IET Communications最新文献

In today's digital landscape, cybercriminals are constantly evolving their tactics, making it challenging for traditional cybersecurity methods to keep up. To address this issue, this study explores the potential of knowledge graph reasoning as a more adaptable and sophisticated approach to identify and counter network attacks. By leveraging graph structures imbued with human-like thinking, this method enhances the resilience of cybersecurity systems. The study focuses on three critical aspects: data preparation, semantic foundations, and knowledge graph inference techniques. Through an in-depth analysis of these components, the research aims to reveal how knowledge graph reasoning can improve cyberattack detection and enhance the overall efficacy of cybersecurity measures, including intrusion detection systems. The proposed approach has undergone extensive experimentation to validate its effectiveness compared to existing methods. The results of the experiment have shown a remarkable advancement in accuracy, speed, and recall for recognition, surpassing current methods. This achievement is a notable contribution in the realm of managing big data in cybersecurity. The study establishes a foundation for the automation of network attack detection, ultimately enhancing overall network security.

At present, in order to conduct non-destructive testing on the grounding grid of substations under the condition of continuous power supply and no excavation, researchers have applied wireless technology based on electrochemical methods to remotely monitor the corrosion state of grounding conductors online. Nevertheless, wireless signals are affected by the environment when they are transmitted underground. In the field of grounding gird wireless monitoring, how to plan the information transmission path of wireless sensor network (WSN) with high accuracy of data transfer and low energy consumption earns growing research attention. To address the problem of WSN path planning in grounding grid area, a path planning method for WSN based on the hybrid algorithm of map-matching algorithm and double-pole search algorithm (MM-DPS) is proposed in this paper. The map-matching algorithm is employed to calculate the optimal sampling node number of the data transmission path. On the basis of the optimal sampling node number, the double-pole search algorithm is employed in seeking out each sensor node of the path, and two groups of path plans are obtained. In the simulation experiment, compared with the A-star algorithm, the MM-DPS algorithm shortens the data transmission path length by about 39% and reduces the energy consumption by about 57%. The research work brings a method to alleviate the problem of data transmission underground of WSN in grounding grid area. The method not only ensures the accuracy of data transmission, but also shorts the transmission distance and reduces energy consumption.

Opportunistic spectrum access (OSA), a promising technology to resolve radio spectrum scarcity, is still faced with some challenges, of which one is potentially frequent channel handoff (CH) even handoff blocking (HB) for secondary user (SU) to avoid primary user (PU). Reaccess is a direct way to resolve HB. However, multiple handoff blocking-then-reaccess (H-BTR) are time consuming. Whether it is worthwhile to make multiple H-BTR, is an interesting but hardly mentioned issue. To this end, the multiple H-BTR-based OSA are focused on here. Three key indices, the average transmission probability, handoff delay, and average reaccess times of SU in the H-BTR-based OSA, are deduced as close form first. Then, the effects of H-BTR frequency and service traffic rate of SU on OSA are discussed. Finally, the H-BTR-based OSA is compared to two other schemes, the handoff without BTR (HWBTR)-based OSA and the stop-and-waiting (SW)-based OSA. Theoretical and simulated results show that the H-BTR-based OSA performs best among three schemes. Making H-BTR in moderate frequency according to service traffic rate can increase transmission opportunity while additional delay of SU is tolerable, thus the performance of OSA can be well improved.

Sybil attacks are a very serious challenge in social networks including, the Social Internet of Things (SIoT). This paper introduces the SybilPSIoT method, in which a hybrid prevention and detection decentralized approach is proposed in SIoT based on smart contracts. The owner adds his objects to the smart contract. However, hostile owners can create Sybil things. This paper formally presents a model that uses a signed SIoT network with objects and identifiers as network nodes and information about the type of nodes (acknowledgers). Assuming the relationship between the edge marks between nodes and the node type, the proposed method uses trust paths between verification and desired nodes using a Bayesian inference model and structural balance patterns to judge the target node in these paths. It also uses game theory to control access owners to prevent Sybil from creating new things based on a cost-benefit function. Based on the analysis method, a validating effect proportional to the path length on the target object was presented. This method was compared with the most novel available methods; the results from this comparison depict the scalability and effectiveness of the proposed method for large networks.

