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Secure beamforming and deployment design for rate-splitting multiple access-based UAV communications

Science China(Information Sciences) 2025年第1期68卷 342-356页

作者： Bing WANG Xiaofeng TAO Shujun HAN Huici WU Kai YANG Zhu HAN National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Peng Cheng Laboratory Department of Electrical and Computer Engineering University of Houston

Rate-splitting multiple access(RSMA) has recently gained attention as a potential robust multiple access(MA)scheme for upcoming wireless networks. Given its ability to efficiently utilize wireless resources and design interference management strategies, it can be applied to unmanned aerial vehicle(UAV) networks to provide convenient services for large-scale access ground users. However, due to the line-of-sight(LoS) broadcast nature of UAV transmission, information is susceptible to eavesdropping in RSMA-based UAV networks. Moreover, the superposition of signals at the receiver in such networks becomes complicated. To cope with the challenge, we propose a two-user multi-input single-output(MISO) RSMA-based UAV secure transmission framework in downlink communication networks. In a passive eavesdropping scenario, our goal is to maximize the sum secrecy rate by optimizing the transmit beamforming and deployment location of the UAV-base station(UAV-BS),while considering quality-of-service(QoS) constraints, maximum transmit power, and flight space limitations. To address the non-convexity of the proposed problem, the optimization problem is first decoupled into two subproblems. Then, the successive convex approximation(SCA) method is employed to solve each subproblem using different propositions. In addition, an alternating optimization(AO)-based location RSMA(L-RSMA) beamforming algorithm is developed to implement joint optimization to obtain the suboptimal solution. Numerical results demonstrate that(1) the proposed L-RSMA scheme yields a28.97% higher sum secrecy rate than the baseline L-space division multiple access(SDMA) scheme;(2) the proposed L-RSMA scheme improves the security performance by 42.61% compared to the L-non-orthogonal multiple access(NOMA) scheme.

关键词： rate-splitting multiple access UAV communication precoding optimization deployment location physical layer security

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Entropy-Bottleneck-Based Privacy Protection Mechanism for Semantic Communication

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computers, Materials & Continua 2025年第5期83卷 2971-2988页

作者： Kaiyang Han Xiaoqiang Jia Yangfei Lin Tsutomu Yoshinaga Yalong Li Jiale Wu School of Information Engineering Inner Mongolia University of TechnologyHohhot010051China Department of Computer and Network Engineering University of Electro-CommunicationsChofuTokyo1828585Japan

With the rapid development of artificial intelligence and the Internet of Things,along with the growing demand for privacy-preserving transmission,the need for efficient and secure communication systems has become increasingly *** communication methods transmit data at the bit level without considering its semantic significance,leading to redundant transmission overhead and reduced *** communication addresses this issue by extracting and transmitting only the mostmeaningful semantic information,thereby improving bandwidth ***,despite reducing the volume of data,it remains vulnerable to privacy risks,as semantic features may still expose sensitive *** address this,we propose an entropy-bottleneck-based privacy protection mechanism for semantic *** approach uses semantic segmentation to partition images into regions of interest(ROI)and regions of non-interest(RONI)based on the receiver’s needs,enabling differentiated semantic *** focusing transmission on ROIs,bandwidth usage is optimized,and non-essential data is *** entropy bottleneck model probabilistically encodes the semantic information into a compact bit stream,reducing correlation between the transmitted content and the original data,thus enhancing privacy *** proposed framework is systematically evaluated in terms of compression efficiency,semantic fidelity,and privacy *** comparative experiments with traditional and state-of-the-art methods,we demonstrate that the approach significantly reduces data transmission,maintains the quality of semantically important regions,and ensures robust privacy protection.

