https://github.com/nsswifter/cyberattackdetection
This project focuses on detecting cyber attacks using advanced analytical techniques and a deep learning model. It leverages structured datasets and explores the use of algorithms for detecting anomalies or malicious behavior in network traffic or system logs.
https://github.com/nsswifter/cyberattackdetection
cyber-attack-detection cybersecurity intrusion-detection long-short-term-memory lstm recurrent-neural-networks rnn
Last synced: 8 months ago
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This project focuses on detecting cyber attacks using advanced analytical techniques and a deep learning model. It leverages structured datasets and explores the use of algorithms for detecting anomalies or malicious behavior in network traffic or system logs.
- Host: GitHub
- URL: https://github.com/nsswifter/cyberattackdetection
- Owner: nsswifter
- License: mit
- Created: 2024-11-22T20:20:21.000Z (11 months ago)
- Default Branch: main
- Last Pushed: 2024-11-23T17:57:39.000Z (11 months ago)
- Last Synced: 2024-11-23T18:27:42.339Z (11 months ago)
- Topics: cyber-attack-detection, cybersecurity, intrusion-detection, long-short-term-memory, lstm, recurrent-neural-networks, rnn
- Language: Jupyter Notebook
- Homepage:
- Size: 1000 Bytes
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 🌐 Cyberattack Detection and Anomalous Behavior Analysis Using Recurrent Neural Networks (RNN)
Cybersecurity is a critical domain as network attacks increasingly threaten systems worldwide. Detecting cyberattacks and analyzing anomalous behaviors in network traffic is essential for securing digital environments. This project leverages **Recurrent Neural Network (RNN)**, technically **Long Short-Term Memory (LSTM)**, to identify abnormal patterns in network traffic that signal potential cyberattacks, such as DDoS, port scanning, and brute-force attacks.
## Dataset
The project utilizes the **[CIC-IDS2017 Dataset](https://www.unb.ca/cic/datasets/ids-2017.html)**, developed by the [Canadian Institute for Cybersecurity](https://www.unb.ca). It is widely used for intrusion detection system evaluation and contains real-world attack simulations, including:
- **DDoS attacks**
- **Brute force attacks**
- **SQL injection**
- **Port scanning**
- **Botnet activities**
The dataset is suitable for RNN-based methods due to its sequential nature, allowing temporal dependencies in network traffic to be analyzed effectively.
For easier access, we used the [Network Intrusion Dataset on Kaggle](https://www.kaggle.com/datasets/chethuhn/network-intrusion-dataset).
## How to Run
1. To run this project, clone the repository and navigate to the project directory:
```bash
git clone https://github.com/nsswifter/CyberattackDetection.git
cd CyberattackDetection
```
2. Ensure you have `Python 3.11.1+` and the required libraries installed. Use the command below to install dependencies:
```bash
pip install -r requirements.txt
```
3. Open the Jupyter Notebook:
```bash
jupyter notebook cyberattack_detection.ipynb
```
4. Execute the notebook cells sequentially to preprocess data, train the model, and evaluate results.
## Results
The RNN model effectively detects cyberattacks with competitive accuracy, leveraging temporal patterns in the data. Detailed evaluation metrics and visualizations are included in the notebook.
## License
This project is licensed under the [MIT License](LICENSE).