Federated Learning (FL) has recently become an effective approach for cyberattack detection systems, especially in Internet-of-Things (IoT) networks. By distributing the learning process across IoT gateways, FL can improve learning efficiency, reduce communication overheads and enhance privacy for cyberattack detection systems. Challenges in implementation of FL in such systems include unavailability of labeled data and dissimilarity of data features in different IoT networks. In this paper, we propose a novel collaborative learning framework that leverages Transfer Learning (TL) to overcome these challenges. Particularly, we develop a novel collaborative learning approach that enables a target network with unlabeled data to effectively and quickly learn knowledge from a source network that possesses abundant labeled data. It is important that the state-of-the-art studies require the participated datasets of networks to have the same features, thus limiting the efficiency, flexibility as well as scalability of intrusion detection systems. However, our proposed framework can address these problems by exchanging the learning knowledge among various deep learning models, even when their datasets have different features. Extensive experiments on recent real-world cybersecurity datasets show that the proposed framework can improve more than 40% as compared to the state-of-the-art deep learning based approaches.
翻译:联邦学习联合会(FL)最近已成为网络攻击探测系统的有效方法,特别是在互联网电话网(IoT)网络中。通过在IoT网关上传播学习过程,FL可以提高学习效率,减少通信间接费用,加强网络攻击探测系统的隐私。在这类系统中实施FL系统的挑战包括缺乏标签数据,不同IoT网络的数据特征差异。在本文件中,我们提出一个新的合作学习框架,利用转移学习(TL)来克服这些挑战。特别是,我们开发了一种新的协作学习方法,使拥有无标签数据的目标网络能够有效和迅速地从拥有大量标签数据的来源网络中学习知识。最新的最新研究要求参与的网络数据集具有相同的特征,从而限制入侵探测系统的效率、灵活性和可扩展性。然而,我们提出的框架可以通过在各种深层次学习模型之间交流知识来解决这些问题,即使它们的数据集有不同的特征。对最近拥有大量标签数据的来源网络进行广泛的实验,以便能有效和迅速地从拥有知识。重要的是,最新的技术研究要求参与的网络数据集具有相同的特征,从而限制入侵探测系统的效率、灵活性和可扩缩度。然而,我们提出的框架可以通过在各种深层次学习模式之间交换知识来解决这些问题,即使它们的数据集具有不同的特性。比较以改进最近的深层次数据集。