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A Comprehensive Survey of Digital Twins and Federated Learning for Industrial Internet of Things (IIoT), Internet of Vehicles (IoV) and Internet of Drones (IoD)
2022
Applied System Innovation
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As a result of the advancement in the fourth industrial revolution and communication technology, the use of digital twins (DT) and federated learning (FL) in the industrial Internet of Things (IIoT), the ...
However, the deployment of DT and FL for IoV is challenging. In this survey, we focus on DT and FL for IIoT, IoV, and IoD. Initially, we analyzed the existing surveys. ...
We analyzed the use of DT and FL in the IIoT, IoV, and IoD. We summarized several state-of the art studies using DT and FL for the IIoT, IoV, and IoD. ...
doi:10.3390/asi5030056
fatcat:vnznyern55eqlmdxkbx7d45wom
Table of contents
2021
IEEE Transactions on Network and Service Management
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Das 1690 QoSChain: Provisioning Inter-AS QoS in Software-Defined Networks With Blockchain . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
TTP to IoC: Advanced Persistent Graphs for Threat Hunting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ...
doi:10.1109/tnsm.2021.3081191
fatcat:3m3td5s3snhfhcgukcuqoavdlu
Turning the Hunted into the Hunter via Threat Hunting: Life Cycle, Ecosystem, Challenges and the Great Promise of AI
[article]
2022
arXiv
pre-print
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We specifically establish a life cycle and ecosystem for privacy-threat hunting in addition to identifying the related challenges. We also discovered how critical the use of AI is in threat hunting. ...
This work paves the way for future work in this area as it provides the foundational knowledge to make meaningful advancements for threat hunting. ...
The application of threat hunting can be seen in smart homes, smart cities, IoT, industrial cyber-physical systems, Windows and Android systems, time and safety-critical systems, software defined networks ...
arXiv:2204.11076v1
fatcat:jc3bghaiungxxlta5itgjnla2a
Defensive Cyberspace: Navigating the Landscape of Cyber Security
[article]
2024
Zenodo
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Cyber Threat Landscape 4. Risk Management in Cyber Security 5. Network Security 6. Endpoint Security 7. Identity and Access Management 8. Incident Response and Forensics 9. ...
Security Awareness and Training 10. Securing Cloud Environments 11. Emerging Technologies and Cyber Security 12. International Cyber Security Collaboration 13. ...
Deep Learning for Cybersecurity: Neural Networks: Deep learning models, such as neural networks, excel at complex pattern recognition tasks in cybersecurity. ...
doi:10.5281/zenodo.10529044
fatcat:ydw65ur62fhzlbtmj4oslmhxui
Generative AI for Cyber Threat-Hunting in 6G-enabled IoT Networks
[article]
2023
arXiv
pre-print
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In this paper, we discuss the use of generative AI for cyber threat-hunting (CTH) in 6G-enabled IoT networks. ...
Generative Artificial Intelligence (AI) can be used to detect and prevent cyber attacks by continuously learning and adapting to new threats and vulnerabilities. ...
Therefore, we propose a new GAN and Transformer-based model for Cyber Threat-Hunting in 6G-enabled IoT Networks. ...
arXiv:2303.11751v1
fatcat:fsama6qifngwjab3pv6yrth6fa
Guest Editors' Introduction: Special Issue on Latest Developments for Security Management of Networks and Services
2021
IEEE Transactions on Network and Service Management
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She began her career in industry and became a Chartered Engineer in 2006 having worked as a Systems Engineer and an Engineering Group Leader with Airbus. ...
She has published a series of IEEE/ACM papers on security designs and solutions for softwarized networks based on her research on the development of network security architectures and security functions ...
In "A Multi-Dimensional Deep Learning Framework for IoT Malware Classification and Family Attribution," M. Dib et al. ...
doi:10.1109/tnsm.2021.3079189
fatcat:bzciidpzyjgknkarhziwpe26cu
Towards Trusted and Intelligent Cyber-Physical Systems: A Security-by-Design Approach
[article]
2022
arXiv
pre-print
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The complexity of cyberattacks in Cyber-Physical Systems (CPSs) calls for a mechanism that can evaluate the operational behaviour and security without negatively affecting the operation of live systems ...
Furthermore, we integrate blockchain to safeguard product lifecycle data. We discuss the applicability of the proposed framework for the automotive industry as a CPS use case. ...
Thus, loopholes in the system infrastructure enable attackers to gain deep knowledge of system behavior and launch covert attacks or Advanced Persistent Threats (APTs). ...
arXiv:2105.08886v3
fatcat:w7yyixz7qvgw5eaw3rjhoflol4
Internet of Things Botnet Detection Approaches: Analysis and Recommendations for Future Research
2021
Applied Sciences
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IoT botnets, for instance, have become a critical threat; however, systematic and comprehensive studies analyzing the importance of botnet detection methods are limited in the IoT environment. ...
