IET Networks最新文献

The Industrial Internet of Things (IIoT) has transformed industrial operations with real-time monitoring and control, enhancing efficiency and productivity. However, this connectivity brings significant security challenges. This study addresses these challenges by identifying abnormal sensor data patterns using machine learning-based anomaly detection models. The proposed framework employs advanced algorithms to strengthen industrial defences against cyber threats and disruptions. Focusing on temperature anomalies, a critical yet often overlooked aspect of industrial security, this research fills a gap in the literature by evaluating machine learning models for this purpose. A novel variance-based model for temperature anomaly detection is introduced, demonstrating high efficacy with accuracy scores of 0.92 and 0.82 on the NAB and AnoML-IOT datasets, respectively. Additionally, the model achieved F1 scores of 0.96 and 0.89 on these datasets, underscoring its effectiveness in enhancing IIoT security and optimising cybersecurity for industrial processes. This research not only identifies security vulnerabilities but also presents concrete solutions to improve the security posture of IIoT systems.

Over the past two decades, the cloud computing paradigm has gradually attracted more popularity due to its efficient resource usage and simple service access model. Virtualisation technology is the fundamental element of cloud computing that brings several benefits to cloud users and providers, such as workload isolation, energy efficiency, server consolidation, and cost reduction. This paper examines the combination of operating system-level virtualisation (containers) and hardware-level virtualisation (virtual machines). To this end, the performance of containers running on top of virtual machines is experimentally compared with standalone virtual machines and containers based on different hardware resources, including the processor, main memory, disk, and network in a real testbed by running the most commonly used benchmarks. Paravirtualisation and full virtualisation as well as type 1 and type 2 hypervisors are covered in this study. In addition, three prevalent containerisation platforms are examined.

Forests play a pivotal role in protecting the environment, preserving vital natural resources, and ultimately sustaining human life. However, the escalating occurrences of forest fires, whether of human origin or due to climate change, poses a significant threat to this ecosystem. In recent decades, the emergence of the IoT has been characterised by the utilisation of smart sensors for real-time data collection. IoT facilitates proactive decision-making for forest monitoring, control, and protection through advanced data analysis techniques, including AI algorithms. This research study presents a comprehensive approach to deploying a dynamic and adaptable network topology in forest environments, aimed at optimising data transmission and enhancing system reliability. Three distinct topologies are proposed in this research study: direct transmission from nodes to gateways, cluster formation with multi-step data transmission, and clustering with data relayed by cluster heads. A key innovation is the use of high-powered telecommunication modules in cluster heads, enabling long-range data transmission while considering energy efficiency through solar power. To enhance system reliability, this study incorporates a reserve routing mechanism to mitigate the impact of node or cluster head failures. Additionally, the placement of gateway nodes is optimised using meta-heuristic algorithms, including particle swarm optimisation (PSO), harmony search algorithm (HSA), and ant colony optimisation for continuous domains (ACOR), with ACOR emerging as the most effective. The primary objective of this article is to reduce power consumption, alleviate network traffic, and decrease nodes' interdependence, while also considering reliability coefficients and error tolerance as additional considerations. As shown in the results, the proposed methods effectively reduce network traffic, optimise routing, and ensure robust performance across various environmental conditions, highlighting the importance of these tailored topologies in enhancing energy efficiency, data accuracy, and network reliability in forest monitoring applications.

The authors introduce an unsupervised Intrusion Detection System designed to detect zero-day distributed denial of service (DDoS) attacks in Internet of Things (IoT) networks. This system can identify anomalies without needing prior knowledge or training on attack information. Zero-day attacks exploit previously unknown vulnerabilities, making them hard to detect with traditional deep learning and machine learning systems that require pre-labelled data. Labelling data is also a time-consuming task for security experts. Therefore, unsupervised methods are necessary to detect these new threats. The authors focus on DDoS attacks, which have recently caused significant financial and service disruptions for many organisations. As IoT networks grow, these attacks become more sophisticated and harmful. The proposed approach detects zero-day DDoS attacks by using random projection to reduce data dimensionality and an ensemble model combining K-means, Gaussian mixture model, and one-class SVM with a hard voting technique for classification. The method was evaluated using the CIC-DDoS2019 dataset and achieved an accuracy of 94.55%, outperforming other state-of-the-art unsupervised learning methods.

