Transactions on Emerging Telecommunications Technologies最新文献

The number of attacks against Industrial Internet of Things (IIoT) devices has increased over the past years, particularly on widely used communication protocols like Message Queuing Telemetry Transfer (MQTT). The fast increase in IIoT applications brings both critical challenges and technical gaps in cybersecurity. On the other hand, traditional cyber-attack detection approaches scrap to address and support the run-time responsibilities of IIoT environments. This study presents a hybrid Genetic Algorithm and Random Forest (GA_RF) method for detecting cyber-attacks in Industrial Control Machines (ICS) that use MQTT protocol in the IIoT environment. This architecture integrates ICS with edge devices and cloud servers, using a GA_RF algorithm to detect anomalies in data collected by sensors. Normal data is processed locally and then sent to the cloud for storage and return, ensuring continuous monitoring and security. Also, the MQTT-IOT-IDS2020 dataset as a real test case was applied for prediction of the proposed GA_RF method with compare to some other powerful machine and deep learning models. The experimental results show that the proposed GA_RF method has an optimum accuracy of 99.87%–100% for detecting cyber-attacks. This hybrid algorithm also achieved 0–0.0015 in Mean Absolute Error (MAE) and 100% in Precision, Recall, and F-score factors. This result led to the proposed architecture, which connects the ICS to a server while running GA_RF on the IIoT environment. In conclusion, this study indicates the effectiveness of GA_RF and aims to improve security by using the MQTT protocol in IIoT.

The communications industry has a new, forward-thinking architecture: software-defined networks (SDN). The inflexible structure of conventional networks severely hinders their ability to service modern organizations. The increased adaptability and transparency of SDN-powered networks makes them more vulnerable to security hazards like Distributed denial of service (DDoS) assaults. The switch information system introduces protection at the cost of some degree of adaptability. The information and software layers are potential entry points for DDoS assaults. The flow-based policies frequently collide at the application layer, giving rise to safety issues. The central nervous system of the network's components is the SDN controller located at the control layer. The centrally located controller interacts with networking hardware via switches. Attacks on control devices, switches, and communication channels can cause vulnerabilities in SDN networks. The study proposes Blockchain-SDN-based switch information security (BC-SDN-SIS) to address the information security problems. SDN applications operate at the highest level of SDN controllers, facilitating the easy rollout of new network services. The suggested design uses a distributed BC approach to guarantee confidentiality, anonymity, privacy, and scalability. The proposed BC-SDN-SIS model increases the accuracy ratio of 97.45%, precision ratio of 98.43, recall ratio of 97.34, effectiveness ratio of 97.6%, bandwidth of 2.5 Mbps, latency rate of .04 ms and F1-score ratio of 98.28% compared to other existing models.

Cyber-Physical Systems (CPSs) provide critical infrastructure for the betterment of human lives thereby integrating cyber and physical components but the fusion of physical and digital components leads to an increase in the attack surface, which in turn provides opportunities for the attackers to intrude on these systems, which can affect the critical services like health care, water treatment facility, the electrical grid, hydropower plant, and so forth. The existing intrusion detection systems (IDSs) in CPSs are facing issues like poor detection accuracy, high false alarm rate and more computation time. Also, existing intrusion detection systems cannot identify new attacks that is, zero-day assaults. Prerequisite exists for the design of the framework for detecting intrusions using artificial intelligence inspired approaches grounded on the principle of ensemble techniques. Intrusion detection framework assimilating ensemble-based technique for CPS has been proposed. Since the proposed framework incorporates privileges of multiple techniques for intrusion detection and classification, hence the proposed framework may overcome the limitations of existing IDSs for CPS. Overview of cutting-edge incursion identification methods for CPSs is presented. Several characteristics of CPS, industrial protocols, and anomaly detection techniques for intrusion detection are analyzed. Taxonomy of IDS for CPS has been proposed, taxonomy of attacks and threats on CPS has been intended. Research challenges for IDS in CPS are also uncovered from this review.

