Pub Date : 2025-03-01DOI: 10.23919/JCIN.2025.10964098
Huaifeng Shi;Chengsheng Pan;Lishang Qin;Yingzhi Wang;Huangjie Lu
In response to the complex and multidimensional nature of converged traffic on heterogeneous links in tactical communication networks, which leads to the difficulty in ensuring the quality of service (QoS) requirements for critical services, a frame generation algorithm for differentiated services (DS-FG) is proposed. DS-FG deploys an adaptive frame generation algorithm based on deep reinforcement learning (DRL-FG) for time-sensitive service, while deploying a high efficient frame generation (HEFG) algorithm for non-time-sensitive service. DRL-FG constructs a reward function by combining the queue status information of time-sensitive service and utilizes deep deterministic policy gradients (DDPG) to train a decision model for adaptive frame generation (AFG) algorithm thresholds. Furthermore, Gaussian noise sampling and prioritized experience replay strategies are employed to enhance model training efficiency and performance, achieving optimal matching between time-sensitive service QoS requirements and frame generation thresholds. Experimental results demonstrate that DS-FG outperforms traditional algorithms, achieving up to 13% improvement in throughput and over 19.7% reduction in average queueing delay for time-sensitive service.
{"title":"Frame Generation Algorithm for Differentiated Services in Tactical Communication Networks","authors":"Huaifeng Shi;Chengsheng Pan;Lishang Qin;Yingzhi Wang;Huangjie Lu","doi":"10.23919/JCIN.2025.10964098","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964098","url":null,"abstract":"In response to the complex and multidimensional nature of converged traffic on heterogeneous links in tactical communication networks, which leads to the difficulty in ensuring the quality of service (QoS) requirements for critical services, a frame generation algorithm for differentiated services (DS-FG) is proposed. DS-FG deploys an adaptive frame generation algorithm based on deep reinforcement learning (DRL-FG) for time-sensitive service, while deploying a high efficient frame generation (HEFG) algorithm for non-time-sensitive service. DRL-FG constructs a reward function by combining the queue status information of time-sensitive service and utilizes deep deterministic policy gradients (DDPG) to train a decision model for adaptive frame generation (AFG) algorithm thresholds. Furthermore, Gaussian noise sampling and prioritized experience replay strategies are employed to enhance model training efficiency and performance, achieving optimal matching between time-sensitive service QoS requirements and frame generation thresholds. Experimental results demonstrate that DS-FG outperforms traditional algorithms, achieving up to 13% improvement in throughput and over 19.7% reduction in average queueing delay for time-sensitive service.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"15-25"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Autonomous vehicles (AVs) have the potential to enhance road safety, reduce fuel consumption, and alleviate traffic congestion. However, the computational demands of processing large volumes of sensory data for tasks like motion planning and trajectory forecasting impose critical challenges on limited onboard resources of AVs. Mobile edge computing (MEC) offers a solution by offloading these tasks to edge servers located in proximity of the vehicles. When AVs traverse remote areas lacking terrestrial infrastructures, low Earth orbit (LEO) satellites can fill this gap by providing edge computing services. In this paper, we propose a microservice-based framework for MEC-enabled hybrid satellite-terrestrial networks to support AVs. By decomposing monolithic applications into microservices deployed in containers, we enable scalable and flexible computing services. We address the challenges of microservice migration due to the mobility of AVs and LEO satellites by formulating a long-term optimization problem aimed at minimizing task and migration delays. An online Lyapunov-based algorithm is developed to solve this problem, reducing the decision space by scheduling periodic migrations and decomposing it into mixed-integer linear programming. Numerical results demonstrate that our proposed algorithm can achieve a nearly optimal results while maintaining a low execution time.
