IET Networks最新文献

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.

An even faster and more heterogeneous communication infrastructure is planned for the 6G network, based on 5G in a way that leads us to much more deeply connected, programmable, intelligent, and sensing devices, with excellent network performance and coverage, and new dimensions of functionality. Therefore, 6G brings even greater challenges to network/traffic engineering and optimisation.

This virtual collection on Network/Traffic Optimisation towards 6G Network brings together the best six research papers submitted from academia, and reflects some of the latest and original achievements, concentrating on the performance of a mobile hotspot in vehicular communication, on the mobility modelling and ad hoc routing in Flying Ad-hoc NETworks (FANETs), on the performance of a joint antenna and relay selection Multiple-Input Multiple-Output (MIMO) system for cooperative Non-Orthogonal Multiple Access (NOMA) networks, on optimal resource optimisation based on multi-layer monitoring and Machine Learning (ML), on Voice over Wi-Fi Security Threats—Address Resolution Protocol (ARP) attacks and countermeasures—and on the management of 5G and Beyond networks through cloud-native deployments and end-to-end monitoring.

Although the rapid and substantial changes in networking technologies towards the 6G Network over the recent years could readily justify this virtual issue, our real motivation was the 13th event of the International Symposium of Communications Systems, Networks and Digital Signal Processing, held in Porto, Portugal (20–22 July 2022), and the IET's open call.

We begin with the first paper where Marinos Vlasakis et al theoretically analyse the performance of a mobile hotspot with limited bandwidth capacity and a Connection Admission Control functionality which provides Quality of Service (QoS) support for handover voice calls by serving them in priority over new voice calls. An interesting application example of vehicular communication is presented by considering a vehicle (say a bus), which alternates between stop and moving phases. In the stop phase, the vehicle can service both new and handover calls, while in the moving phase, only new calls (originating from the vehicle) are supported. Obviously, when passengers enter the vehicle while talking on their mobile phone, a handover should occur, that is, the Access Point must support handover connections in priority over new call connections. To this end, the capacity of the mobile hotspot is probabilistically reserved during the stop phase to benefit handover calls. In this case, new calls are accepted with a probability. This is called probabilistic bandwidth reservation policy. The system is modelled based on three-dimensional Markov chains. Moreover, the traffic is assumed quasi-random (originating from a finite traffic source population). This consideration is the first for loss/queueing models applied in a mobile hotspot and is proven to be very essential.

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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.