IET Networks最新文献

Supervisory control and data acquisition (SCADA) systems are vital in monitoring and controlling industrial processes through the web. However, while such systems result in lower costs, greater utilisation efficiency, and improved reliability, they are vulnerable to cyberattacks, with consequences ranging from the inconvenience and minor disruption to severe physical damage and even loss of life. The authors evaluate the security of the Dam system in the form of Common Criteria, develop safety goals to improve this safety, and focus on threats and risks to the dam SCADA system. Finally proposes an anomaly-based machine-learning framework for detecting malicious network attacks in the SCADA system of a dam. Three unsupervised classification algorithms are considered: hierarchical clustering, local outlier factor, and isolation forest. It is shown that the hierarchical clustering algorithm achieves the highest precision and F-score of the three algorithms. Overall, the results confirm the effectiveness of anomaly-based detection algorithms in enhancing the robustness of SCADA systems toward malicious attacks. At the same time, it complies with the security objectives of Common Criteria, achieving the safety and protection of the dam.

The efficient development of Mobile Wireless Sensor Networks (MWSNs) relies heavily on optimizing two key parameters: Throughput and Energy Consumption. The proposed work investigates network connectivity issues with MWSN and proposes two routing algorithms, namely Self-Organising Maps based-Optimised Link State Routing (SOM-OLSR) and Deep Reinforcement Learning based-Optimised Link State Routing (DRL-OLSR) for MWSNs. The primary objective of the proposed algorithms is to achieve energy-efficient routing while maximizing throughput. The proposed algorithms are evaluated through simulations by considering various performance metrics, including connection probability (CP), end-to-end delay, overhead, network throughput, and energy consumption. The simulation analysis is discussed under three scenarios. The first scenario undertakes ‘no optimisation’, the second considers SOM-OLSR, and the third undertakes DRL-OLSR. A comparison between DRL-OLSR and SOM-OLSR reveals that the former surpasses the latter in terms of low latency and prolonged network lifetime. Specifically, DRL-OLSR demonstrates a 47% increase in throughput, a 67% reduction in energy consumption, and a CP three times higher than SOM-OLSR. Furthermore, when contrasted with the ‘no optimisation’ scenario, DRL-OLSR achieves a remarkable 69.7% higher throughput and nearly 89% lower energy consumption. These findings highlight the effectiveness of the DRL-OLSR approach in wireless sensor networks.

Natural and artificial (human-made) disasters have been steadily increasing all over the world, signifying the importance of providing reliable and energy friendly communication network to survivors in the aftermath of a disaster. On the other hand, low-battery devices running optimised link state routing (OLSR) protocol often experience quick power failure which restricts their ability to communicate for a necessary period during rescue operations. To extend the lifespans and prioritise message delivery on low-battery devices, the authors examine disaster scenario optimised link state routing (DS-OLSR) protocol ALERT message and propose an innovative solution to prioritise messages based on the device battery energy level, leading to more energy conservation, packet delivery as well as better emotional state of survivors. An ALERT message is a novel message type added to mobile ad-hoc network's (MANET) popular OLSR protocol for energy efficiency. The proposed DS-OLSR Protocol and Message Prioritisation (DS-OLSRMP) as an extension of DS-OLSR modifies the multipoint relay mechanism and uses a prioritisation technique which classify nodes into four priority groups: Critical, High, Medium, and Low priorities. These priority groups help in prioritising both message delivery and message status notifications for devices with low battery energy. The DS-OLSRMP was implemented in a Network Simulator, version 3.29 and compared with DS-OLSR, OLSRv1 and OLSRv2. The simulation results show that DS-OLSRMP performs better than DS-OLSR, OLSRv1 and OLSRv2 in terms of energy conservation and packets delivery in the simulation of both sparse and dense network scenarios.

Cross-site scripting (XSS) attack has been one of the most dangerous attacks in cyberspace security. Traditional methods essentially discover XSS attack by detecting malicious payloads in requests, which is unable to distinguish attacking attempts with the attacking reality. The authors collect responses from a web server and train a bagging-based PU learning model to determine whether the XSS vulnerability is truly triggered. To validate the authors’ proposed framework, experiments are performed on 5 popular web applications with 11 specified CVE recorded vulnerabilities and 32 vulnerable inputs. Results show that the authors’ approach outperforms existing research studies, effectively identifies the attacking reality from attacking attempts, and meanwhile reduces the number of worthless security alarms.