自动驾驶汽车（AV）具有提高道路安全性、降低油耗和缓解交通拥堵的潜力。然而，为完成运动规划和轨迹预测等任务而处理大量感知数据的计算需求，给自动驾驶汽车有限的车载资源带来了严峻挑战。移动边缘计算（MEC）提供了一种解决方案，可将这些任务卸载到位于车辆附近的边缘服务器上。当自动驾驶汽车穿越缺乏地面基础设施的偏远地区时，低地球轨道（LEO）卫星可以通过提供边缘计算服务填补这一空白。在本文中，我们提出了一个基于微服务的框架，用于支持 AV 的 MEC 卫星-地面混合网络。通过将单体应用分解为部署在容器中的微服务，我们实现了可扩展和灵活的计算服务。我们提出了一个长期优化问题，旨在最大限度地减少任务和迁移延迟，从而应对因视听设备和低地轨道卫星的移动性而带来的微服务迁移挑战。我们开发了一种基于 Lyapunov 的在线算法来解决这个问题，通过安排周期性迁移来缩小决策空间，并将其分解为混合整数线性规划。数值结果表明，我们提出的算法可以在保持较低执行时间的同时，获得接近最优的结果。
{"title":"Microservice Migration in Hybrid Satellite-Terrestrial Networks for Autonomous Vehicles","authors":"Xin Long;Yuning Jiang;Zixin Wang;Xin Liu;Yuanming Shi;Yong Zhou","doi":"10.23919/JCIN.2025.10964099","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964099","url":null,"abstract":"Autonomous vehicles (AVs) have the potential to enhance road safety, reduce fuel consumption, and alleviate traffic congestion. However, the computational demands of processing large volumes of sensory data for tasks like motion planning and trajectory forecasting impose critical challenges on limited onboard resources of AVs. Mobile edge computing (MEC) offers a solution by offloading these tasks to edge servers located in proximity of the vehicles. When AVs traverse remote areas lacking terrestrial infrastructures, low Earth orbit (LEO) satellites can fill this gap by providing edge computing services. In this paper, we propose a microservice-based framework for MEC-enabled hybrid satellite-terrestrial networks to support AVs. By decomposing monolithic applications into microservices deployed in containers, we enable scalable and flexible computing services. We address the challenges of microservice migration due to the mobility of AVs and LEO satellites by formulating a long-term optimization problem aimed at minimizing task and migration delays. An online Lyapunov-based algorithm is developed to solve this problem, reducing the decision space by scheduling periodic migrations and decomposing it into mixed-integer linear programming. Numerical results demonstrate that our proposed algorithm can achieve a nearly optimal results while maintaining a low execution time.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Walkie-talkies play a key role in public safety and military communications. To counter the security threats posed by counterfeiting and illegal use, this paper proposes a radio frequency (RF) fingerprinting method for walkie-talkies based on interpretable features and multi-layer perceptron (MLP). By extracting interpretable features in time, frequency, and statistical domains affected by different RF impairments and performing correlation analysis and dimensionality reduction, it ensures that the acceptable low complexity requirements of walkie-talkie equipment are maintained while the recognition capability is preserved. The measured results show that the recognition accuracy of the method can reach 96.5%, which has the advantages of being efficient, stable and interpretable. This research provides a low complexity and reliable physical layer security solution for identifying legitimate and illegitimate walkie-talkies, which can be further used to guide the engineering implementation and application of RF fingerprint identification modules.
