Leveraging generative adversarial networks and federated learning for enhanced cybersecurity: a concise review
Mohamed Adel Hammad, Basma Abd El-Rahiem, Ahmed A. Abd El‐Latif · Institution of Engineering and Technology eBooks · 2023
The rise of cyber threats in recent years has made cybersecurity a critical concern for individuals, organizations, and governments worldwide. Machine learning has proven to be a powerful tool in security mechanisms, providing more effective and efficient detection and response capabilities to cyber threats. Deep learning, a subset of machine learning, has also shown promising results in various fields, including cybersecurity. This chapter explores the use of advanced technologies such as generative adversarial networks (GANs) and federated learning (FL) in cybersecurity to provide more effective and efficient detection and response capabilities to cyber threats while preserving data privacy. GANs can be used to generate synthetic data for training machine learning models and simulate cyber-attacks for training and testing cybersecurity defenses. FL enables devices or parties to collaborate and train a machine learning model without sharing their data with a central server, thereby mitigating the risks of data breaches and misuse while also enhancing model accuracy. The goal of using GANs and FL in cybersecurity is to develop novel approaches that leverage the power of artificial intelligence and machine learning to improve threat detection and response while also addressing the growing concerns around data privacy and security, ultimately contributing to a safer and more secure digital world.