Today, elastic optical networks (EONs) are required to accommodate traffic with different service priorities. For example, mission-critical applications such as industrial internet, smart grids, and remote surgery require an ultra-reliable low-latency communication system. The novel impairment-aware resource allocation scheme proposed here prioritises traffic. It satisfies the quality and latency requirements of mission-critical traffic while causing minimum disruptions to other forms of low-priority traffic connections. The results obtained for 6-node and 14-node networks under various traffic distribution environments indicate that the proposed algorithm achieves higher spectral efficiency, reduces spectrum fragmentation, and causes minimal disruptions over the benchmark algorithm.

With the continuous evolution and in-depth integration between wireless communication and emerging technology such as internet of things (IoT), artificial intelligence (AI) etc., wireless terminals are growing exponentially, thus bringing great challenges to available spectrum resources. The contradiction between unlimited frequency needs and limited spectrum resources has become a bottleneck restricting the development of wireless communication technology. As an efficient way to improve spectrum efficiency, cognitive radio (CR) continues to be the focus of wireless communication within decades. To conduct CR, the main procedure is the discovery of available spectral holes by periodically monitoring the target authorized band, namely spectrum sensing (SS). Energy detector (ED) is widely accepted for SS due to its low complexity and high convenience. The essence of traditional ED based SS schemes consist in the adaptive variation of sensing threshold/sampling point with environmental signal-to-noise ratio (SNR) at the receiver of CR terminal, namely adaptive sensing threshold/sampling point based SS. However, the performance of both adaptive sensing threshold and adaptive sampling point based SS schemes are always at the expense of computation complexity due to the excessive sampling point. In addition, these two schemes are both about the optimization issue of a single variable under constraints. Actually, both detection probability and false alarm probability of ED are a two-dimensional function of sensing threshold and sampling point for a given SNR. The optimal solution of sensing performance can not be obtained by optimizing sensing threshold or sampling point alone. Motivated by these, the joint optimization of sampling point and sensing threshold is considered for SS in this paper, where sampling point and sensing threshold are jointly adaptive with the variation of environmental SNR. In addition, Q-learning is considered in this paper to obtain the sub-optimal solution due to the non-convexity of the considered optimization problem. Finally, the simulation experiments are made and the results validate the effectiveness of the proposed scheme.

Recently, there have been numerous studies exploring the field of molecular communication (MC) systems. However, due to the high cost and limited availability of advanced micro/nano-scale equipment, most of these works remain purely theoretical, with only a few being examined through experimental platforms. Additionally, the absence of a suitable model for flow-assisted MC-based systems poses another significant challenge. This research focuses on the potential applications of MC technology within the human body. To address the limitations mentioned above, a closed-loop experimental platform based on the human circulatory system is proposed. This platform offers a cost-effective and accessible solution for studying MC systems. The implementation process involves a brief discussion about the circulatory system model. By varying flow rates and the quantity of released information particles, channel impulse responses are obtained. Based on the observed experimental data, the authors have successfully developed a new theoretical model that accurately fits the experimental data. The model demonstrates a strong level of agreement with the observed results. This model demonstrates its suitability for flow-assisted MC-based systems.

Intelligent reflective surface (IRS) provides an effective solution for reconfiguring air-to-ground wireless channels, and intelligent agents based on reinforcement learning can dynamically adjust the reflection coefficient of IRS to adapt to changing channels. However, most exiting IRS configuration schemes based on reinforcement learning require long training time and are difficult to be industrially deployed. This paper, proposes a model-free IRS control scheme based on reinforcement learning and adopts transfer learning to accelerate the training process. A knowledge base of the source tasks has been constructed for transfer learning, allowing accumulation of experience from different source tasks. To mitigate potential negative effects of transfer learning, quantitative analysis of task similarity through unmanned aerial vehicle (UAV) flight path is conducted. After identifying the most similar source task to the target task, parameters of the source task model are used as the initial values for the target task model to accelerate the convergence process of reinforcement learning. Simulation results demonstrate that the proposed method can increase the convergence speed of the traditional DDQN algorithm by up to 60%.