomaly Detection for Cyber-physical Systems via Neural System Identification and Bayesian Filtering

Real-Time-Leak-Detection-in-Infrastructure uses sensors, machine learning, and data analytics to continuously monitor pipelines and water systems. It detects anomalies such as sudden pressure drops or unusual flow patterns, enabling early leak identification

No description available

ECG Anomaly Detection System using HTM

Anomaly based Instrusion Detection System using RNN-LSTMs. Datasets include NSL-KDD and UNSW-NB15.

A condition monitoring system for gas turbine, including refenrece value, anomaly detection, and fault diagnosis.

The Kubervisor allow you to control which pods should receive traffic or not based on anomaly detection.It is a new kind of health check system.

DeepLog-Anomaly Detection and Diagnosis from System Logs through Deep Learning

A Spatio-Temporal Deep Learning Approach for Unsupervised Anomaly Detection in Cloud Systems (TNNLS)

# Machine Learning (Coursera) This is my solution to all the programming assignments and quizzes of Machine-Learning (Coursera) taught by Andrew Ng. After completing this course you will get a broad idea of Machine learning algorithms. Try to solve all the assignments by yourself first, but if you get stuck somewhere then feel free to browse the code. ## Contents * Lectures Slides * Solution to programming assignment * Solution to Quizzes by Andrew Ng, Stanford University, [Coursera](https://www.coursera.org/learn/machine-learning/home/welcome) ### Week 1 - [X] Videos: Introduction - [X] Quiz: Introduction - [X] Videos: Linear Regression with One Variable - [X] Quiz: Linear Regression with One Variable ### Week 2 - [X] Videos: Linear Regression with Multiple Variables - [X] Quiz: Linear Regression with Multiple Variables - [X] Videos: Octave/Matlab Tutorial - [X] Quiz: Octave/Matlab Tutorial - [X] Programming Assignment: Linear Regression ### Week 3 - [X] Videos: Logistic Regression - [X] Quiz: Logistic Regression - [X] Videos: Regularization - [X] Quiz: Regularization - [X] Programming Assignment: Logistic Regression ### Week 4 - [X] Videos: Neural Networks: Representation - [X] Quiz: Neural Networks: Representation - [X] Programming Assignment: Multi-class Classification and Neural Networks ### Week 5 - [X] Videos: Neural Networks: Learning - [X] Quiz: Neural Networks: Learning - [X] Programming Assignment: Neural Network Learning ### Week 6 - [X] Videos: Advice for Applying Machine Learning - [X] Quiz: Advice for Applying Machine Learning - [X] Videos: Programming Assignment: Regularized Linear Regression and Bias/Variance - [X] Machine Learning System Design - [X] Quiz: Machine Learning System Design ### Week 7 - [X] Videos: Support Vector Machines - [X] Quiz: Support Vector Machines - [X] Programming Assignment: Support Vector Machines ### Week 8 - [X] Videos: Unsupervised Learning - [X] Quiz: Unsupervised Learning - [X] Videos: Dimensionality Reduction - [X] Quiz: Principal Component Analysis - [X] Programming Assignment: K-Means Clustering and PCA ### Week 9 - [X] Videos: Anomaly Detection - [X] Quiz: Anomaly Detection - [X] Videos: Recommender Systems - [X] Quiz: Recommender Systems - [X] Programming Assignment: Anomaly Detection and Recommender Systems ### Week 10 - [X] Videos: Large Scale Machine Learning - [X] Quiz: Large Scale Machine Learning ### Week 11 - [X] Videos: Application Example: Photo OCR - [X] Quiz: Application: Photo OCR ## Certificate * [Verified Certificate]() ## References [[1] Machine Learning - Stanford University](https://www.coursera.org/learn/machine-learning)