Anomaly Detection on Dynamic (time-evolving) Graphs in Real-time and Streaming manner. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

WinDBG Anti-RootKit Extension

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

Deep Learning sample programs using PyTorch in C++

(Python, R, C/C++) Isolation Forest and variations such as SCiForest and EIF, with some additions (outlier detection + similarity + NA imputation)

Actively developed Hierarchical Temporal Memory (HTM) community fork (continuation) of NuPIC. Implementation for C++ and Python

Anomaly Detection on Time-Evolving Streams in Real-time. Detecting intrusions (DoS and DDoS attacks), frauds, fake rating anomalies.

Sketch-Based Anomaly Detection in Streaming Graphs

(Python, R, C++) Explainable outlier/anomaly detection through decision tree conditioning

Core streaming heterogeneous graph clustering and anomaly detection code (KDD 2016)

Anomaly Detection in Dynamic Graphs

Anomaly detection and monitoring software

Anomalous versions of OpenAI Gym and PyBullet3 environments

Neural Networks package for R with a fast C++ back-end and special support for unsupervised anomaly detection using autoencoders

Simple anomaly detection for univariate time series data.

KOMB is a tool for fast identification of unitigs of interest in metagenomes. KOMB introduces the concept of a Hybrid Unitig Graph (an extension to compacted de Bruijn graphs) and relies on k-core and K-truss decomposition algorithms.

Source code for IJCAI 2022 paper "HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams."

Isconna: Streaming Anomaly Detection with Frequency and Patterns

This repository shows our's implementation of Milestone 2 in the semesterial project in Advanced Programming 2 course. Computer Science, Bar-Ilan University.

A framework for time-series anomaly detection. The framework consists a prediction module and a detection module. Prediction module is based on LSTM and CNN. DTW is used in detection module .