{"title":"Feature Correlation Based RF Fingerprinting Identification for Walkie-Talkies Push-to-Talk Communications","authors":"Renhe Fan;Longcheng Li;Pengfei Sun;Yang Yu;Gang Wu","doi":"10.23919/JCIN.2025.10964097","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964097","url":null,"abstract":"Walkie-talkies play a key role in public safety and military communications. To counter the security threats posed by counterfeiting and illegal use, this paper proposes a radio frequency (RF) fingerprinting method for walkie-talkies based on interpretable features and multi-layer perceptron (MLP). By extracting interpretable features in time, frequency, and statistical domains affected by different RF impairments and performing correlation analysis and dimensionality reduction, it ensures that the acceptable low complexity requirements of walkie-talkie equipment are maintained while the recognition capability is preserved. The measured results show that the recognition accuracy of the method can reach 96.5%, which has the advantages of being efficient, stable and interpretable. This research provides a low complexity and reliable physical layer security solution for identifying legitimate and illegitimate walkie-talkies, which can be further used to guide the engineering implementation and application of RF fingerprint identification modules.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"57-70"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.23919/JCIN.2025.10964104
Zhouyong Hu;Afang Yuan;Qinyu Zhang;Zhihua Yang
In the lunar missions, relay satellites on the various orbits play critical roles in the cislunar communications, i.e. telemetry and telecommand to the probers on the far side of moon, which could help achieving the constant visibility between the actuator and the controller. However, huge time span in the end-to-end communication incurs obvious delay during the control loop which current controller design could not deal with effectively. In this work, therefore, we propose a greedy algorithm-based scheduling mechanism (GASM) for multiple time-delay control loops through the cislunar relay communication. In particular, we develop a novel metric of information value loss (IVL) to measure the utility of the proposed GASM by considering the timeliness of telemetry and telecommand data on the control loop. The simulations show that the proposed GASM could achieve excellent control stability and timeliness of data compared with typical scheduling algorithms based on age of information (AoI).
{"title":"Relaying in Cislunar Telemetry and Telecommand Communication: An Information Value Loss Guided Scheduling Mechanism for Multiple Time-Delay Control Systems","authors":"Zhouyong Hu;Afang Yuan;Qinyu Zhang;Zhihua Yang","doi":"10.23919/JCIN.2025.10964104","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964104","url":null,"abstract":"In the lunar missions, relay satellites on the various orbits play critical roles in the cislunar communications, i.e. telemetry and telecommand to the probers on the far side of moon, which could help achieving the constant visibility between the actuator and the controller. However, huge time span in the end-to-end communication incurs obvious delay during the control loop which current controller design could not deal with effectively. In this work, therefore, we propose a greedy algorithm-based scheduling mechanism (GASM) for multiple time-delay control loops through the cislunar relay communication. In particular, we develop a novel metric of information value loss (IVL) to measure the utility of the proposed GASM by considering the timeliness of telemetry and telecommand data on the control loop. The simulations show that the proposed GASM could achieve excellent control stability and timeliness of data compared with typical scheduling algorithms based on age of information (AoI).","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"37-49"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.23919/JCIN.2025.10964101
Chen Guo;Fang Cui;Chao Xu;Mohan Su;Zhihao Wang;Hongjia Li
Artificial intelligence (AI) has been widely envisioned as a key enabler for 5G and beyond networks. To integrate AI into mobile networks, the third generation partnership (3GPP) introduces the network data analytics function (NWDAF) starting from Release 15 to support “in-network” learning and inference, and further supports federated learning (FL) from Release 16 to protect data privacy. However, practical deployment of federated learning in 5G networks still faces challenges of high communication overhead and potential risks of model and data leakage. Motivated by these challenges, we propose a hierarchical networking and privacy-preserving federated learning (HiNP-FL) framework for 5G networks. Specifically, in the HiNP-FL framework, 1) we propose the hierarchical NWDAF based FL mechanism to reduce FL communication overhead in 5G networks; 2) based on multi-party polynomial evaluation (OMPE), we design a FL model and data privacy protection mechanism for the hierarchical FL mechanism; 3) we validate the privacy protection capability of the HiNP-FL framework through privacy analysis, and testify its effectiveness in terms of model accuracy and communication efficiency through extensive experiments.
