This paper presents a novel Collaborative Cyberattack Detection (CCD) system aimed at enhancing the security of blockchain-based data-sharing networks by addressing the complex challenges associated with noise addition in federated learning models. Leveraging the theoretical principles of differential privacy, our approach strategically integrates noise into trained sub-models before reconstructing the global model through transmission. We systematically explore the effects of various noise types, i.e., Gaussian, Laplace, and Moment Accountant, on key performance metrics, including attack detection accuracy, deep learning model convergence time, and the overall runtime of global model generation. Our findings reveal the intricate trade-offs between ensuring data privacy and maintaining system performance, offering valuable insights into optimizing these parameters for diverse CCD environments. Through extensive simulations, we provide actionable recommendations for achieving an optimal balance between data protection and system efficiency, contributing to the advancement of secure and reliable blockchain networks.

In this work, we propose a novel machine learning empowered intrusion detection for Mobile Edge Computing (MEC) networks. Unlike most of the research works that focus on detecting attacks at the network layer, such as IP spoofing and Denial of Service (DoS) attacks, we aim to detect attacks/threats at the application layer, especially attacks caused by malicious codes embedded in offloaded computing tasks. This is an emerging issue in MEC networks as more and more MEC services allow MEC users to offload their computational tasks to the edge nodes to process. Yet, this is a very challenging problem in MEC, as data at the application layer is often complex and challenging to interpret, making anomaly detection difficult. Therefore, we first propose an effective solution to transfer data from the original offloading file to a new form, i.e., images, to make it more effective for the detection process. After that, a Convolutional Neural Network (CNN) and a collaborative learning process are proposed to learn information from training data (i.e., transformed images) and, at the same time, share the learned knowledge (i.e., trained models) together to improve the global accuracy in detecting attacks. Simulation results show that our approach can detect attacks with an accuracy of approximately 90%.

With the ever-increasing popularity of blockchain applications, securing blockchain networks plays a critical role in these cyber systems. In this paper, we first study cyberattacks (e.g., flooding of transactions, brute pass) in blockchain networks and then propose an efficient collaborative cyberattack detection model to protect blockchain networks. Specifically, we deploy a blockchain network in our laboratory to build a new dataset including both normal and attack traffic data. The main aim of this dataset is to generate actual attack data from different nodes in the blockchain network that can be used to train and test blockchain attack detection models. We then propose a realtime collaborative learning model that enables nodes in the network to share learning knowledge without disclosing their private data, thereby significantly enhancing system performance for the whole network. The extensive simulation and realtime experimental results show that our proposed detection model can detect attacks in the blockchain network with an accuracy of up to 97%.

This article aims to study intrusion attacks and then develop a novel cyberattack detection framework to detect cyberattacks at the network layer (e.g., brute password and flooding of transactions) of blockchain networks. Specifically, we first design and implement a blockchain network in our laboratory. This blockchain network will serve two purposes, i.e., to generate the real traffic data (including both normal data and attack data) for our learning models and to implement real-time experiments to evaluate the performance of our proposed intrusion detection framework. To the best of our knowledge, this is the first dataset that is synthesized in a laboratory for cyberattacks in a blockchain network. We then propose a novel collaborative learning model that allows efficient deployment in the blockchain network to detect attacks. The main idea of the proposed learning model is to enable blockchain nodes to actively collect data, learn the knowledge from data using the Deep Belief Network, and then share the knowledge learned from its data with other blockchain nodes in the network. In this way, we can not only leverage the knowledge from all the nodes in the network but also do not need to gather all raw data for training at a centralized node like conventional centralized learning solutions. Such a framework can also avoid the risk of exposing local data’s privacy as well as excessive network overhead/congestion. Both intensive simulations and real-time experiments clearly show that our proposed intrusion detection framework can achieve an accuracy of up to 98.6% in detecting attacks.

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. However, one of the biggest challenges for deploying FL in IoT networks is the unavailability of labeled data and dissimilarity of data features for training. In this article, 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 data sets 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 (DL) models, even when their data sets have different features. Extensive experiments on recent real-world cybersecurity data sets show that the proposed framework can improve more than 40% as compared to the state-of-the-art DL-based approaches.

Although the development of IoT Industry 4.0 has brought breakthrough achievements in many sectors, e.g., manufacturing, healthcare, and agriculture, it also raises many security issues to human beings due to a huge of emerging cybersecurity threats recently. In this paper, we propose a novel collaborative learning-based intrusion detection system which can be efficiently implemented in IoT Industry 4.0. In the system under consideration, we develop smart “filters” which can be deployed at the IoT gateways to promptly detect and prevent cyberattacks. In particular, each filter uses the collected data in its network to train its cyberattack detection model based on the deep learning algorithm. After that, the trained model will be shared with other IoT gateways to improve the accuracy in detecting intrusions in the whole system. In this way, not only the detection accuracy is improved, but our proposed system also can significantly reduce the information disclosure as well as network traffic in exchanging data among the IoT gateways. Through thorough simulations on real datasets, we show that the performance obtained by our proposed method can outperform those of the conventional machine learning methods.