Jiang H, Xia H, Zare M. Dynamic service function chain placement in mobile computing: an asynchronous advantage actor-critic based approach. Trans Emerging Tel Tech. 2024;35(8):e5022. doi: 10.1002/ett.5022

Due to unforeseen circumstances, there have been changes in the affiliations of the authors associated with this paper. The correct affiliations for each author are as follows:

Heling Jiang

School of Information Management, Central China Normal University, Wuhan, Hubei 430079, China

Information Department, GuiZhou University of Finance & Economic, Guiyang 550025, China

Hai Xia

Gui'an New Area Science and Innovation Industry Development Co., Ltd, Guiyang, Guizhou 550025, China

Mansoureh Zare

Department of Computer Engineering, Bushehr Branch, Islamic Azad University, Bushehr 75168, Iran

This error was on the part of the corresponding author, and we apologize for any inconvenience this may have caused.

Khattak MI, Yuan H, Ahmad A, Ahmed M, Khan A, Inamullah. PAM: Predictive analytics and modules-based computation offloading framework using greedy heuristics and 5G NR-V2X. Trans Emerging Tel Tech. 2024;35(7):e5003. https://doi.org/10.1002/ett.5003

The second affiliation of the third author was not included in the final published version. The affiliation is below.

“Department of Electrical and Computer Engineering, COMSATS University Islamabad, Wah Campus, Pakistan.”

We apologize for this error.

Millimeter waves (mmWave) present an enticing opportunity for wireless communication due to their substantial bandwidth. However, mitigating signal losses within this spectrum necessitates employing numerous antennas for transmission and reception. In practical scenarios, dedicating individual RF chains to each antenna is often unfeasible. In response to this limitation, our research investigates a hybrid beamforming approach, seeking to optimize spectral efficiency through an alternating optimization (AO) technique. Our goal is to develop an algorithm that can be easily integrated into diverse hybrid beamforming configurations. On the other hand the pursuit of optimizing spectral efficiency while adhering to the constraints imposed by phase shifters results in a non-convex problem. To confront this challenge, we employ a gradient descent framework combined with projection methods. We introduce the gradient prediction method (GPM), which leads to a closed-form solution for projection. Simulations underscore that this hybrid beamforming structure can achieve the performance of a fully digital beamforming method when the number of RF chains is twice the total number of data streams, regardless of the number of antennas involved. Furthermore, we will conduct a comparative performance analysis of our proposed algorithm against other established benchmark algorithms to ascertain its superiority.

The Internet of Things (IoT) plays a crucial role in enhancing technology by facilitating data transfer, storage, control, and management across networks. Secure communication within IoT environments remains a significant challenge. This research aims to enhance IoT security by integrating Message Queuing Telemetry Transport (MQTT) and MQTT-Sensor Network (MQTT-SN) protocols with the Dual Mutation-Based Seagull Optimization Algorithm (DMBSOA). MQTT, known for its lightweight and dependable messaging, is widely used in the IoT community but is vulnerable to cyber-attacks, particularly concerning privacy and authentication. DMBSOA, inspired by seagulls' foraging behavior, optimizes MQTT settings to enhance security, reliability, and performance. The proposed model dynamically adjusts key parameters such as transmission frequency, Quality of service (QoS) levels, and message size to improve energy consumption, throughput, end-to-end delay, and packet delivery ratio. A comprehensive system model is presented, comprising publisher, subscriber, and broker nodes, with security mechanisms integrated into the broker to ensure data integrity, authentication, and encryption. MQTT operates over Transmission Control Protocol/Internet Protocol (TCP/IP), while MQTT-SN uses User Datagram Protocol (UDP), catering to resource-constrained devices and low-power modes. The proposed technique attained throughput (68 kB), energy consumption (120 mJ), security level (96%), energy efficiency (98.80%), path loss (22 dB), end-to-end delay (35 ms), processing time (230 s) and packet delivery ratio (0.98). The DMBSOA-optimized MQTT protocol demonstrates superior performance compared to these models, highlighting its potential to meet the evolving demands of IoT security. This research underscores the effectiveness of DMBSOA in enhancing MQTT protocol security and efficiency, providing a promising solution for secure IoT communication.