Several machine learning and deep learning algorithms have been presented to detect the criminal behaviours in a smart grid environment in recent studies because of many successful results. However, most learning algorithms for the electricity theft detection have their pros and cons; hence, a critical research issue nowadays has been how to develop an effective detection algorithm that leverages the strengths of different learning algorithms. To demonstrate the performance of such an integrated detection model, the algorithm proposed first builds on deep neural networks, a meta-learner for determining the weights of detection models for the construction of an ensemble detection algorithm and then uses a promising metaheuristic algorithm named search economics to optimise the hyperparameters of the meta-learner. Experimental results show that the proposed algorithm is able to find better results and outperforms all the other state-of-the-art detection algorithms for electricity theft detection compared in terms of the accuracy, F1-score, area under the curve of precision-recall (AUC-PR), and area under the curve of receiver operating characteristic (AUC-ROC). Since the results show that the meta-learner of the proposed algorithm can improve the accuracy of deep learning algorithms, the authors expect that it will be used in other deep learning-based applications.

Source-specific multicast is a key technology for multicast services such as IPTV broadcasting, which relies on IGMPv3/MLDv2 for source-group membership signalling and multicast routing protocols such as PIM-SSM for building and maintaining receiver-initiated source-based distribution trees across the network. The authors propose the Hard-state Protocol Independent Multicast—Source-Specific Multicast (HPIM-SSM), a novel multicast routing protocol that keeps the design principles of PIM-SSM but overcomes its limitations, such as slow convergence and the possibility of creating suboptimal trees. The state machines of HPIM-SSM were designed to react promptly to all network events susceptible to reconfiguring the multicast trees, avoiding the need for soft-state maintenance through the periodic transmission of control messages. Moreover, the authors eliminated the need for designated routers, which led to suboptimal trees, and introduced a control-driven assert protocol that operates per source, allowing for considerable memory savings. Finally, the protocol enables the coexistence of multiple unicast routing protocols. HPIM-SSM was implemented in Python, and its correctness was extensively validated through model-checking techniques. Furthermore, a comparison between HPIM-SSM and PIM-SSM was conducted, encompassing both theoretical analysis and experimental evaluation of convergence times. The results demonstrate clearly that HPIM-SSM outperforms PIM-SSM, exhibiting significantly faster convergence times and completely avoiding suboptimal trees.

The world of communications technology has recently undergone an extremely significant revolution. This revolution is an immediate consequence of the immersion that the fifth generation B5G and 6G have just brought. The latter responds to the growing need for connectivity and it improves the speeds and qualities of the mobile connection. To improve the energy and spectral efficiency of these types of networks, the non-orthogonal multiple access (NOMA) technique is seen as the key solution that can accommodate more users and dramatically improve spectrum efficiency. The basic idea of NOMA is to achieve multiple access in the power sector and decode the required signal via continuous interference cancelation. A resource allocation approach is proposed for the B5G/6G-NOMA network that aims to maximise system throughput, spectrum and energy efficiency and fairness among users while minimising latency. The proposed approach is based on reinforcement learning (RL) with the use of the Q-Learning algorithm. First, the process of resource allocation as a problem of maximising rewards is formulated. Next, the Q-Learning algorithm is used to design a resource allocation algorithm based on RL. The results of the simulation confirm that the proposed scheme is feasible and efficient.

IEEE 802.1 Time-Sensitive Networking (TSN) has proposed several shapers, for example, time-aware shaper (TAS, 802.1Qbv), asynchronous traffic shaping (ATS, 802.1Qcr), credit-based shaper (CBS, 802.1Qav), and cyclic queuing and forwarding (CQF, 802.1Qch). The shapers have their advantages and disadvantages and can be used in isolation or in combination to address the varied timing requirements of industrial application streams. There is very limited work on how to analyse and configure shaper combinations. The authors are interested in the configuration optimisation of multi-shaper TSN networks, targeting the TAS + CBS, TAS + ATS, and TAS + Multi-CQF combinations. The authors first propose multi-shaper integration approaches, focusing on a novel iterative delay analysis for TAS + ATS, an approach to integrate TAS and CQF by placing constraints on TAS scheduling as well as the TAS and CBS integration. We formulate the combinatorial optimisation problem of configuring multi-shaper TSN networks, which consists, for example, of the routing of streams, the assignment of streams to the egress port queues, and the synthesis of gate control lists for TAS. Then, the authors propose a solution based on a simulated annealing metaheuristic. The proposed solutions are evaluated on large realistic test cases, up to tens of thousands of streams and devices.

Intrusion detection systems built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. The authors used a qualitative method for analysing and evaluating the performance of network intrusion detection system (NIDS) in a systematic way. However, their approach has limitations as it only identifies gaps by analysing and summarising data comparisons without considering quantitative measurements of NIDS's performance. The authors provide a detailed discussion of various deep learning (DL) methods and explain data intrusion networks based on an infrastructure of networks and attack types. The authors’ main contribution is a systematic review that utilises meta-analysis to provide an in-depth analysis of DL and traditional machine learning (ML) in notable recent works. The authors assess validation methodologies and clarify recent trends related to dataset intrusion, detected attacks, and classification tasks to improve traditional ML and DL in NIDS-based publications. Finally, challenges and future developments are discussed to pose new risks and complexities for network security.