Industrial IoT (IIoT) applications are widely used in multiple use cases to automate the industrial environment. Industry 4.0 presents challenges in numerous areas, including heterogeneous data, efficient data sensing and collection, real-time data processing, and higher request arrival rates, due to the massive amount of industrial data. Building a time-sensitive network that supports the voluminous and dynamic IoT traffic from heterogeneous applications is complex. Therefore, the authors provide insights into the challenges of industrial networks and propose a strategy for enhanced traffic management. An efficient multivariate forecasting model that adapts the Multivariate Singular Spectrum Analysis is employed for an SDN-based IIoT network. The proposed method considers multiple traffic flow parameters, such as packet sent and received, flow bytes sent and received, source rate, round trip time, jitter, packet arrival rate and flow duration to predict future flows. The experimental results show that the proposed method can effectively predict by contemplating every possible variation in the observed samples and predict average load, delay, inter-packet arrival rate and source sending rate with improved accuracy. The forecast results shows reduced error estimation when compared with existing methods with Mean Absolute Percentage Error of 1.64%, Mean Squared Error of 11.99, Root Mean Squared Error of 3.46 and Mean Absolute Error of 2.63.

Heterogeneous networks (HetNets) refer to the communication network, consisting of different types of nodes connected through communication networks deploying diverse radio access technologies like LTE, Wi-Fi, Zigbee, and Z-wave, and using different communication protocols and operating frequencies. Vertical handover, is the process of switching a mobile device from one network type to another, such as from a cellular network to a Wi-Fi network, and is critical for ensuring a seamless user experience and optimal network performance, within the handover process handover triggering estimation is one of the crucial step affecting the overall performance. A mathematical analysis is presented for the handover triggering estimation. The performance evaluation shows significant improvement in the probability of successful handover using the proposed handover triggering condition based on speed, distance, and different mobility models. The handover triggering condition is optimised based on the speed of the mobile node, handover completion time, and the coverage range of the current and the target networks of the HetNet node, with due consideration of the mobility model.

One of the significant challenges of the Radio over fibre (RoF) Medium Access Control (MAC) protocol is the propagation delay. This delay can lead to serious issues, such as higher propagation delay resulting in collisions and unnecessary retransmissions. Quantum entanglement is an excellent candidate to overcome the propagation delay of the RoF MAC protocol. A new quantum MAC protocol is proposed, named the Quantum Entanglement-based MAC protocol (QE-MAC), in which Quantum Teleportation is utilised to address the propagation delay. Four entanglement states are employed to represent the control packets of the classical MAC protocol, and data is transmitted over the classical channel. Instead of using control packets such as acknowledgement, request to send, and Clear to send, state transitions are employed. This approach avoids the delay and collision issues associated with control packets, resulting in a significant improvement in network performance. The delay, duty cycle (DC), and power consumption of the proposed QE-MAC protocol are formulated and derived. The protocol is evaluated in terms of delay, DC, and power consumption, demonstrating superior performance compared to the classical RoF MAC protocol. In comparison to published works, our proposed approach has successfully reduced both delay and power consumption by 35%.

Concurrent multipath transfer (CMT) is a transport layer protocol used for transferring data over multiple paths concurrently. In the case of asymmetric paths, the dissimilarity of delay, bandwidth, and loss rate among paths leads to challenges such as out-of-order packets, receiver buffer blocking, and throughput reduction. A framework that uses the end-to-end delay of paths to distribute the packets over asymmetric paths in ordering policy is proposed. The proposed framework detects network congestion in the assigned path and reduces the congestion window. Furthermore, the proposed framework predicts receiver buffer blocking and deactivates the highest delay path that causes this problem. The simulation results show that the proposed framework achieves highest throughput, lower entropy and lower average application end-to-end delay than the previous algorithms.

Solar cells are increasingly being utilised for both energy harvesting and reception in free-space optical (FSO) communication networks. The authors focus on the implementation of a mid-band p-In_0.01Ga_0.99 N/p-In_0.5Ga_0.5 N/n-In_0.5Ga_0.5 N (PPN) solar cell, boasting an impressive 26.36% conversion efficiency (under 1.5AM conditions) as a receiver within an indoor FSO communication network. Employing a solar cell with dimensions of 1 mm in length and width, the FSO system underwent simulation using Optisystm software, while the solar cell's behaviour was simulated using SCAPS-1D. The received power from the solar cell was then compared to that of four commercially available avalanche photodiode (APD) receivers. Exploring incident wavelengths spanning 400–700 nm within the visible spectrum, across transmission distances of 5, 10, 15, and 20 m, the study presented current-voltage (IV) and power-voltage curves. Notably, the InGaN solar cell exhibited superior electrical power output compared to all commercial APDs. In conclusion, the findings underscore that augmenting received power has the potential to enhance FSO network quality and support extended transmission distances.