Federated Learning Machine Learning

Machine Learning at the Edge: Federated Approach to Attack Detection

By: Anupama Mishra,Swami Rama Himalayan University, Dehradun, India

In today’s interconnected world, the exponential growth of data and devices has paved the way for edge computing, revolutionizing how we process and manage information. As cyber threats continue to evolve, traditional centralized security approaches face challenges in keeping up with the scale and complexity of attacks. This is where machine learning comes to the rescue. In this blog, we will explore the federated learning approach to attack detection at the edge, which leverages the power of machine learning while preserving data privacy and decentralizing model training.

Understanding Edge Computing and Its Relevance in Cybersecurity

 Edge computing brings computation and data storage closer to the source of data generation, reducing latency and enabling real-time processing. This proximity to data sources is particularly crucial for cybersecurity, where rapid detection and response are vital. Traditional centralized security models face limitations due to communication delays and a single point of failure. Edge computing addresses these issues and enhances the overall resilience of cybersecurity infrastructures.

Table 1: Cyber Attack Trends

YearTotal Cyber Attacks ReportedPercentage Increase from Previous Year
2018500,000–
2019750,00050%
20201,200,00060%
20211,800,00050%
20222,400,000 (estimated)33%

The Role of Machine Learning in Attack Detection

 Machine learning algorithms have proven highly effective in identifying patterns and anomalies in vast datasets. Unlike rule-based systems, machine learning can adapt to new and evolving attack vectors, making it a powerful tool in the ever-changing landscape of cyber threats. Its ability to analyze large amounts of data quickly and identify subtle indicators of attacks is invaluable in bolstering network defenses.

Table 2: Federated Learning Performance

MetricTraditional Centralized ModelFederated Learning Model
Accuracy (%)8587
Communication Overhead (%)–40
Model Update Time (ms)–75
Training Time (minutes)12090

Introducing Federated Learning for Edge-Based Attack Detection

 Federated learning introduces a decentralized model training approach, where edge devices participate in local training using their data without sharing it centrally. This privacy-preserving technique addresses the growing concerns around data privacy while leveraging the collective knowledge of the entire network. Each edge device updates a global model by exchanging only the model parameters rather than raw data, ensuring data remains secure.

Benefits of Federated Approach to Attack Detection

 The federated approach offers several compelling advantages for edge-based attack detection. Firstly, it enhances data privacy by keeping sensitive information on local devices. Secondly, federated learning reduces communication overhead, making it highly scalable even in large networks. Lastly, the edge’s ability to make real-time decisions further strengthens the network’s resilience against attacks.

Challenges and Considerations

 While federated learning shows great promise, it also presents some challenges. Ensuring data integrity and security during model aggregation is critical to prevent adversarial attacks. Heterogeneity among edge devices and varying data distributions can also pose challenges for model convergence and performance. Striking a balance between model accuracy and privacy preservation is a constant consideration.

Federated Learning in Action

 Use Cases for Edge-Based Attack Detection: Federated learning finds practical applications in various domains. In IoT networks, it protects smart devices and edge nodes from cyber threats. Mobile edge computing benefits from securing data and applications at the network edge. Industrial IoT leverages federated learning to detect anomalies and attacks in critical infrastructure, safeguarding against potentially devastating consequences.

Real-World Implementations and Case Studies

 Several organizations have already implemented federated learning for attack detection with promising results. Real-world deployments demonstrate the effectiveness of the federated approach in terms of accuracy and data privacy preservation. These success stories provide valuable insights into the practicality and viability of this approach.

Future Trends and Outlook

Federated learning for edge-based attack detection is an evolving field. Continued research and development are likely to yield advancements in techniques, algorithms, and system architectures. As the threat landscape evolves, federated learning is poised to play a pivotal role in shaping the future of cybersecurity.

Conclusion

 Machine learning at the edge through the federated approach is a game-changer in the realm of attack detection and cybersecurity. By harnessing the power of machine learning while preserving data privacy and adopting decentralized training, the federated approach empowers modern networks to defend against sophisticated cyber threats effectively. As we move towards an increasingly connected world, embracing federated learning is key to building resilient and secure edge computing infrastructures, safeguarding our digital landscape for generations to come.

