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Cooperative Spectrum Sensing Based on Centralized Double Threshold in MCN

China Communications 2020年第5期17卷 235-242页

作者： Hai Huang Junsheng Mu Xiaojun Jing School of Information and Communication Engineering Beijing University of Posts and TelecommunicationsBeijing 100876China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of EducationBeijing 100876China

Cooperative spectrum sensing appears popular currently due to its ability to solve the issue of hidden terminal and improve detection performance in Cognitive Radio Networks. Meanwhile, double threshold based energy detector has attracted much attention for its low computational complexity and superior performance. Motivated by this, a cooperative spectrum sensing scheme is proposed in this paper based on centralized double threshold in the maritime communication networks(MCN), where the energy value of received signal in each cognitive node is forwarded to the fusion center for final decision based on double thresholds. Additionally, the proposed scheme is further optimized for the decisions that the energy is within the scope of maximum threshold and minimum threshold. Simulation experiments verify the performance of the proposed method.

关键词： cooperative spectrum sensing double threshold maritime communication network

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A Lightweight and Robust Cross-Domain Authentication Scheme Based on Master-Slave Blockchain

A Lightweight and Robust Cross-Domain Authentication Scheme ...

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International Conference on Computer and Communications (ICCC)

作者： Kaiyang Deng Yali Gao Jie Yuan Zihan Hou Xiaoyong Li Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education College of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China

In the era of network informatization, the Internet of Things has more and more complex requirements for information interaction across trust domains, and cross-domain identity authentication has become the focus of research and attention by security personnel. However, IoT devices often lack computing power and lack of security protection measures. The existing authentication schemes are complex and have poor robustness, which is not suitable for the IoT environment. to address this problem, this paper proposes a lightweight and robust cross-domain authentication scheme based on master-slave blockchain. Our method adopts the master-slave blockchain mode for cross-domain identity authentication, uses the trust evaluation mechanism to measure the reputation value of nodes, and selects nodes with high reputation values as cross-domain communication nodes. Experimental results show that our method is not only more robust than similar methods but also takes less time to complete the authentication.

关键词： Power measurement Authentication Computer architecture Main-secondary Robustness Blockchains Internet of Things

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A DNS-based Data Exfiltration Traffic Detection Method for Unknown Samples 7

A DNS-based Data Exfiltration Traffic Detection Method for U...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Ruiling, Gan Jiawen, Diao Xiang, Cui Shouyou, Song QI-ANXIN Technology Group Beijing China Beijing University of Posts and Telecommunications Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service Beijing China Guangzhou University Cyberspace Institute Advanced Technology Guangzhou China Beijing DigApis Technology Co. Ltd Beijing China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

The advanced persistent threat (APT) is one of the most serious threats to cyberspace security. Posting back of exfiltrated data by way of DNS covert channels has become increasingly popular among APT attackers. Early detection techniques were mainly based on rule matching, whose accuracy may be affected by the subjectivity of the researchers. The rise of machine learning technology solves this problem. However, the current DNS traffic detection models based on machine learning lack the open-source datasets for training and they will lose detection accuracy for unknown malicious traffic whose abnormal points are different from the observed samples. As for the problem of insufficient data set, we propose a sample set enhancement method that simulating DNS attacks in the cyber range and using the captured flow as the training set. Regarding the detection of unknown malicious traffic, we put forward four new features (domain readability, domain structure, second-level domain phishing and IP discreteness) based on the principles for constructing malicious traffic. We use the decision tree algorithm to implement a detection model. In the unknown DNS data leakage traffic tests, our model achieved an average detection rate of 99.925%. After applying our sample set and feature set enhanced schemes to the existing work, the experimental results show that the enhanced detection model can detect unknown DNS data leakage traffic that could not be detected previously. © 2022 IEEE.

关键词： Decision trees

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A Practical YOLOV5 Face Detector with Decoupled Swin Head

A Practical YOLOV5 Face Detector with Decoupled Swin Head

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IEEE International Conference on Systems, Man and Cybernetics

作者： Shuozhi Yuan Wenming Guo Feng Yang Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Xinjiang Institute of Engineering Beijing China Xinjiang Institute of Engineering Urumqi China

Face detection is a fundamental and practical problem in computer vision, which aims to indicate the face positions in a wild environment precisely. However, different from the generic object detection tasks, there are a large number of face samples that suffer from unconstrained poses, occlusion, extreame lights, or other harmful conditions. YOLOV5 is an incredible milestone in the object detection area, but still not powerful enough for the challenging samples. To ease these difficulties, in this paper, we customize DSH-YOLOV5, a practical face detector. Specifically, we integrate a decoupled head with swin transformer layers, whose self-attention mechanism has great potential to explore subtle interconnection details. Additionally, we use two context modules (CBAM and SSH) to enhance the performance of features. Furthermore, we design a novel copy-paste data augmentation to fit the above challenging scenes. Extensive experiments demonstrate that we achieve SOTA performance on WIDER FACE, FDDB, and PASCAL FACE with competitive computational costs. Moreover, under COVID-19, we add the breathing mask and gender classification branches based on the ROI Align to produce more practical face information.

