https://github.com/machinelearningzuu/network-traffic-classification
Deep Learning model for analysis and identify the application for given Teletraffic pattern. 1D convolution and FFN models using for this task
https://github.com/machinelearningzuu/network-traffic-classification
Last synced: 8 months ago
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Deep Learning model for analysis and identify the application for given Teletraffic pattern. 1D convolution and FFN models using for this task
- Host: GitHub
- URL: https://github.com/machinelearningzuu/network-traffic-classification
- Owner: machinelearningzuu
- Created: 2020-04-19T13:00:12.000Z (about 6 years ago)
- Default Branch: master
- Last Pushed: 2021-05-30T05:55:48.000Z (about 5 years ago)
- Last Synced: 2025-04-12T22:05:54.937Z (about 1 year ago)
- Language: Python
- Homepage:
- Size: 805 KB
- Stars: 18
- Watchers: 0
- Forks: 7
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Network Traffic Classification and Anomaly Detection
Deep Learning model for analysis and identify the application for given Teletraffic pattern. Experiment with different models including MLPs and CNNs. Final object
is to detect anomaly apps with unusual traffic patterns.
- Analyze network traffic for both incoming and outgoing
- Extract statistical features
- Train the supervised deep learning model
- Handle anomalies using softmax probabilities
# Techniques
- Supervised Deep Learning, Unsupervised Deep Learning
- Statistical feature calculation
# Models
- 1D-CNN with Statistical features
- ANN with Statistical features
- 2D-CNN with Spatial features
# Tools
* TensorFlow - Deep Learning Model
* pandas - Data Extraction and Preprocessing
* numpy - numerical computations
* scikit learn - Advanced preprocessing
### Installation
Install the dependencies and conda environment
```sh
$ conda create -n envname python=python_version
$ activate envname
$ conda install -c anaconda tensorflow-gpu
$ conda install -c anaconda pandas
$ conda install -c anaconda matplotlib
$ conda install -c anaconda scikit-learn
```
For Train Model...
```sh
$ python model.py
```