- Overview: This project is an AI-based Intrusion Detection System that uses an LSTM (Long Short-Term Memory) neural network to detect malicious network traffic.
This project was developed as part of a research study focused on improving intrusion detection systems using deep learning techniques. Traditional IDS models often suffer from high false positive rates and struggle to detect modern attack patterns.
To address this, an LSTM-based deep learning model was designed to analyze sequential network traffic data and identify anomalies more effectively. The model was trained and evaluated on the NSL-KDD dataset using metrics such as accuracy, precision, recall, and F1-score.
The results demonstrated improved detection performance and reduced false positives compared to traditional machine learning approaches.
This project is based on an accepted research paper:
Paper Title: AI Driven Intrusion Detection Systems Using Deep Learning
Authors: Anmol Sarin, Aayushi Mohan, Adarsh Rai, Dr. Neha Ahlawat
Journal: International Journal of All Research Education & Scientific Methods (IJARESM)
ISSN: 2455-6211 | Impact Factor: 9.175
Volume: 14, Issue 4, April 2026
Status: ✅ Accepted
- Features:
- Detects normal vs attack traffic
- Uses deep learning (LSTM)
- Trained on NSL-KDD dataset
- Visualizes accuracy and loss
- Technologies used:
- TensorFlow / Keras
- NumPy
- Pandas
- Matplotlib
- Streamlit
- Project structure:
- AI_Intrusion_Detection_System.ipynb → Model training
- app.py → Streamlit web app
- ids_model.keras → Saved model
- IDS_requirements.pdf → Installation guide
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How to run: Install dependencies- pip install tensorflow numpy pandas matplotlib streamlit
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This project can be run locally using Streamlit: streamlit run app.py
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Output: The model predicts whether the input network traffic is normal or an attack.
- Implemented LSTM for sequential network data
- Reduced false positives compared to traditional IDS
- Trained on NSL-KDD dataset
- Built interactive UI using Streamlit for real-time testing