{"title":"A Hierarchical Networking and Privacy-Preserving Federated Learning Framework for 5G Networks","authors":"Chen Guo;Fang Cui;Chao Xu;Mohan Su;Zhihao Wang;Hongjia Li","doi":"10.23919/JCIN.2025.10964101","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964101","url":null,"abstract":"Artificial intelligence (AI) has been widely envisioned as a key enabler for 5G and beyond networks. To integrate AI into mobile networks, the third generation partnership (3GPP) introduces the network data analytics function (NWDAF) starting from Release 15 to support “in-network” learning and inference, and further supports federated learning (FL) from Release 16 to protect data privacy. However, practical deployment of federated learning in 5G networks still faces challenges of high communication overhead and potential risks of model and data leakage. Motivated by these challenges, we propose a hierarchical networking and privacy-preserving federated learning (HiNP-FL) framework for 5G networks. Specifically, in the HiNP-FL framework, 1) we propose the hierarchical NWDAF based FL mechanism to reduce FL communication overhead in 5G networks; 2) based on multi-party polynomial evaluation (OMPE), we design a FL model and data privacy protection mechanism for the hierarchical FL mechanism; 3) we validate the privacy protection capability of the HiNP-FL framework through privacy analysis, and testify its effectiveness in terms of model accuracy and communication efficiency through extensive experiments.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"26-36"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821645","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.23919/JCIN.2025.10964096
Shuai Chen;Kunqi Guo
Device-to-device (D2D) communication underlay cellular networks offers several benefits, including offloading cellular traffic and improving throughput. In this paper, a scheme is proposed for D2D communication underlay cellular networks, by taking resource allocation algorithm into account. Firstly, a scenario where cellular users (CUs) and D2D users (DUs) coexist is considered. To characterize the clustering feature of DUs, the locations of CUs and DUs are modeled as Poisson cluster processes (PCPs), and the locations of base stations (BSs) are modeled as a Poisson point process (PPP). Subsequently, a resource allocation algorithm is proposed to allocate resources for CUs to DUs in the cluster, which involves the optimization of matching between D2D user and cellular user. Finally, the coverage probability of the clustering network is analyzed. The simulation results indicate that the proposed scheme can effectively enhance system capacity and reveal the effects of various parameters on system performance.
{"title":"Resource Allocation and Performance Analysis in D2D Underlay Cellular Networks with Poisson Cluster Process","authors":"Shuai Chen;Kunqi Guo","doi":"10.23919/JCIN.2025.10964096","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964096","url":null,"abstract":"Device-to-device (D2D) communication underlay cellular networks offers several benefits, including offloading cellular traffic and improving throughput. In this paper, a scheme is proposed for D2D communication underlay cellular networks, by taking resource allocation algorithm into account. Firstly, a scenario where cellular users (CUs) and D2D users (DUs) coexist is considered. To characterize the clustering feature of DUs, the locations of CUs and DUs are modeled as Poisson cluster processes (PCPs), and the locations of base stations (BSs) are modeled as a Poisson point process (PPP). Subsequently, a resource allocation algorithm is proposed to allocate resources for CUs to DUs in the cluster, which involves the optimization of matching between D2D user and cellular user. Finally, the coverage probability of the clustering network is analyzed. The simulation results indicate that the proposed scheme can effectively enhance system capacity and reveal the effects of various parameters on system performance.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"71-80"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821577","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-03-01DOI: 10.23919/JCIN.2025.10964100
Dong Wang;Runqin Rong;Guizhi Li
Using unmanned aerial vehicles (UAVs) to collect data provides a paradigm for the design of wireless sensor networks (WSNs), but faces with the challenges of data security and energy limitation. To cope with these issues, the coverage-optimal deployment of the UAV is proposed by optimizing the UAV's positions to accommodate the intelligent movements of the eavesdropper while covering more sensors for secure data transmission. Then, to maximize the energy efficiency of data collection, the power is optimally allocated to the activated sensors that satisfy the condition of secure transmission. As evaluated in simulation, by the joint optimization of spatial and power domains, the data security and energy efficiency can be ensured for the UAV-enabled data collection in the WSNs.
