Introduction to the special section on advances of machine learning in cybersecurity (VSI-mlsec)

Namasudra, Suyel; Crespo, Rubén González; Kumar, Sathish

doi:10.1016/j.compeleceng.2022.108048

Cited by 15 publications

(5 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The IoT security management platform [ 17 ] plays a pivotal role in ensuring the security of IoT applications. It facilitates comprehensive management, monitoring, and control of IoT applications, offering unified security management for IoT devices and systems.…”

Section: Related Workmentioning

confidence: 99%

A Microservice and Serverless Architecture for Secure IoT System

Ouyang

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

In cross-border transactions, the transmission and processing of logistics information directly affect the trading experience and efficiency. The use of Internet of Things (IoT) technology can make this process more intelligent, efficient, and secure. However, most traditional IoT logistics systems are provided by a single logistics company. These independent systems need to withstand high computing loads and network bandwidth when processing large-scale data. Additionally, due to the complex network environment of cross-border transactions, the platform’s information security and system security are difficult to guarantee. To address these challenges, this paper designs and implements an intelligent cross-border logistics system platform that combines serverless architecture and microservice technology. This system can uniformly distribute the services of all logistics companies and divide microservices based on actual business needs. It also studies and designs corresponding Application Programming Interface (API) gateways to solve the interface exposure problem of microservices, thereby ensuring the system’s security. Furthermore, asymmetric encryption technology is used in the serverless architecture to ensure the security of cross-border logistics data. The experiments show that this research solution validates the advantages of combining serverless architecture and microservices, which can significantly reduce the operating costs and system complexity of the platform in cross-border logistics scenarios. It allows for resource expansion and billing based on application program requirements at runtime. The platform can effectively improve the security of cross-border logistics service processes and meet cross-border transaction needs in terms of data security, throughput, and latency.

show abstract

Section: Related Workmentioning

confidence: 99%

A Microservice and Serverless Architecture for Secure IoT System

Ouyang

Wang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…Ref. [12] discussed the use of machine learning in network security research in their paper, pointing out that while we use machine learning to maintain network security, attackers are attempting to use machine learning to launch network attacks. Therefore, the development of new network security technologies is imminent.…”

Section: Related Workmentioning

confidence: 99%

“…Data security [12] Machine Learning Machine Learning Platform [13] RBF neural network Radial Basis Function neural network sliding mode controller [14] Fast R-CNN Multi-strategy region proposal network algorithm [15] Data fusion approaches Data collection framework Asymmetric network [16] Blockchain-as-a-Service multi-preference matching [17] Deep reinforcement learning Cloud-edge cooperative content-delivery strategy [18] Mobile edge computing Task offloading and fault tolerance algorithm traditional consensus algorithms [19] PoW [20] PoS [21] DPoS consensus algorithms in IoV [23] PoW,PoS Decentralized trust management system [24] PoW,PBFT Blockchain-based TM with conditional privacy-preserving scheme [25] DPoS Soft security solution [26] PBFT,EigenTrust T-PBFT…”

Section: Literature Basic Algorithm Innovationmentioning

confidence: 99%

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

Sun

Wang

et al. 2023

Symmetry

View full text Add to dashboard Cite

With the enhanced interoperability of information among vehicles, the demand for collaborative sharing among vehicles increases. Based on blockchain, the classical consensus algorithms in collaborative IoV (Internet of Vehicle), such as PoW (Proof of Work), PoS (Proof of Stake), and DPoS (Delegated Proof of Stake), only consider the node features, which is hard to adapt to the immediacy and flexibility of vehicles. On the other hand, classical consensus algorithms often require mass computing, which undoubtedly increases the communication overhead, resulting in the inability to achieve collaborative IoV under asymmetric networks. Therefore, proposing a low failure rate consensus algorithm that takes into account running time and energy consumption becomes a major challenge in IoV applications. This paper proposes an AI-enabled consensus algorithm with vehicle features, combining vehicle-based metrics and neural networks. First, we introduce vehicle-based metrics such as vehicle online time, performance, and behavior. Then, we propose an integral model and a hierarchical classification method, which combine with a BP neural network to obtain the optimal solution for interconnection. Among them, we also use Informer to predict the future online duration of vehicles, which effectively solves the situation that the primary node vehicle drops off in collaborative IoV. Finally, the experimentations show that the vehicle-based metrics eliminate the problem of the primary node vehicle being offline, which realizes the collaborative IoV considering vehicle features. Meanwhile, it reduces the vehicle network system delay and energy consumption.

show abstract

“…CNN has made greater accomplishments in image recognition and classification [6]. The popularity and accuracy of CNN for image classification have been optimized due to the largescale system for learning and image processing, higher-speed GPUs, and the huge availability of public datasets of an image [7]. The idea of smart waste classification with waste and trash images has tremendous potential.…”

Section: Introductionmentioning

confidence: 99%

Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT

Duhayyim

2023

Sustainability

View full text Add to dashboard Cite

The internet of things (IoT) paradigm roles an important play in enhancing smart city tracking applications and managing city procedures in real time. The most important problem connected to smart city applications has been solid waste management, which can have adverse effects on society’s health and environment. Waste management has developed a challenge faced by not only evolving nations but also established and developed counties. Solid waste management is an important and stimulating problem for environments across the entire world. Therefore, there is the need to develop an effective technique that will remove these problems, or at least decreases them to a minimal level. This study develops a modified cuttlefish swarm optimization with machine learning-based solid waste management (MCSOML-SWM) in smart cities. The MCSOML-SWM technique aims to recognize different categories of solid wastes and enable smart waste management. In the MCSOML-SWM model, a single shot detector (SSD) model allows effectual recognition of objects. Then, a deep convolutional neural network-based MixNet model was exploited to produce feature vectors. Since trial-and-error hyperparameter tuning is a tedious process, the MCSO algorithm was applied for automated hyperparameter tuning. For accurate waste classification, the MCSOML-SWM technique applies support vector machine (SVM) in this study. A comprehensive set of simulations demonstrate the improved classification performance of the MCSOML-SWM model with maximum accuracy of 99.34%.

show abstract

Introduction to the special section on advances of machine learning in cybersecurity (VSI-mlsec)

Cited by 15 publications

References 10 publications

A Microservice and Serverless Architecture for Secure IoT System

A Microservice and Serverless Architecture for Secure IoT System

AI-Enabled Consensus Algorithm in Human-Centric Collaborative Computing for Internet of Vehicle

Modified Cuttlefish Swarm Optimization with Machine Learning-Based Sustainable Application of Solid Waste Management in IoT

Contact Info

Product

Resources

About