Deep-Learning-Based Blockchain for Secure Zero Touch Networks

Kumar, Randhir; Kumar, Prabhat; Aloqaily, Moayad; Aljuhani, Ahamed

doi:10.1109/mcom.001.2200294

Cited by 19 publications

(3 citation statements)

References 13 publications

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“…As insecure data exchange over public channels carries security risks, Ref. [37] analyzes attack surfaces and architectural flaws in ZTNs enabled by IoT. It presents a deep learning and blockchain-assisted case study for secure data sharing, including a novel intrusion detection system and an authentication protocol.…”

Section: Related Work In Abe and Other Security Schemesmentioning

confidence: 99%

Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

Shruti

Rani

Sah

et al. 2023

Sensors

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Most data nowadays are stored in the cloud; therefore, cloud computing and its extension—fog computing—are the most in-demand services at the present time. Cloud and fog computing platforms are largely used by Internet of Things (IoT) applications where various mobile devices, end users, PCs, and smart objects are connected to each other via the internet. IoT applications are common in several application areas, such as healthcare, smart cities, industries, logistics, agriculture, and many more. Due to this, there is an increasing need for new security and privacy techniques, with attribute-based encryption (ABE) being the most effective among them. ABE provides fine-grained access control, enables secure storage of data on unreliable storage, and is flexible enough to be used in different systems. In this paper, we survey ABE schemes, their features, methodologies, benefits/drawbacks, attacks on ABE, and how ABE can be used with IoT and its applications. This survey reviews ABE models suitable for IoT platforms, taking into account the desired features and characteristics. We also discuss various performance indicators used for ABE and how they affect efficiency. Furthermore, some selected schemes are analyzed through simulation to compare their efficiency in terms of different performance indicators. As a result, we find that some schemes simultaneously perform well in one or two performance indicators, whereas none shines in all of them at once. The work will help researchers identify the characteristics of different ABE schemes quickly and recognize whether they are suitable for specific IoT applications. Future work that may be helpful for ABE is also discussed.

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Section: Related Work In Abe and Other Security Schemesmentioning

confidence: 99%

Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

Shruti

Rani

Sah

et al. 2023

Sensors

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“…Fifth-generation (5G) 4 cellular networks are currently advancing and putting different demands on them, notably those for dependability, delay, and throughput. Confidentiality is another issue that has drawn the most interest from the networking and telecommunications research community as networks are becoming more complicated.…”

Section: Introductionmentioning

confidence: 99%

NGMD: next generation malware detection in federated server with deep neural network model for autonomous networks

Babbar,

Rani,

Boulila

2024

Sci Rep

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Distributed denial-of-service (DDoS) attacks persistently proliferate, impacting individuals and Internet Service Providers (ISPs). Deep learning (DL) models are paving the way to address these challenges and the dynamic nature of potential threats. Traditional detection systems, relying on signature-based techniques, are susceptible to next-generation malware. Integrating DL approaches in cloud-edge/federated servers enhances the resilience of these systems. In the Internet of Things (IoT) and autonomous networks, DL, particularly federated learning, has gained prominence for attack detection. Unlike conventional models (centralized and localized DL), federated learning does not require access to users’ private data for attack detection. This approach is gaining much interest in academia and industry due to its deployment on local and global cloud-edge models. Recent advancements in DL enable training a quality cloud-edge model across various users (collaborators) without exchanging personal information. Federated learning, emphasizing privacy preservation at the cloud-edge terminal, holds significant potential for facilitating privacy-aware learning among collaborators. This paper addresses: (1) The deployment of an optimized deep neural network for network traffic classification. (2) The coordination of federated server model parameters with training across devices in IoT domains. A federated flowchart is proposed for training and aggregating local model updates. (3) The generation of a global model at the cloud-edge terminal after multiple rounds between domains and servers. (4) Experimental validation on the BoT-IoT dataset demonstrates that the federated learning model can reliably detect attacks with efficient classification, privacy, and confidentiality. Additionally, it requires minimal memory space for storing training data, resulting in minimal network delay. Consequently, the proposed framework outperforms both centralized and localized DL models, achieving superior performance.

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“…Recently, several concepts or architectures have been proposed for network automation and optimization, such as Self-Organizing Network Management (SON), Intent-Based Network Management (IBN), and Autonomic Network Management (ANM) [2]. More recently, Zero-Touch Networks (ZTNs) have emerged as a transformative approach and a next-generation system for automating network operations to minimize manual network tasks, including network management, configuration, and optimization [3]. ZTNs aim to enhance network efficiency, reliability, and security by leveraging Artificial Intelligence Li Yang is with the Faculty of Business and Information Technology, Ontario Tech University, Oshawa, ON L1G 0C5, Canada (e-mail: li.yang@ontariotechu.ca) Mirna El Rajab and Abdallah Shami are with the Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada (e-mails: melrajab@uwo.ca, abdallah.shami@uwo.ca) Sami Muhaidat is with the Department of Electrical Engineering and Computer Science, Khalifa University, Abu Dhabi 127788, UAE (e-mail: sami.muhaidat@ku.ac.ae) (AI) and Machine Learning (ML) techniques, as well as the fifth generation of network (5G) technologies, like Software-Defined Networking (SDN) and Network Functions Virtualization (NFV).…”

Section: Introductionmentioning

confidence: 99%

Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis

Yang,

Rajab,

Shami

et al. 2024

Preprint

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Zero-Touch Networks (ZTNs) represent a state-of-the-art paradigm shift towards fully automated and intelligent network management, enabling the automation and intelligence required to manage the complexity, scale, and dynamic nature of next-generation (6G) networks. ZTNs leverage Artificial Intelligence (AI) and Machine Learning (ML) to enhance operational efficiency, support intelligent decision-making, and ensure effective resource allocation. However, the implementation of ZTNs is subject to security challenges that need to be resolved to achieve their full potential. In particular, two critical challenges arise: the need for human expertise in developing AI/ML-based security mechanisms, and the threat of adversarial attacks targeting AI/ML models. In this survey paper, we provide a comprehensive review of current security issues in ZTNs, emphasizing the need for advanced AI/ML-based security mechanisms that require minimal human intervention and protect AI/ML models themselves. Furthermore, we explore the potential of Automated ML (AutoML) technologies in developing robust security solutions for ZTNs. Through case studies, we illustrate practical approaches to securing ZTNs against both conventional and AI/ML-specific threats, including the development of autonomous intrusion detection systems and strategies to combat Adversarial ML (AML) attacks. The paper concludes with a discussion of the future research directions for the development of ZTN security approaches. Abstract content goes here

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Deep-Learning-Based Blockchain for Secure Zero Touch Networks

Cited by 19 publications

References 13 publications

Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

Attribute-Based Encryption Schemes for Next Generation Wireless IoT Networks: A Comprehensive Survey

NGMD: next generation malware detection in federated server with deep neural network model for autonomous networks

Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis

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