关键词： Semantic communication privacy protection semantic segmentation entropy-based compression

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Blockchain-based knowledge-aware semantic communications for remote driving image transmission

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Digital Communications and networks 2025年第2期11卷 317-325页

作者： Yangfei Lin Tutomu Murase Yusheng Ji Wugedele Bao Lei Zhong Jie Li Department of Computer and Network Engineering University of Electro-Communications Information Technology Center Nagoya University National Institute of Informatics(NII) Hohhot Minzu College Toyota Motor Corporation Department of Computer Science and Engineering Shanghai Jiaotong University

Remote driving, an emergent technology enabling remote operations of vehicles, presents a significant challenge in transmitting large volumes of image data to a central server. This requirement outpaces the capacity of traditional communication methods. To tackle this, we propose a novel framework using semantic communications, through a region of interest semantic segmentation method, to reduce the communication costs by transmitting meaningful semantic information rather than bit-wise data. To solve the knowledge base inconsistencies inherent in semantic communications, we introduce a blockchain-based edge-assisted system for managing diverse and geographically varied semantic segmentation knowledge bases. This system not only ensures the security of data through the tamper-resistant nature of blockchain but also leverages edge computing for efficient management. Additionally, the implementation of blockchain sharding handles differentiated knowledge bases for various tasks, thus boosting overall blockchain efficiency. Experimental results show a great reduction in latency by sharding and an increase in model accuracy, confirming our framework's effectiveness.

关键词： Semantic communication Remote driving Semantic segmentation Blockchain Knowledge base management

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FSCIL-EACA: Few-Shot Class-Incremental Learning network Based on Embedding Augmentation and Classifier Adaptation for Image Classification

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Chinese Journal of Electronics 2024年第1期33卷 139-152页

作者： Ruru ZHANG Haihong E Meina SONG School of Computer Science Beijing University of Posts and Telecommunications Education Department Information Network Engineering Research Center Beijing University of Posts and Telecommunications

The ability to learn incrementally is critical to the long-term operation of AI systems. Benefiting from the power of few-shot class-incremental learning(FSCIL), deep learning models can continuously recognize new classes with only a few samples. The difficulty is that limited instances of new classes will lead to overfitting and exacerbate the catastrophic forgetting of the old classes. Most previous works alleviate the above problems by imposing strong constraints on the model structure or parameters, but ignoring embedding network transferability and classifier adaptation(CA), failing to guarantee the efficient utilization of visual features and establishing relationships between old and new classes. In this paper, we propose a simple and novel approach from two perspectives: embedding bias and classifier bias. The method learns an embedding augmented(EA) network with cross-class transfer and class-specific discriminative abilities based on self-supervised learning and modulated attention to alleviate embedding bias. Based on the adaptive incremental classifier learning scheme to realize incremental learning capability,guiding the adaptive update of prototypes and feature embeddings to alleviate classifier bias. We conduct extensive experiments on two popular natural image datasets and two medical datasets. The experiments show that our method is significantly better than the baseline and achieves state-of-the-art results.

关键词： Few-shot class-incremental learning Embedding augmentation Classifier adaptation Image classification

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A Novel Eccentric Intrusion Detection Model Based on Recurrent Neural networks with Leveraging LSTM

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computers, Materials & Continua 2024年第3期78卷 3089-3127页

作者： Navaneetha Krishnan Muthunambu Senthil Prabakaran Balasubramanian Prabhu Kavin Kishore Senthil Siruvangur Kavitha Chinnadurai Jehad Ali Department of Computer Applications Karpagam College of EngineeringCoimbatore641032India Department of Computer Science and Engineering Karpagam College of EngineeringCoimbatore641032India Department of Data Science and Business Systems College of Engineering and TechnologySRM Institute of Science and TechnologyKattankulathur603203India Department of Computer Science and Engineering Sathyabama Institute of Science and TechnologyJeppiaar NagarRajiv Gandhi SalaiChennai600119India Department of AI Convergence Network Ajou UniversitySuwon16499Korea

The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the ***,this development has expanded the potential targets that hackers might *** adequate safeguards,data transmitted on the internet is significantly more susceptible to unauthorized access,theft,or *** identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious *** research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural networks(RNN)integrated with Long Short-Term Memory(LSTM)*** proposed model can identify various types of cyberattacks,including conventional and distinctive *** networks,a specific kind of feedforward neural networks,possess an intrinsic memory *** Neural networks(RNNs)incorporating Long Short-Term Memory(LSTM)mechanisms have demonstrated greater capabilities in retaining and utilizing data dependencies over extended *** such as data types,training duration,accuracy,number of false positives,and number of false negatives are among the parameters employed to assess the effectiveness of these models in identifying both common and unusual *** are utilised in conjunction with LSTM to support human analysts in identifying possible intrusion events,hence enhancing their decision-making capabilities.A potential solution to address the limitations of Shallow learning is the introduction of the Eccentric Intrusion Detection *** model utilises Recurrent Neural networks,specifically exploiting LSTM *** proposed model achieves detection accuracy(99.5%),generalisation(99%),and false-positive rate(0.72%),the parameters findings reveal that it is superior to state-of-the-art techniques.

