Federated Learning Approach to Safeguard User Privacy
Abstract
Current intrusion detection models based on machine learning require reliable datasets, but the public dataset updates are typically delayed after new attacks, which slows down the model's update speed. Also, to train the existing model, the data needs to be shared; hence, it lacks data integrity. To address this issue, this project implements a never-ending learning (NEL) framework for intrusion detection that utilizes multi-task and transfer learning to continuously acquire knowledge from private datasets, regardless of sharing them publicly. The NEL framework also integrates serendipitous learning, which updates the model by identifying and classifying new attack categories from the suspected traffic of attacked devices. The project also enhances various continuous learning training methods with federated learning to safeguard user privacy, ensuring that user data is not transmitted directly.
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