Project Background:
The smart intrusion detection systems using federated learning stem from the pressing need to bolster cybersecurity defenses against evolving threats while respecting data privacy and confidentiality. Traditional intrusion detection systems (IDS) often rely on centralized data repositories, posing significant vulnerabilities and privacy concerns. Federated learning offers a compelling alternative by enabling collaborative model training across distributed network environments without sharing raw data. In the context of intrusion detection, this approach allows individual devices or networks to contribute their local knowledge while preserving sensitive information.
By leveraging federated learning techniques, smart intrusion detection systems can continuously adapt and evolve to detect emerging threats more effectively. Furthermore, federated learning facilitates the integration of diverse data sources, including network traffic logs, system logs, and endpoint telemetry, enhancing the robustness and accuracy of intrusion detection models. This aims to harness the power of federated learning to develop intelligent IDS capable of detecting and mitigating cyber threats in real time, thereby bolstering cybersecurity defenses while safeguarding data privacy and security in distributed environments.
Problem Statement
- Centralized intrusion detection systems (IDS) pose data privacy and security risks due to the need for sharing sensitive information across networks.
- Traditional IDS often struggle to adapt to evolving cyber threats and may lack scalability across distributed network environments.
- Balancing the trade-off between effective threat detection and preserving data privacy remains a challenge.
- Offers a potential solution by enabling collaborative model training across decentralized data sources without sharing raw data.
- Introduces complexities in model aggregation, communication overhead, and privacy preservation.
- Ensuring interoperability and scalability of federated learning-based IDS across diverse network environments requires further investigation.
Aim and Objectives
- Develop smart intrusion detection systems using federated learning to enhance cybersecurity while preserving data privacy.
- Design federated learning techniques for collaborative threat detection across decentralized network environments.
- Ensure data privacy and confidentiality in intrusion detection systems leveraging federated learning.
- Improve detection accuracy and scalability of intrusion detection models through federated learning approaches.
- Minimize communication overhead and computational complexity in federated learning protocols for IDS.
- Facilitate interoperability and adaptability of federated learning-based IDS across diverse network infrastructures.
Contributions to Smart Intrusion Detection Systems using Federated Learning
- Enhancing threat detection capabilities while preserving data privacy and confidentiality through federated learning.
- Improving the accuracy and scalability of intrusion detection systems by leveraging insights from decentralized data sources.
- Mitigating risks associated with data sharing and centralized model training in traditional IDS approaches.
- Facilitating collaboration and knowledge sharing among network entities without compromising individual data security.
- Strengthening resilience against evolving cyber threats through collective learning and adaptive defense mechanisms.
Deep Learning Algorithms for Smart Intrusion Detection Systems using Federated Learning
- Federated Long Short-Term Memory (LSTM)
-
Federated Convolutional Neural Networks (CNNs)
-
Federated Recurrent Neural Networks (RNNs)
-
Federated Autoencoders
-
Federated Variational Autoencoders (VAEs)
-
Federated Generative Adversarial Networks (GANs)
-
Federated Capsule Networks (CapsNets)
-
Federated Attention Mechanisms
-
Federated Transformer-based Models
-
Federated Graph Neural Networks (GNNs)
Datasets for Smart Intrusion Detection Systems using Federated Learning
- NSL-KDD Dataset
-
UNSW-NB15 Dataset
-
CICIDS 2017 Dataset
-
KDD Cup 1999 Dataset
-
DARPA Intrusion Detection Evaluation Dataset
-
ISCX-IDS 2012 Dataset
-
UGR 16 Dataset
-
Kyoto 2006+ Dataset
-
UGR 17 Dataset
-
CSE-CIC-IDS2018 Dataset
Software Tools and Technologies:
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