关键词： Cybersecurity intrusion detection machine learning leveraging long short-term memory(LLSTM) CICIDS2019 dataset innovative cyberattacks

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An Improved Reptile Search Algorithm Based on Cauchy Mutation for Intrusion Detection

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computer Systems Science & engineering 2023年第8期46卷 2509-2525页

作者： Salahahaldeen Duraibi Department of Computer and Network Engineering Jazan UniversityJazan 82822-6649Saudi Arabia

With the growth of the discipline of digital communication,the topic has acquiredmore attention in the *** Intrusion Detection(ID)system monitors network traffic to detect malicious *** paper introduces a novel Feature Selection(FS)approach for *** Search Algorithm(RSA)—is a new optimization algorithm;in this method,each agent searches a new region according to the position of the host,which makes the algorithm suffers from getting stuck in local optima and a slow convergence *** overcome these problems,this study introduces an improved RSA approach by integrating Cauchy Mutation(CM)into the RSA’s ***,the CM can effectively expand search space and enhance the performance of the *** developed RSA-CM is assessed on five publicly available ID datasets:KDD-CUP99,NSL-KDD,UNSW-NB15,CIC-IDS2017,and CIC-IDS2018 and two engineering *** RSA-CM is compared with the original RSA,and three other state-of-the-art FS methods,namely particle swarm optimization,grey wolf optimization,and multi-verse optimizer,and quantitatively is evaluated using fitness value,the number of selected optimum features,accuracy,precision,recall,and F1-score *** results reveal that the developed RSA-CMgot better results than the other competitive methods applied for FS on the ID datasets and the examined engineering ***,the Friedman test results confirm that RSA-CMhas a significant superiority compared to other methods as an FS method for ID.

关键词： Feature selection intrusion detection metaheuristic algorithms reptile search algorithm cauchy mutation

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An Intelligent Admission Control Scheme for Dynamic Slice Handover Policy in 5G network Slicing

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computers, Materials & Continua 2023年第5期75卷 4611-4631页

作者： Ratih Hikmah Puspita Jehad Ali Byeong-hee Roh Department of AI Convergence Network Ajou UniversitySuwon16499Korea Department of Computer Engineering and Department of AI Convergence NetworkAjou UniversitySuwon16499Korea

5G use cases,for example enhanced mobile broadband(eMBB),massive machine-type communications(mMTC),and an ultra-reliable low latency communication(URLLC),need a network architecture capable of sustaining stringent latency and bandwidth requirements;thus,it should be extremely flexible and *** enables service providers to develop various network slice *** users travel from one coverage region to another area,the callmust be routed to a slice thatmeets the same or different *** research aims to develop and evaluate an algorithm to make handover decisions appearing in 5G sliced *** of thumb which indicates the accuracy regarding the training data classification schemes within machine learning should be considered for validation and selection of the appropriate machine learning ***,this study discusses the network model’s design and implementation of self-optimization Fuzzy Qlearning of the decision-making algorithm for slice *** algorithm’s performance is assessed by means of connection-level metrics considering the Quality of Service(QoS),specifically the probability of the new call to be blocked and the probability of a handoff call being ***,within the network model,the call admission control(AC)method is modeled by leveraging supervised learning algorithm as prior knowledge of additional ***,to mitigate high complexity,the integration of fuzzy logic as well as Fuzzy Q-Learning is used to discretize state and the corresponding action *** results generated from our proposal surpass the traditional methods without the use of supervised learning and fuzzy-Q learning.

