Browsing by Author "Shu, Lei"

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    Authentication Protocols for Internet of Things: A Comprehensive Survey
    (Hindawi, 2017-11-06) Ferrag, Mohamed Amine; Maglaras, Leandros; Janicke, Helge; Jiang, Jianmin; Shu, Lei
    In this paper, a comprehensive survey of authentication protocols for Internet of Things (IoT) is presented. Specifically more than forty authentication protocols developed for or applied in the context of the IoT are selected and examined in detail. These protocols are categorized based on the target environment: (1) Machine to Machine Communications (M2M), (2) Internet of Vehicles (IoV), (3) Internet of Energy (IoE), and (4) Internet of Sensors (IoS). Threat models, countermeasures, and formal security verification techniques used in authentication protocols for the IoT are presented. In addition a taxonomy and comparison of authentication protocols that are developed for the IoT in terms of network model, specific security goals, main processes, computation complexity, and communication overhead are provided. Based on the current survey, open issues are identified and future research directions are proposed.
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    Digital Agriculture Security: Aspects, Threats, Mitigation Strategies, and Future Trends
    (IEEE, 2022) Friha, Othmane; Ferrag, Mohamed Amine; Maglaras, Leandros; Shu, Lei
    Agricultural advancement over time has been an essential component of human civilization's evolution. The rapid progress of emerging technologies is driving digital empowerment in nearly every industry, including the agricultural sector. Regardless of the benefits derived from this evolution, there are several security threats involved, which can have a significant impact on the agricultural domain. This paper provides a review of digital agriculture from the security perspective. First, we provide a clear introduction to the digital agriculture ecosystem from a technological standpoint. Next, we highlight the key aspects of digital agriculture security to get the reader on board. Then, we focus on the current and potential threats facing these particular systems, as well as current and/or possible mitigation strategies to address these threats. Finally, we discuss future research directions and open challenges.
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    Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis
    (IEEE, 2021-10-06) Ferrag, Mohamed Amine; Friha, Othmane; Maglaras, Leandros; Janicke, Helge; Shu, Lei
    In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.
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    FELIDS: Federated Learning-based Intrusion Detection System for Agricultural Internet of Things
    (Elsevier, 2022) Friha, Othmane; Ferrag, Mohamed Amine; Shu, Lei; Maglaras, Leandros; Choo, Kim-Kwang Raymond; Mehdi, Nafaa
    In this paper, we propose a federated learning-based intrusion detection system, named FELIDS, for securing agricultural-IoT infrastructures. Specifically, the FELIDS system protects data privacy through local learning, where devices benefit from the knowledge of their peers by sharing only updates from their model with an aggregation server that produces an improved detection model. In order to prevent Agricultural IoTs attacks, the FELIDS system employs three deep learning classifiers, namely, deep neural networks, convolutional neural networks, and recurrent neural networks. We study the performance of the proposed IDS on three different sources, including, CSE-CIC-IDS2018, MQTTset, and InSDN. The results demonstrate that the FELIDS system outperforms the classic/centralized versions of machine learning (non-federated learning) in protecting the privacy of IoT devices data and achieves the highest accuracy in detecting attacks.
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    Internet of Things for the Future of Smart Agriculture: A Comprehensive Survey of Emerging Technologies
    (IEEE, 2021-03-10) Friha, O.; Ferrag, Mohamed Amine; Shu, Lei; Maglaras, Leandros; Wang, X.
    This paper presents a comprehensive review of emerging technologies for the internet of things (IoT)-based smart agriculture. We begin by summarizing the existing surveys and describing emergent technologies for the agricultural IoT, such as unmanned aerial vehicles, wireless technologies, open-source IoT platforms, software defined networking (SDN), network function virtualization (NFV) technologies, cloud/fog computing, and middleware platforms. We also provide a classification of IoT applications for smart agriculture into seven categories: including smart monitoring, smart water management, agrochemicals applications, disease management, smart harvesting, supply chain management, and smart agricultural practices. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward supply chain management based on the blockchain technology for agricultural IoTs. Furthermore, we present real projects that use most of the aforementioned technologies, which demonstrate their great performance in the field of smart agriculture. Finally, we highlight open research challenges and discuss possible future research directions for agricultural IoTs.
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    Security and Privacy for Green IoT-based Agriculture: Review, Blockchain solutions, and Challenges
    (IEEE, 2020-02-11) Ferrag, Mohamed Amine; Shu, Lei; Yang, Xing; Derhab, Abdelouahid; Maglaras, Leandros
    This paper presents research challenges on security and privacy issues in the field of green IoT-based agriculture. We start by describing a four-tier green IoT-based agriculture architecture and summarizing the existing surveys that deal with smart agriculture. Then, we provide a classification of threat models against green IoT-based agriculture into five categories, including, attacks against privacy, authentication, confidentiality, availability, and integrity properties. Moreover, we provide a taxonomy and a side-by-side comparison of the state-of-the-art methods toward secure and privacy-preserving technologies for IoT applications and how they will be adapted for green IoT-based agriculture. In addition, we analyze the privacy-oriented blockchain-based solutions as well as consensus algorithms for IoT applications and how they will be adapted for green IoT-based agriculture. Based on the current survey, we highlight open research challenges and discuss possible future research directions in the security and privacy of green IoT-based agriculture.
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    Security and Privacy in Fog Computing: Challenges
    (IEEE, 2017-09-06) Mukherjee, Mithun; Matam, Rakesh; Shu, Lei; Maglaras, Leandros; Ferrag, Mohamed Amine; Choudhry, Nikumani; Kumar, Vikas
    Fog computing paradigm extends the storage, networking, and computing facilities of the cloud computing toward the edge of the networks while offloading the cloud data centers and reducing service latency to the end users. However, the characteristics of fog computing arise new security and privacy challenges. The existing security and privacy measurements for cloud computing cannot be directly applied to the fog computing due to its features, such as mobility, heterogeneity, and large-scale geo-distribution. This paper provides an overview of existing security and privacy concerns, particularly for the fog computing. Afterward, this survey highlights ongoing research effort, open challenges, and research trends in privacy and security issues for fog computing.