At the same time, it has resulted in an increase in cybersecurity risks due to the lack of security for IoT devices. ...
Therefore, the authors gratefully acknowledge the technical and financial support from the Ministry of Education and King Abdulaziz University, Jeddah, Saudi Arabia. ...
doi:10.3390/app11125713
fatcat:d56mns6avfhwtk4rqkwxoomoqi
An Ensemble Deep Learning-based Cyber-Attack Detection in Industrial Control System
2020
IEEE Access
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The integration of communication networks and the Internet of Things (IoT) in Industrial Control Systems (ICSs) increases their vulnerability towards cyber-attacks, causing devastating outcomes. ...
INDEX TERMS Cyber-attacks, critical infrastructure, industrial control system, integrity attack, operation technology, information technology, deep learning, neural network. ...
His research interests include building AI-powered solutions to support cyber threat attribution, cyber threat hunting, and digital forensics tasks in the Internet of Things (IoT), the Industrial IoT, ...
doi:10.1109/access.2020.2992249
fatcat:vtudysg4z5fe5f3e2ppylysx7a
Internet of Things in Industry: Research Profiling, Application, Challenges and Opportunities—A Review
2022
Energies
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The paper explains the concepts of IoT, IIoT and Industry 4.0. It highlights the accompanying opportunities, threats and challenges related to their implementation. ...
IoT is being implemented in various areas of the modern economy, for example, healthcare, quality control, logistics, energy, agriculture and production. ...
Proposed Subject Area 1 Brick red 222 Software engineering, System engineering 2 Green 211 Industry 4.0 3 Turquoise 168 Deep Learning, Data Mining 4 Lime green 128 Data management 5 Lilac 104 Internet ...
doi:10.3390/en15051806
fatcat:nszzvslwkvekritsntjsnvqxbe
Cyber Threat Intelligence on Blockchain: A Systematic Literature Review
2024
Computers
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Cyber Threat Intelligence (CTI) has become increasingly important in safeguarding organizations against cyber threats. ...
, IPFS, deep learning, and encryption. ...
[67] introduce FedChain-Hunter, a threat-hunting framework that combines blockchain and Federated Learning (FL) to collaboratively detect cyber threats while upholding data privacy and transparent data ...
doi:10.3390/computers13030060
fatcat:5rnxcialwrdnxgodjxzuay4oga
Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review
2022
Electronics
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This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. ...
In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially ...
Industrial
IoT application dataset RNN Diro and Chilamkurti, [7] A new method for cybersecurity attack detection using a deep learning method in the social IoT was investigated. ...
doi:10.3390/electronics11020198
fatcat:fzqm47exe5dgjeqlxjhagrypoa
IoVT: Internet of Vulnerable Things? Threat Architecture, Attack Surfaces, and Vulnerabilities in Internet of Things and Its Applications towards Smart Grids
2020
Energies
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To mitigate these issues, this paper gives unique insights for handling the growing vulnerabilities in common IoT devices and proposes a threat architecture for IoT, addressing threats in the context of ...
Moreover, the vulnerabilities in an IoT system are exploited in chains to penetrate deep into the network and yield more adverse aftereffects. ...
[25] 2019 Risk analysis of threats with respect to all the layers of IoT To detect cyber-attacks (high accuracy) with Software-defined networking (SDN). ...
doi:10.3390/en13184813
fatcat:dmgwl4gw4vg5tcwh253cuhrn7q
MVFCC: A Multi-View Fuzzy Consensus Clustering Model for Malware Threat Attribution
2020
IEEE Access
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Utilizing fuzzy-learning systems for threat attribution: pattern-based machine learning agents such as deep learners or classifiers are good in identifying weaponized payloads with similar patterns to ...
In Section II, we review the related work in cyberthreat hunting and threat attributions. In Section III, we present our MVFCC model for cyberthreat attribution. ...
His lab is focused on building AI-powered solutions to support cyber threat attribution, cyber threat hunting, and digital forensics tasks in the Internet of Things (IoT), industrial IoT, and Internet ...
doi:10.1109/access.2020.3012907
fatcat:5vc2wvb3gbf2jmgbhekxlkspbi
The Dichotomy of Cloud and IoT: Cloud-Assisted IoT From a Security Perspective
[article]
2022
arXiv
pre-print
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In recent years, the existence of a significant cross-impact between Cloud computing and Internet of Things (IoT) has lead to a dichotomy that gives raise to Cloud-Assisted IoT (CAIoT) and IoT-Based Cloud ...
Although it is pertinent to study both technologies, this paper focuses on CAIoT, and especially its security issues, which are inherited from both Cloud computing and IoT. ...
Moreover, the authors in [194] summarised the study and proposed a cross-architecture IoT malware threat hunting methodology based on advanced ensemble learning as an MTHAEL model. ...
arXiv:2207.01590v2
fatcat:2kznp4sj7resznpiylio3wfrsa
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