With the rapid growth of internet of thing (IoT) devices, cooperative spectrum sensing (CSS) has emerged as a promising solution to leverage the spatial diversity of multiple secondary IoT sensing nodes (SNs) for spectrum availability. However, the cooperative paradigm also incurs increased cooperative costs between each SN and the fusion center (FC), leading to decreased cooperative efficiency and achievable throughput, especially in large-scale cognitive IoT (CIoT). To address these challenges, we present a sequential detection with feedback information (SD-FI) approach in this paper. To achieve this objective, we propose a two-way CSS model that formulates an optimization problem of Bayes cost in a quickest detection framework with feedback. To solve this optimization problem, we derive the structure of the optimal local decision rule from the local decision function and determine the optimal detection threshold in conjunction with the cost function. Following the optimal threshold pair, we implement the optimal SD-FI and theoretically demonstrate the uniqueness of the optimal threshold and optimal sensing time. Simulation results demonstrate superiority of SD-FI in terms of cooperative performance (i.e., detection performance and Bayes cost) and sample size. Notably, even with limited sensing time, our proposed SD-FI exhibits high throughput, highlighting its effectiveness in enhancing spectrum availability and utilization in CIoT.

In the cognitive IoT spectrum sharing process, the complex interference environment leads to a low spectral efficiency of the licensed spectrum. To solve this problem, this article allows more cognitive users (CU) to access the licensed spectrum while ensuring reliable transmission of primary users (PU) by introducing the advanced interference steering (IS) technique. Traditional IS methods are only suitable for single-user and single-data-stream scenarios. Meanwhile, these methods have many drawbacks in multi-user and multi-data-stream scenarios. To overcome these drawbacks, two IS algorithms based on time division (TD) multiple access are proposed in this article, which are TD based equivalent interference sub-channel interference steering (TD-EI-SCIS) and TD-based sub-channel interference steering (TD-SCIS), respectively. Furthermore, considering the fact that PU has the highest communication quality priority in cognitive IoT, a joint interference management (JIM) scheme based on TD-EI-SCIS, TD-SCIS, and partial interference alignment (PIA) is proposed to protect the PU. Simulation results show that the spectral efficiency (SE) of the PU in the proposed JIM scheme is not significantly improved compared with the proposed TD-EI-SCIS in the low SNR region. However, from the perspective of the total SE in the whole cognitive IoT system, TD-EI-SCIS is superior to the JIM about 3 to 4 bit/s/Hz on average. In the high SNR region, the JIM scheme significantly outperforms TD-EI-SCIS not only in terms of the SE of PU about 10 bit/s/Hz but also in the aspect of the SE in the total network about 7 bit/s/Hz on average.

Internet of Things (IoT) devices are constantly sending data to the cloud. The resource-rich cloud computing paradigm provides users with significant potential to reduce costs and improve quality of service (QoS). However, the centralized architecture of cloud data centers and thousands of miles away from clients has reduced the efficiency of this paradigm in delay-sensitive and real-time applications. In order to get over these restrictions, fog computing was integrated into cloud computing as a new paradigm. Without using the cloud, fog computing can supply the resources needed for IoT devices at the network's edge. Delay is thereby decreased because processing, analysis, and storage are located closer to the clients and the areas where the data is created. In Mobile Edge Computing (MEC) networks, this study sets up an architecture based on Deep Reinforcement Learning (DRL) to deliver online services to end users. We introduce a DRL-based method named DPPR for Dynamic service function chain (SFC) Placement that uses Parallelized virtual network functions (VNFs) and seeks to optimize the long-term expected cumulative Reward. Online service provider DPPR can accomplish processing acceleration through parallel VNF sharing. In addition, by extracting the distribution of initialized VNFs, DPPR improves the capacity to handle subsequent requests. The conducted simulations demonstrate the efficacy of the proposed method, so that the average number of accepted requests is improved by about 11.7%.