{"title":"Coverage-Optimal UAV Deployment and Energy-Efficient Sensor Power Allocation for UAV-Enabled Data Collection","authors":"Dong Wang;Runqin Rong;Guizhi Li","doi":"10.23919/JCIN.2025.10964100","DOIUrl":"https://doi.org/10.23919/JCIN.2025.10964100","url":null,"abstract":"Using unmanned aerial vehicles (UAVs) to collect data provides a paradigm for the design of wireless sensor networks (WSNs), but faces with the challenges of data security and energy limitation. To cope with these issues, the coverage-optimal deployment of the UAV is proposed by optimizing the UAV's positions to accommodate the intelligent movements of the eavesdropper while covering more sensors for secure data transmission. Then, to maximize the energy efficiency of data collection, the power is optimally allocated to the activated sensors that satisfy the condition of secure transmission. As evaluated in simulation, by the joint optimization of spatial and power domains, the data security and energy efficiency can be ensured for the UAV-enabled data collection in the WSNs.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"10 1","pages":"50-56"},"PeriodicalIF":0.0,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143821810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCIN.2024.10820165
Chao Gong;Ping Li;Panlong Yang;Zhao Lv
In the realm of the Internet of things (IoT), radio frequency identification (RFID) technology is essential for linking physical objects to digital data. The introduction of harmonic technology has expanded RFID's applications by improving sensitivity and enabling communication with wireless fidelity (Wi-Fi) networks. However, this trend faces challenges, notably interference from strong Wi-Fi signals, which impacts RFID-based sensing systems. This paper proposes the WiFID (Wi-Fi and RFID) algorithm, which enables Wi-Fi devices to sense weak RFID harmonic signals. Adaptive subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic sensing. Experimental validation on the universal software radio peripheral (USRP) platform demonstrates that WiFID successfully detects 90% of RFID harmonics at −30 dBm, with only a minimal 4% decrease in Wi-Fi throughput.
{"title":"Detection and Recovery of RFID Harmonic Signal for Wi-Fi Interference","authors":"Chao Gong;Ping Li;Panlong Yang;Zhao Lv","doi":"10.23919/JCIN.2024.10820165","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10820165","url":null,"abstract":"In the realm of the Internet of things (IoT), radio frequency identification (RFID) technology is essential for linking physical objects to digital data. The introduction of harmonic technology has expanded RFID's applications by improving sensitivity and enabling communication with wireless fidelity (Wi-Fi) networks. However, this trend faces challenges, notably interference from strong Wi-Fi signals, which impacts RFID-based sensing systems. This paper proposes the WiFID (Wi-Fi and RFID) algorithm, which enables Wi-Fi devices to sense weak RFID harmonic signals. Adaptive subcarrier deallocation minimally reduces Wi-Fi throughput while effectively limiting interference with RFID harmonic sensing. Experimental validation on the universal software radio peripheral (USRP) platform demonstrates that WiFID successfully detects 90% of RFID harmonics at −30 dBm, with only a minimal 4% decrease in Wi-Fi throughput.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 4","pages":"1-10"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCIN.2024.10820160
Yuanxiang Gong;Chiya Zhang;Yiyi Liu
Zero-day malware refers to a previously unknown or newly discovered type of malware. While most existing studies rely on large malware sample sets, their performance is unknown when dealing with a limited number of samples. This paper addresses this challenge by proposing a novel approach for effective zero-day malware detection, even with a scarcity of known samples. The proposed method begins by visualizing the malware binary and converting it into an entropy image. Subsequently, a deep convolutional generative adversarial network (DCGAN) is employed to learn from the available samples and generate new, highly similar synthetic samples. By combining these generated samples with the real ones, a comprehensive training set is constructed for a convolutional neural network (CNN) classification model. The randomness introduced by DCGAN facilitates the generation of new features, even in the presence of a small sample size. This enables the classifier to learn the characteristics of unknown zero-day malware and enhance its detection capabilities. Extensive experiments validate the effectiveness of the proposed approach, demonstrating that leveraging entropy images as features and applying DCGAN for data augmentation leads to a robust zero-day malware detection system, capable of achieving promising results even with a limited number of samples.