关键词：

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SATMask: Spatial Attention Transform Mask for Dense Instance Segmentation 7

SATMask: Spatial Attention Transform Mask for Dense Instance...

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7th IEEE International Conference on Data Science in Cyberspace, DSC 2022

作者： Mao, Quanzhong Sun, Lijuan Wu, Jingchen Gao, Yutong Wu, Xu Qiu, Lirong Ministry of Education Key Laboratory of Trustworthy Distributed Computing and Service China Bupt School of Cyberspace Security Bupt School of Computer Science National Pilot Software Engineering School Beijing Jiaotong University Beijing University of Posts and Telecommunications Beijing China Beijing University of Posts and Telecommunications Library China

ISBN: (数字)9781665474801

ISBN: (纸本)9781665474801

There are often dense objects in the images processed by instance segmentation, but too dense objects will cause the problem that the objects are difficult to segment. Most of the current dense instance segmentation methods are based on dense sliding window such as TensorMask. However, the sliding window has the problems of high computation and difficult design of anchor. In order to solve the above difficulties, we propose an anchor-free and single shot dense image segmentation framework, named SATMask, which adds a Spatial Attention Transform (SAT) mask head on anchor-free one stage object detector (FCOS) to predict high quality instance mask with low complexity, and uses feature-aligned pyramid network to fuse the feature map generated by backbone to obtain rich spatial details and better semantic information. Extensive experiments on the challenging COCO and Cityscapes datasets demonstrate the effectiveness of SATMask. In particular, under the same backbone (ResNet-101), SATMask achieves 39.8% AP on COCO, surpassing the state-of-the-art instance segmentation method Mask R-CNN 1.5% AP. © 2022 IEEE.

关键词： Discrete cosine transforms

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Low-Complexity distributed Precoding in User-Centric Cell-Free mmWave MIMO Systems

arXiv

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arXiv 2022年

作者： Zhong, Yingrong Cao, Yashuai Lv, Tiejun Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

User-centric (UC) based cell-free (CF) structures can provide the benefits of coverage enhancement for millimeter wave (mmWave) multiple input multiple output (MIMO) systems, which is regarded as the key technology of the reliable and high-rate services. In this paper, we propose a new beam selection scheme and precoding algorithm for the UC CF mmWave MIMO system, where a weighted sum-rate maximization problem is formulated. Since the joint design of beam selection and precoding is non-convex and tractable with high complexity, this paper designs the beam selection and precoding separately. Particularly, the proposed beam selection aims at reducing the inter-cluster inter-beam interference, then we also propose a precoding algorithm based on the weighted sum mean-square error (WSMSE) framework, where the precoding matrix can be updated in a distributed manner. We further employ the low-rank decomposition and Neumann series expansion (NSE) to reduce the computational complexity of the precoding. Simulations and complexity analysis verify the effectiveness of the proposed algorithm with a considerable reduction in computational complexity. Copyright © 2022, The Authors. All rights reserved.

关键词： Millimeter waves

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WBRP: Consensus Algorithm Based on Weak Proof of Work

WBRP: Consensus Algorithm Based on Weak Proof of Work

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IEEE International Conference on Blockchain (Blockchain)

作者： Jiarui Li Dongbin Wang Hui Zhi Wencheng Zhu Jianing Li Zedong Wang School of Cyberspace Engineering Beijing University of Post and Telecommunications Beijing China National Engineering Laboratory for Mobile Network Beijing China TravelSky Technology Limited Beijing China Key Laboratory of Ministry of Education and Trustworthy Distributed Computing and Service Beijing China

ISBN: (数字)9798350351590

ISBN: (纸本)9798350351606

Nakamoto Consensus (NC) is a classic Proof of Work (PoW) consensus algorithm where the probability of miners earning the right to a block is proportional to their computational power. To prevent blockchain forks, the interval between block outputs is restricted, which results in lower throughput for PoW. To address blockchain performance issues, a transaction ledger scheme based on weak PoW consensus has been proposed. This approach combines strong and weak blocks, allowing miners to submit weak blocks with lower mining difficulty. Nodes can then use these weak blocks to organize transaction information on the main chain and ensure strict linear ordering of blocks and transactions through sorting rules. Experimental results demon-strate that the consensus algorithm based on weak PoW can significantly enhance system performance, allowing transaction throughput to increase linearly with bandwidth. Specifically, with a node bandwidth of 1 Gb/s, the performance can exceed NC by more than 300 times.

