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Article
Open AccessSPEI-FL: Serverless Privacy Edge Intelligence-Enabled Federated Learning in Smart Healthcare Systems
Smart healthcare systems promise significant benefits for fast and accurate medical decisions. However, working with personal health data presents new privacy issues and constraints that must be solved from a ...
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Article
A threat intelligence framework for protecting smart satellite-based healthcare networks
Human-to-machine (H2M) communication is an important evolution in the industrial internet of health things (IIoHT), where many H2M interfaces are remotely interacting with industrial and medical assets. Lightw...
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Article
Open AccessAn improved Henry gas optimization algorithm for joint mining decision and resource allocation in a MEC-enabled blockchain networks
This paper investigates a wireless blockchain network with mobile edge computing in which Internet of Things (IoT) devices can behave as blockchain users (BUs). This blockchain network’s ultimate goal is to in...
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Chapter and Conference Paper
An Explainable Intrusion Discovery Framework for Assessing Cyber Resilience in the Internet of Things Networks
Cyber hardening systems, such as intrusion detection and firewalls, are no longer sufficient to keep cyber systems safe from today’s smart and well-resourced attackers. However, these systems have high false-p...
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Article
Open AccessCyber Threat Intelligence Sharing Scheme Based on Federated Learning for Network Intrusion Detection
The uses of machine learning (ML) technologies in the detection of network attacks have been proven to be effective when designed and evaluated using data samples originating from the same organisational netwo...
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Article
Privacy-preserving big data analytics for cyber-physical systems
Cyber-physical systems (CPS) generate big data collected from combining physical and digital entities, but the challenge of CPS privacy-preservation demands further research to protect CPS sensitive informatio...
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Chapter
Unsupervised Deep Learning for Secure Internet of Things
This chapter elaborates on the potential of unsupervised deep learning solutions for assuring the security of IoT-based systems to give the reader an insightful discussion of how these solutions could satisfy ...
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Chapter
Deep Reinforcement Learning for Secure Internet of Things
Reinforcement learning (RL) is identified as a branch of artificial intelligence (AI) the seek to addresses the dilemma of automated learning of ideal determinations throughout time, which is a popular and bro...
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Chapter
Internet of Things, Preliminaries and Foundations
This chapter mainly presents a detailed discussion of the IoT technologies and dependent systems with the main objectives of emphasizing the main attributes of IoT systems that might possibly threaten the secu...
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Chapter
Digital Forensics in Internet of Things
As IoT technology becomes an integral part of everyday life, enhancing productivity for businesses through automation, it should come as no surprise that attackers would seek to exploit these systems and the s...
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Chapter
Introduction Conceptualization of Security, Forensics, and Privacy of Internet of Things: An Artificial Intelligence Perspective
The Internet of Things (IoT) is a rapidly evolving technology that empowers billions of globally distributed physical things to be interconnected over the internet to capture, collect, exchange, and share a wi...
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Chapter
Internet of Things Security Requirements, Threats, Attacks, and Countermeasures
This chapter elaborates on different security aspects to be taken into accounts during the development and the deployments of IoT architecture. To make the reader about the security of the IoT based system, th...
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Chapter
Challenges, Opportunities, and Future Prospects
The central intention of this chapter is to discuss the primary security challenges in internet of things (IoT) environments with the main emphasis on the opportunities for deep learning for securing and maint...
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Chapter
Supervised Deep Learning for Secure Internet of Things
This chapter elaborates on the potential of supervised deep learning solutions for fulfilling the security requirements of IoT-based systems with main aim to realize a reliable and trustworthy IoT environment.
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Chapter
Semi-supervised Deep Learning for Secure Internet of Things
Previous chapters demonstrate the great success achieved by principally in supervised settings, by leveraging a larger volume of precisely annotated dataset. Nevertheless, annotated data instances are frequent...
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Chapter
Federated Learning for Privacy-Preserving Internet of Things
The rapid evolution of the Internet of Things (IoT) and relevant applications have been paving the way toward the fulfillment of smart cities. Smart cities are thought to come up with multiple crucial smart Io...
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Chapter and Conference Paper
A Novel Cognitive Computing Technique Using Convolutional Networks for Automating the Criminal Investigation Process in Policing
Criminal Investigation (CI) plays an important role in policing, where police use various traditional techniques to investigate criminal activities such as robbery and assault. However, the techniques should h...
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Chapter and Conference Paper
A Collaborative Intrusion Detection System Using Deep Blockchain Framework for Securing Cloud Networks
Security solutions, especially intrusion detection and blockchain, have been individually employed in the cloud for detecting cyber threats and preserving private data. Both solutions demand ensembled models-b...
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Chapter and Conference Paper
NetFlow Datasets for Machine Learning-Based Network Intrusion Detection Systems
Machine Learning (ML)-based Network Intrusion Detection Systems (NIDSs) have become a promising tool to protect networks against cyberattacks. A wide range of datasets are publicly available and have been used...
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Chapter and Conference Paper
Correction to: A Tri-level Programming Framework for Modelling Attacks and Defences in Cyber-Physical Systems
The original version of this chapter was revised. The following corrections have been incorporated:
