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An efficient trustworthy cyberattack defence mechanism system for self guided federated learning framework using
Louai A Maghrabi1, Alanoud Subahi2, Nouf Atiahallah Alghanmi2
1Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia.
Scientific Reports
|May 15, 2025
Summary
Federated learning (FL) enhances cybersecurity by enabling collaborative model training on local data, protecting privacy. The novel CDMFL-AIDCNN technique achieves 99.07% accuracy in cyberattack detection.
Area of Science:
- Cybersecurity
- Machine Learning
- Decentralized Systems
Background:
- Conventional centralized threat intelligence models struggle with advanced cyberattacks.
- Federated learning (FL) offers a privacy-preserving, decentralized approach to cybersecurity.
- Machine learning (ML) and Deep Learning (DL) advancements are crucial for robust cyberattack defense.
Purpose of the Study:
- To present a novel Cyberattack Defence Mechanism System for Federated Learning Framework (CDMFL-AIDCNN).
- To enhance cybersecurity resilience and privacy in distributed systems using FL and attention-based deep learning.
- To improve cyberattack detection accuracy and efficiency.
Main Methods:
- Utilized Z-score normalization for data preprocessing.
- Employed Dung Beetle Optimization (DBO) for effective feature selection.
- Developed an Attention Induced Deep Convolution Neural Networks (AIDCNN) model, integrating convolutional neural networks, bidirectional long short-term memory, gated recurrent units, and attention (CBLG-A), optimized by the Growth Optimizer (GO).
Main Results:
- The CDMFL-AIDCNN technique demonstrated superior performance in cyberattack detection.
- Achieved high accuracy rates of 99.07% on the CIC-IDS-2017 dataset and 98.64% on the UNSW-NB15 dataset.
- Validated the effectiveness of FL and deep learning fusion for enhanced cybersecurity.
Conclusions:
- The proposed CDMFL-AIDCNN system significantly improves cyberattack defence mechanisms in federated learning frameworks.
- The integration of FL with advanced deep learning models offers a robust and privacy-preserving solution for modern cybersecurity challenges.
- This approach paves the way for more adaptive and resilient cybersecurity practices in distributed environments.
