Internet of things (IoT) devices are prone to attacks due to the limitation of their privacy and security components. These attacks vary from exploiting backdoors to disrupting the communication network of the devices. Intrusion Detection Systems (IDS) play an essential role in ensuring information privacy and security of IoT devices against these attacks. Recently, deep learning-based IDS techniques are becoming more prominent due to their high classification accuracy. However, conventional deep learning techniques jeopardize user privacy due to the transfer of user data to a centralized server. Federated learning (FL) is a popular privacy-preserving decentralized learning method. FL enables training models locally at the edge devices and transferring local models to a centralized server instead of transferring sensitive data. Nevertheless, FL can suffer from reverse engineering ML attacks that can learn information about the user's data from model. To overcome the problem of reverse engineering, mimic learning is another way to preserve the privacy of ML-based IDS. In mimic learning, a student model is trained with the public dataset, which is labeled with the teacher model that is trained by sensitive user data. In this work, we propose a novel approach that combines the advantages of FL and mimic learning, namely federated mimic learning to create a distributed IDS while minimizing the risk of jeopardizing users' privacy, and benchmark its performance compared to other ML-based IDS techniques using NSL-KDD dataset. Our results show that we can achieve 98.11% detection accuracy with federated mimic learning.
The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. The utilisation of Machine Learning (ML) capabilities in the defence against IoT cyber attacks has many potential benefits. However, the currently proposed frameworks do not consider data privacy, secure architectures, and/or scalable deployments of IoT ecosystems. In this paper, we propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion dete…
University of Pittsburgh（匹兹堡大学）；University of Massachusetts Amherst（马萨诸塞大学阿默斯特分校）
Internet of Things (IoT) devices are becoming increasingly popular and are influencing many application domains such as healthcare and transportation. These devices are used for real-world applications such as sensor monitoring, real-time control. In this work, we look at differentially private (DP) neural network (NN) based network intrusion detection systems (NIDS) to detect intrusion attacks on networks of such IoT devices. Existing NN training solutions in this domain either ignore privacy considerations or assume that the privacy requirements are homogeneous across all users. We show that…
SecureDyn-FL: A Robust Privacy-Preserving Federated Learning Framework for Intrusion Detection in IoT Networks
Sir Syed CASE Institute of Technology（Sir Syed CASE 理工学院）；Habib University（哈比布大学）；Yeunganam University（延世南大学）；University of Nottingham Ningbo China（诺丁汉大学宁波分校）；Korea University（高丽大学）
University of Murcia（穆尔西亚大学）；European Commission, Joint Research Centre（欧盟委员会联合研究中心）
The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks through an effective and efficient detection process. In the context of the Internet of Things (IoT), most ML-enabled IDS approaches use centralized approaches where IoT devices share their data with data centers for further analysis. To mitigate privacy concerns associated with centralized approaches, in recent years the use of Federated Learning (FL) has attracted a significant interest in different sectors, including hea…
University of Pisa（比萨大学）；CNR, Institute of Informatics and Telematics（意大利国家研究委员会信息学与电信研究所）；CNR, Institute of Information Science and Technologies（意大利国家研究委员会信息科学与技术研究所）
Automatic traffic classification is increasingly becoming important in traffic engineering, as the current trend of encrypting transport information (e.g., behind HTTP-encrypted tunnels) prevents intermediate nodes from accessing end-to-end packet headers. However, this information is crucial for traffic shaping, network slicing, and Quality of Service (QoS) management, for preventing network intrusion, and for anomaly detection. 3D networks offer multiple routes that can guarantee different levels of QoS. Therefore, service classification and separation are essential to guarantee the required…
The network security analyzers use intrusion detection systems (IDSes) to distinguish malicious traffic from benign ones. The deep learning-based IDSes are proposed to auto-extract high-level features and eliminate the time-consuming and costly signature extraction process. However, this new generation of IDSes still suffers from a number of challenges. One of the main issues of an IDS is facing traffic concept drift which manifests itself as new (i.e., zero-day) attacks, in addition to the changing behavior of benign users/applications. Furthermore, a practical DL-based IDS needs to be confor…
Anomaly detection for the Internet of Things (IoT) is a major intelligent service required by many fields, including intrusion detection, device-activity analysis, and security supervision. However, the heterogeneous distribution of data and resource-constrained end nodes present challenges for existing anomaly detection models. Due to the advantages of flexible deployment and multi-dimensional resources, high altitude platform stations (HAPSs) and unmanned aerial vehicles (UAVs), which are important components of vertical heterogeneous networks (VHetNets), have significant potential for sensi…
The University of Hong Kong（香港大学）；Southern University of Science and Technology（南方科技大学）；The Chinese University of Hong Kong (Shenzhen)（香港中文大学（深圳））；Peng Cheng Laboratory（鹏城实验室）
Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices. In this paper, a novel topology-optimized federated edge learning (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To sol…