关键词： 5g network slice fuzzy q-Learning slice handover

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Effective Controller Placement in Software-Defined Internet-of-Things Leveraging Deep Q-Learning (DQL)

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computers, Materials & Continua 2024年第12期81卷 4015-4032页

作者： Jehad Ali Mohammed J.F.Alenazi Department of AI Convergence Network Ajou UniversitySuwon16499Republic of Korea Department of Computer Engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11362Saudi Arabia

The controller is a main component in the Software-Defined networking(SDN)framework,which plays a significant role in enabling programmability and orchestration for 5G and next-generation *** SDN,frequent communication occurs between network switches and the controller,which manages and directs traffic *** the controller is not strategically placed within the network,this communication can experience increased delays,negatively affecting network ***,an improperly placed controller can lead to higher end-to-end(E2E)delay,as switches must traverse more hops or encounter greater propagation delays when communicating with the *** paper introduces a novel approach using Deep Q-Learning(DQL)to dynamically place controllers in Software-Defined Internet of Things(SD-IoT)environments,with the goal of minimizing E2E delay between switches and controllers.E2E delay,a crucial metric for network performance,is influenced by two key factors:hop count,which measures the number of network nodes data must traverse,and propagation delay,which accounts for the physical distance between *** approach models the controller placement problem as a Markov Decision Process(MDP).In this model,the network configuration at any given time is represented as a“state,”while“actions”correspond to potential decisions regarding the placement of controllers or the reassignment of switches to *** a Deep Q-network(DQN)to approximate the Q-function,the system learns the optimal controller placement by maximizing the cumulative reward,which is defined as the negative of the E2E ***,the lower the delay,the higher the reward the system receives,enabling it to continuously improve its controller placement *** experimental results show that our DQL-based method significantly reduces E2E delay when compared to traditional benchmark placement *** dynamically learning from the network’s real-time conditions,the prop

关键词： Software-defined networking deep Q-learning controller placement quality of service

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Optimized Resource Allocation in Vehicle Edge Computing through Platoon Collaboration

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IEEE Internet of Things Journal 2025年第11期12卷 16129-16141页

作者： Zhao, Liang Feng, Yuhang Hawbani, Ammar Xu, Lexi Liu, Zhi Bi, Yuanguo Shenyang Aerospace University School of Computer Science Shenyang110136 China China Unicom China United Network Communications Corporation Research Institute Beijing100048 China University of Electro-Communications Department of Computer and Network Engineering Japan Northeastern University School of Computer Science and Engineering Shenyang110819 China

In modern vehicular networks, the absence of infrastructure support such as Roadside Units (RSUs) presents significant challenges for efficient task offloading and allocation. Limited computational capabilities of individual vehicles, combined with task allocation imbalances caused by varying task complexity and vehicle capacities, further complicate the process. Additionally, the formation of vehicular platoons requires accurate future route and destination information to ensure stable collaboration and effective coordination. However, such information can be challenging to obtain due to dynamic and unpredictable road environments, hindering the reliability of platoon formation. To address these challenges, we propose a platoon-based offloading strategy that integrates Deep Reinforcement Learning (DRL) and Long Short-Term Memory (LSTM) networks to enhance task allocation efficiency. This approach also leverages the Convoy Formation Algorithm considering Future Positions (CFA-FP) to manage platoon constraints effectively. Experimental results demonstrate that our method significantly improves key performance metrics, including total computation cost, latency, and offloading success rate, compared to other task offloading strategies. © 2014 IEEE.

关键词： Deep reinforcement learning

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Towards robust and efficient intrusion detection in IoMT: a deep learning approach addressing data leakage and enhancing model generalizability

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Multimedia Tools and Applications 2024年 1-20页

作者： Bouke, Mohamed Aly El Atigh, Hayate Abdullah, Azizol Department of Communication Technology and Network Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang43400 Malaysia Departement of Computer Engineering Faculty of Computer Engineering Bandirma Onyedi Eylul University Balikesir10200 Turkey

The Internet of Medical Things (IoMT) brings advanced patient monitoring and predictive analytics to healthcare but also raises cybersecurity and data privacy issues. This paper introduces a deep-learning model for IoMT intrusion detection, utilizing the WUSTL EHMS 2020 dataset, which focuses on cyber threats like man-in-the-middle attacks and data injection. Our sequential model, trained with TensorFlow, surpasses traditional predictive accuracy and efficiency methods. It achieves 99% accuracy with reduced loss and excels in precision, recall, F1 Score, and ROC AUC. The model uses 25 key features from 45, preventing overfitting and addressing data leakage in preprocessing. These steps ensure the model's reliability for IoMT cybersecurity, demonstrating its effectiveness in real-world scenarios. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cybersecurity

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