关键词： System performance Consensus algorithm Bandwidth Throughput Proof of Work Extensibility Blockchains

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Intellicise Wireless Networks From Semantic Communications: A Survey, Research Issues, and Challenges

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IEEE Communications Surveys and Tutorials 2025年第3期27卷 2051-2084页

作者： Zhang, Ping Xu, Wenjun Liu, Yiming Qin, Xiaoqi Niu, Kai Cui, Shuguang Shi, Guangming Qin, Zhijin Xu, Xiaodong Wang, Fengyu Meng, Yue Dong, Chen Dai, Jincheng Yang, Qianqian Sun, Yaping Gao, Dahua Gao, Hui Han, Shujun Song, Xiaodan Beijing University of Posts and Telecommunications State Key Laboratory of Network and Switching Technology Beijing100876 China Peng Cheng Laboratory Department of Broadband Communication Shenzhen518066 China Shenzhen518172 China Tsinghua University Department of Electronic Engineering Beijing National Research Center for Information Science and Technology Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100876 China Beijing University of Posts and Telecommunications School of Artificial Intelligence Beijing100876 China Beijing University of Posts and Telecommunications Key Laboratory of Universal Wireless Communications Ministry of Education Beijing100876 China Zhejiang University College of Information Science and Electronic Engineering Hangzhou310007 China Xidian University School of Artificial Intelligence Xi’an710071 China Pazhou Lab Guangzhou510555 China Beijing University of Posts and Telecommunications Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing100876 China Guangzhou Institute of Technology Xidian University Guangzhou510555 China

Information and communication technology (ICT) has been an essential part of modern society. However, the current communication systems are not sufficient to meet the demands of emerging applications. Intellicise (intelligent and concise) wireless networks, with their inherent characteristics of intelligence-endogenous and primitive-concise, have been proposed as a promising research direction. In this paper, we focus on intellicise wireless networks from semantic communication (SemCom). We present a comprehensive framework of intellicise wireless networks, including components such as brain for intellicise wireless networks (BIWN), intellicise signal processing, intellicise information transmission, intellicise network organization, and intellicise service bearing. We also investigate the enabling technologies and driving factors of intellicise wireless networks. Subsequently, we introduce the applications of intellicise wireless networks and envision new services. Finally, we outline the challenges of implementing intellicise wireless networks from a broad perspective and discuss possible solutions. © 1998-2012 IEEE.

关键词： Mobile telecommunication systems

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Enhancing IoT Flow Anomaly Detection with Differential Optimal Feature Subspace

Enhancing IoT Flow Anomaly Detection with Differential Optim...

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IEEE International Conference on Data Science in Cyberspace (DSC)

作者： Weidong Shi Mingrui Fan Jiaqi Gao Jiahao Qi Daoqi Han Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education School of Cyberspace Security The State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

With the continuous expansion of network scale and increasing complexity, the application of the Internet of Things (IoT) is becoming more extensive. However, it is also vulnerable to various abnormal attacks, making security problems increasingly significant. Typically, traffic data dimensions are relatively large, and selecting features effectively is a key research issue. Addressing the aforementioned problems, this paper analyzes network traffic data and proposes a method(DOFS) to select the optimal feature subspace for different types of attacks, and verifies its effectiveness by comparative experiments. Then the T-S feature selection algorithm is improved, and DOFS is applied to the improved T-S feature selection algorithm, and the usability of the method is verified by experiments. By obtaining feature importance scores, the best feature subspace is selected for each type of traffic category. The method is verified using the network flow dataset CIC2018 and a real-world flow dataset. Experimental evaluations on diverse datasets demonstrate that the proposed method outperforms existing feature selection algorithms under different evaluation metrics, while exhibiting efficient time over-head and strong generalization ability. The results highlight the effectiveness and practicality of the proposed approach in IoT flow anomaly detection.

关键词：

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Seeing the Attack Paths: Improved Flow Correlation Scheme in Stepping-Stone Intrusion

Seeing the Attack Paths: Improved Flow Correlation Scheme in...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zixuan Chen Chao Zheng Zhao Li Jinqiao Shi Zeyu Li School of Cyberspace Security Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing China Geedge Networks Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Stepping-stones are widely used by attackers to conceal their identities and gain unauthorized access to restricted targets. Numerous strategies have been suggested to identify stepping-stones and counteract evasive behaviors, with flow correlation standing out as the key technique used in many deanonymization methods. Existing attempts to tackle the flow correlation issue rely on long-flow observations and feature-based methods. Yet, these approaches meet substantial limitations in their suitability across diverse scenarios, network noises influence and accuracy, particularly in the stepping-stone environment. In this paper, we introduce an improved flow correlation model, FlowLinker, which aims to resolve these issues. Specifically, we combined multidimensional statistical features and cumulative flow sequences to construct a robust traffic feature. Then, we leveraged the triplet network to produce an optimized representation, amplifying the difference between unrelated representations. Consequently, it significantly reduces the false correlation rate with flows that seem similar but are unrelated. The experiments on real-world datasets from different network environments we collected show that FlowLinker outperforms other state-of-the-art methods with shorter lengths of flow observations.

关键词： Correlation Accuracy Federated learning Computational modeling Noise

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