{"title":"Zero-Day Malware Defence with Limited Samples","authors":"Yuanxiang Gong;Chiya Zhang;Yiyi Liu","doi":"10.23919/JCIN.2024.10820160","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10820160","url":null,"abstract":"Zero-day malware refers to a previously unknown or newly discovered type of malware. While most existing studies rely on large malware sample sets, their performance is unknown when dealing with a limited number of samples. This paper addresses this challenge by proposing a novel approach for effective zero-day malware detection, even with a scarcity of known samples. The proposed method begins by visualizing the malware binary and converting it into an entropy image. Subsequently, a deep convolutional generative adversarial network (DCGAN) is employed to learn from the available samples and generate new, highly similar synthetic samples. By combining these generated samples with the real ones, a comprehensive training set is constructed for a convolutional neural network (CNN) classification model. The randomness introduced by DCGAN facilitates the generation of new features, even in the presence of a small sample size. This enables the classifier to learn the characteristics of unknown zero-day malware and enhance its detection capabilities. Extensive experiments validate the effectiveness of the proposed approach, demonstrating that leveraging entropy images as features and applying DCGAN for data augmentation leads to a robust zero-day malware detection system, capable of achieving promising results even with a limited number of samples.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 4","pages":"340-347"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-12-01DOI: 10.23919/JCIN.2024.10820159
Chun Zhu;Guilong Zhu;Jie Yang;Miao Liu;Zheng Shi
The rapid increase in the number of Internet of things (IoT) devices has led to significant access pressure, making network energy consumption and communication load key challenges. Edge caching, cooperative communication, and energy management technologies have proven to be effective in alleviating these issues. This paper investigates a unmanned aerial vehicle (UAV)-assisted Internet of everything (IoE) architecture that integrates caching, communication, and energy management. A collaborative communication-caching-energy optimization scheme is proposed, which involves the joint operation of the UAV and base station (BS) to pre-cache content required by ground users, thus minimizing system energy consumption. We model the joint optimization of content caching, communication, and energy consumption as a Markov decision process (MDP), transforming it into a long-term optimization problem solvable by deep reinforcement learning. Based on the simple deep Q-network (DQN), we design a dynamic content placement strategy that jointly optimizes communication, caching, and energy consumption. Simulation results demonstrate that the proposed method, compared to branch and bound (B&B), particle swarm optimization (PSO), genetic algorithm (GA), and random algorithms, not only approaches the optimal solution most closely, effectively reducing system energy consumption, but also exhibits the lowest time complexity.
{"title":"Quick and Good: A DRL Based Communication-Caching-Energy Joint Optimization Scheme for Prolonging the Lifetime of UAV Assisted IoE","authors":"Chun Zhu;Guilong Zhu;Jie Yang;Miao Liu;Zheng Shi","doi":"10.23919/JCIN.2024.10820159","DOIUrl":"https://doi.org/10.23919/JCIN.2024.10820159","url":null,"abstract":"The rapid increase in the number of Internet of things (IoT) devices has led to significant access pressure, making network energy consumption and communication load key challenges. Edge caching, cooperative communication, and energy management technologies have proven to be effective in alleviating these issues. This paper investigates a unmanned aerial vehicle (UAV)-assisted Internet of everything (IoE) architecture that integrates caching, communication, and energy management. A collaborative communication-caching-energy optimization scheme is proposed, which involves the joint operation of the UAV and base station (BS) to pre-cache content required by ground users, thus minimizing system energy consumption. We model the joint optimization of content caching, communication, and energy consumption as a Markov decision process (MDP), transforming it into a long-term optimization problem solvable by deep reinforcement learning. Based on the simple deep Q-network (DQN), we design a dynamic content placement strategy that jointly optimizes communication, caching, and energy consumption. Simulation results demonstrate that the proposed method, compared to branch and bound (B&B), particle swarm optimization (PSO), genetic algorithm (GA), and random algorithms, not only approaches the optimal solution most closely, effectively reducing system energy consumption, but also exhibits the lowest time complexity.","PeriodicalId":100766,"journal":{"name":"Journal of Communications and Information Networks","volume":"9 4","pages":"1-14"},"PeriodicalIF":0.0,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142912490","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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