论文标题
与物联网连续身份验证的热身和转移基于知识的联合学习方法
Warmup and Transfer Knowledge-Based Federated Learning Approach for IoT Continuous Authentication
论文作者
论文摘要
连续的行为身份验证方法通过允许个人在访问设备时验证其独特身份，从而增加了独特的安全层。现在，通过与移动或物联网（IoT）设备进行交互，使凭据盗窃和会话劫持无效时，维护会话真实性是可行的。通过整合人工智能和机器学习的力量（ML），使这种技术成为可能。大多数文献通过将其数据传输到外部服务器，但要受到私人用户数据暴露于威胁。在本文中，我们提出了一种新颖的联合学习方法（FL）方法，以保护用户数据的匿名性并保持其数据的安全性。我们提出了一种热身方法，可提供明显的准确性提高。此外，我们利用基于功能提取的转移学习技术来提高模型的性能。我们基于四个数据集的广泛实验：MNIST，女权主义者，CIFAR-10和UMDAA-02-FD，在维持用户隐私和数据安全性的同时，显示了用户身份验证准确性的显着提高。
Continuous behavioural authentication methods add a unique layer of security by allowing individuals to verify their unique identity when accessing a device. Maintaining session authenticity is now feasible by monitoring users' behaviour while interacting with a mobile or Internet of Things (IoT) device, making credential theft and session hijacking ineffective. Such a technique is made possible by integrating the power of artificial intelligence and Machine Learning (ML). Most of the literature focuses on training machine learning for the user by transmitting their data to an external server, subject to private user data exposure to threats. In this paper, we propose a novel Federated Learning (FL) approach that protects the anonymity of user data and maintains the security of his data. We present a warmup approach that provides a significant accuracy increase. In addition, we leverage the transfer learning technique based on feature extraction to boost the models' performance. Our extensive experiments based on four datasets: MNIST, FEMNIST, CIFAR-10 and UMDAA-02-FD, show a significant increase in user authentication accuracy while maintaining user privacy and data security.