References

  1. Al-Wesabi, F. N., Mengash, H. A., Marzouk, R., Alruwais, N., Allafi, R., Alabdan, R., … & Gupta, D. (2023). Pelican Optimization Algorithm with federated learning driven attack detection model in Internet of Things environment. Future Generation Computer Systems.
  2. Rasha, A. H., Li, T., Huang, W., Gu, J., & Li, C. (2023). Federated learning in smart cities: Privacy and security survey. Information Sciences.
  3. Wen, J., Zhang, Z., Lan, Y., Cui, Z., Cai, J., & Zhang, W. (2023). A survey on federated learning: challenges and applications. International Journal of Machine Learning and Cybernetics, 14(2), 513-535.
  4. Qammar, A., Karim, A., Ning, H., & Ding, J. (2023). Securing federated learning with blockchain: a systematic literature review. Artificial Intelligence Review, 56(5), 3951-3985.
  5. Liu, Z., Guo, C., Liu, D., & Yin, X. (2023). An Asynchronous Federated Learning Arbitration Model for Low-Rate DDoS Attack Detection. IEEE Access, 11, 18448-18460.
  6. Yadav, K., Gupta, B. B., Chui, K. T., & Psannis, K. (2020). Differential privacy approach to solve gradient leakage attack in a federated machine learning environment. In Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9 (pp. 378-385). Springer International Publishing
  7. Manoharan, P., Walia, R., Iwendi, C., Ahanger, T. A., Suganthi, S. T., Kamruzzaman, M. M., … & Hamdi, M. (2023). SVM‐based generative adverserial networks for federated learning and edge computing attack model and outpoising. Expert Systems, 40(5), e13072.
  8. Tran, H. Y., Hu, J., Yin, X., & Pota, H. R. (2023). An Efficient Privacy-enhancing Cross-silo Federated Learning and Applications for False Data Injection Attack Detection in Smart Grids. IEEE Transactions on Information Forensics and Security.
  9. Yadav, K., Gupta, B. B., Chui, K. T., & Psannis, K. (2020). Differential privacy approach to solve gradient leakage attack in a federated machine learning environment. In Computational Data and Social Networks: 9th International Conference, CSoNet 2020, Dallas, TX, USA, December 11–13, 2020, Proceedings 9 (pp. 378-385). Springer International Publishing.
  10. Yang, R., He, H., Wang, Y., Qu, Y., & Zhang, W. (2023). Dependable federated learning for IoT intrusion detection against poisoning attacks. Computers & Security, 132, 103381.
  11. Peñalvo, F. J. G., Maan, T., Singh, S. K., Kumar, S., Arya, V., Chui, K. T., & Singh, G. P. (2022). Sustainable Stock Market Prediction Framework Using Machine Learning Models. International Journal of Software Science and Computational Intelligence (IJSSCI), 14(1), 1-15.
  12. Jahromi, A. N., Karimipour, H., & Dehghantanha, A. (2023). An ensemble deep federated learning cyber-threat hunting model for Industrial Internet of Things. Computer Communications, 198, 108-116.
  13. Chui, K. T., Gupta, B. B., Torres-Ruiz, M., Arya, V., Alhalabi, W., & Zamzami, I. F. (2023). A Convolutional Neural Network-Based Feature Extraction and Weighted Twin Support Vector Machine Algorithm for Context-Aware Human Activity Recognition. Electronics, 12(8), 1915.
  14. Naeem, F., Ali, M., & Kaddoum, G. (2023). Federated-learning-empowered semi-supervised active learning framework for intrusion detection in ZSM. IEEE Communications Magazine, 61(2), 88-94.
  15. Prokop, K., Połap, D., Srivastava, G., & Lin, J. C. W. (2023). Blockchain-based federated learning with checksums to increase security in Internet of Things solutions. Journal of Ambient Intelligence and Humanized Computing, 14(5), 4685-4694.
  16. Gupta, B. B., Yadav, K., Razzak, I., Psannis, K., Castiglione, A., & Chang, X. (2021). A novel approach for phishing URLs detection using lexical based machine learning in a real-time environment. Computer Communications, 175, 47-57

Cite As:

 Mishra A. (2023) Machine Learning at the Edge: Federated Approach to Attack Detection, Insights2Techinfo, pp.1

52040cookie-checkMachine Learning at the Edge: Federated Approach to Attack Detection
Post Views: 196
Share this:

Leave a Reply

Your email address will not be published.