Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/nouralmulhem/anomaly-detection
🚀 This Project simulates real-time sequences of floating-point numbers, that could represent various metrics such as financial transactions or system metrics.
https://github.com/nouralmulhem/anomaly-detection
anomaly-detection deep-learning machine-learning
Last synced: 10 days ago
JSON representation
🚀 This Project simulates real-time sequences of floating-point numbers, that could represent various metrics such as financial transactions or system metrics.
- Host: GitHub
- URL: https://github.com/nouralmulhem/anomaly-detection
- Owner: nouralmulhem
- License: mit
- Created: 2024-07-06T23:32:10.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-07-10T22:29:08.000Z (6 months ago)
- Last Synced: 2024-12-30T00:12:06.341Z (22 days ago)
- Topics: anomaly-detection, deep-learning, machine-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 620 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Anomaly-Detection
## 
Overview
- Built using Python.
- This is a solution for Anomaly Detection Problem
- This Project simulates real-time sequences of floating-point numbers, that could represent various metrics such as financial transactions or system metrics.
## 
How To Run
- First install the needed packages.
```sh
pip install -r requirements.txt
```
- Folder Structure
```sh
├─── images
├─── src
│ ├── model
│ └─── autoencoder_model.h5
│ ├── RNN
│ ├── RNN.ipynb
│ └─── utile.py
│ ├── AnomalyDetection.py
│ ├── Hotelling.py
│ └─── Streaming.py
....
```
## Anomaly Detection Script
This script allows you to run different anomaly detection algorithms on your data.
### Available Algorithms
- `hotelling`: Hotelling's T-squared algorithm for multivariate anomaly detection.
- `ocsvm`: One-Class Support Vector Machine for anomaly detection.
- `variance`: Variance-based anomaly detection.
- `isolated_forest`: Isolation Forest algorithm for anomaly detection.
### Usage
To run the script, use the following command:
```sh
python path/to/AnomalyDetection.py --algo [--plot]
```
- `--algo `: Choose the algorithm to run. Options are `hotelling`, `ocsvm`, `variance`, and `isolated_forest`.
- `--plot`: (Optional) Set this flag to plot the results.
## Streaming Script
This script allows you to run and stream the data and detect anomalies on the fly from generated data
### Usage
To run the script, use the following command:
```sh
python path/to/Streaming.py
```
OR
```sh
python path/to/StreamingV2.py
```
## 
References
| Name | Link |
|-----------------|--------------------------------------------------------------------------|
| ` Adversarially Learned Anomaly Detection` | [Link](https://arxiv.org/abs/1812.02288) |
| `How to use Python for anomaly detection in data: Detailed Steps` | [Link](https://dataheadhunters.com/academy/how-to-use-python-for-anomaly-detection-in-data-detailed-steps/) |
| `Anomaly Detection` | [Link](https://avinetworks.com/glossary/anomaly-detection/#:~:text=Anomaly%20detection%20is%20the%20identification,noise%2C%20novelties%2C%20and%20exceptions.) |
## 
Contributors
## 🔒 License
> **Note**: This software is licensed under MIT License, See [License](https://github.com/nouralmulhem/Cipher-Sphere/blob/main/LICENSE) for more information ©nouralmulhem.