Project Background:
Cybersecurity using federated learning revolves around addressing the growing challenges of protecting sensitive data and mitigating security threats in distributed environments. Traditional cybersecurity approaches often involve centralizing data for analysis and model training, which poses significant data privacy and security risks. Federated learning offers a promising alternative by allowing machine learning models to be trained collaboratively across multiple decentralized data sources without sharing raw data. In cybersecurity, federated learning enables organizations to leverage insights from their local data while preserving data privacy and confidentiality. This approach is particularly relevant in finance, healthcare, and government sectors, where data sovereignty and regulatory compliance are paramount. It aims to harness federated learning techniques to develop robust cybersecurity solutions, including intrusion detection systems, malware detection, and threat intelligence while protecting sensitive information. By enabling organizations to collectively learn from their distributed data without compromising individual privacy, it can revolutionize cybersecurity practices and enhance resilience against evolving cyber threats.
Problem Statement
- Centralized data storage and analysis in cybersecurity pose risks to data privacy and security.
-
Traditional cybersecurity approaches often require sharing sensitive data across networks, increasing vulnerability to attacks.
-
Protecting sensitive information while enabling effective threat detection and mitigation is a critical challenge.
- Federated learning offers a solution by allowing collaborative model training across decentralized data sources without sharing raw data.
-
Balancing the trade-off between model performance and privacy preservation in federated learning-based cybersecurity solutions is crucial.
Aim and Objectives
- Enhance cybersecurity using federated learning while preserving data privacy and security.
- Develop federated learning techniques for collaborative threat detection and mitigation.
-
Ensure data privacy and confidentiality in federated learning-based cybersecurity solutions.
- Improve model accuracy and effectiveness in detecting and responding to cyber threats.
-
Minimize communication overhead and computational complexity in federated learning protocols.
- Facilitate interoperability and scalability of federated learning frameworks across diverse cybersecurity environments.
- Foster collaboration and knowledge sharing among organizations while respecting data sovereignty and regulatory compliance.
Contributions to Cyber Security using Federated Learning
- Enhancing threat detection capabilities while preserving data privacy and confidentiality through federated learning.
- Improving the accuracy and effectiveness of cybersecurity solutions by leveraging insights from decentralized data sources.
- Mitigating risks associated with data sharing and centralized model training in traditional cybersecurity approaches.
- Facilitating collaboration and knowledge sharing among organizations without compromising individual data security.
- Strengthening resilience against evolving cyber threats through collective learning and adaptive defense mechanisms.
- Advancing the state-of-the-art in federated learning techniques for cybersecurity applications.
Deep Learning Algorithms for Cyber Security using Federated Learning
- Federated Learning with Differential Privacy (FLDP)
-
Secure Aggregation for Federated Learning
-
Federated Learning with Homomorphic Encryption
-
Federated Meta-Learning
-
Federated Reinforcement Learning
-
Federated Generative Adversarial Networks (GANs)
-
Federated Variational Autoencoders (VAEs)
-
Federated Capsule Networks (CapsNets)
-
Federated Attention Mechanisms
-
Federated Long Short-Term Memory (LSTM) Networks
Datasets for Cyber Security using Federated Learning
- ADFA-Intrusion Detection Evaluation Dataset
-
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
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
