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IEEE Global Communications Conference

作者： Kai Lei Junlin Huang Yu Li Fan Zhang Hengky Susanto Bo Bai Gong Zhang Jin Liu Shenzhen Key Lab for Information Centric Networking & Blockchain Technology (ICNLAB) School of Electronic and Computer Engineering (SECE) Peking University Shenzhen China PCL Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China Theory Lab 2012 Labs Huawei Technologies Co. Ltd. Hong Kong

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

In data center networks, flows with different objectives coexist and compete for limited resources. From the application-level perspective, it is hard to satisfy the demands of different flows without effective resource planning. To address the bandwidth allocation problem under multi-objective scenarios, we study a multi-objective network utility maximization (NUM) problem and propose our practical bandwidth allocation framework HOMMO, which consists of an upper layer algorithm (ULA) and a lower layer algorithm (LLA). This hierarchical design helps to strike a balance between accuracy and efficiency. Implemented in network switches, ULA is an online learning-based scheme that allocates bandwidth for aggregated flows with different performances objectives. Taking the outputs from ULA as capacity constraints, LLA acts as a fast scheduling method at packet-level. We extend NUMFabric by implementing the design of isolate queues and corresponding dequeue strategy in switches and make it a feasible solution of the LLA. Therefore, HOMMO achieves satisfactory isolation across flows with different objectives, which is equivalent to provide a network slicing solution. To evaluate the proposed framework, we implement it in ns-3 and verify the performance under various scenarios. The simulation results show that HOMMO not only quickly converges to a near-optimal solution of the multi-objective NUM problem but also guarantees a Pareto-optimal solution. Moreover, it outperforms the well-known transport protocol (i.e., DCTCP) with 2x increase on average bandwidth utilization and 1.96x improvement in global network utility at the aggregated flow level.

关键词： Channel allocation Bandwidth Data centers Scheduling Throughput Convex functions Machine learning

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Robust energy-efficient power loading for MIMO system under imperfect CSI

Robust energy-efficient power loading for MIMO system under ...

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作者： Rui, Yun Deng, Lei Wang, Qian Li, Jing Qian, Guoming Li, Mingqi Wang, Yingguan Shanghai Advanced Research Institute National Mobile Communications Research Laboratory Southeast University 201210 China Department of Information Engineering Chinese University of Hong Kong Hong Kong999077 Hong Kong School of Computer Science Wuhan University 430072 China State Key Laboratory of Integrated Service Networks Xidian University 710075 China College of Electronic Science and Engineering in NUPT 210003 China Shanghai Advanced Research Institute Chinese Academic of Science 201210 China Key Lab of Wireless Sensor Network and Communications Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences 201210 China

In this paper, we will investigate the energy efficient power loading problem in multipleinput multiple-output singular value decomposition (MIMO-SVD) architecture. Most existing power loading schemes are developed on the assumption that a scheduler possesses perfect channel state information (CSI). However, in practice, perfect CSI is not always available. In this paper, we propose an energy-efficient power loading scheme for MIMO system under imperfect CSI, taking into account the effects of channel estimation error (CEE). Specifically, we propose two algorithms to maximise the system energy efficiency. One is a global method which can achieve the global optimal solution with high computational complexity;the other is a suboptimal method which can obtain the approximate optimal solution with lower computational complexity. Simulation results validate the effectiveness and robustness of our proposed power loading scheme. Copyright © 2015 Inderscience Enterprises Ltd.

关键词： MIMO systems

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Secure the 5G and Beyond networks with Zero Trust and Access Control Systems for Cloud Native Architectures

Secure the 5G and Beyond Networks with Zero Trust and Access...

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ACS/IEEE International Conference on computer Systems and Applications

作者： Hisham A. Kholidy Keven Disen Andrew Karam Elhadj Benkhelifa Mohammad A. Rahman Atta-Ur Rahman Ibrahim Almazyad Ahmed F. Sayed Rakia Jaziri Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Dept. of Networks and Comp. Security SUNY Polytechnic Institute Utica NY USA The Air Force Research Laboratory (AFRL RIGB) Rome NY USA Smart Systems AI and Cybersecurity Research Centre Staffordshire Univ. UK Dept of Electrical and Computer Engineering Florida International University USA Dept. of Computer Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Dept. of Electrical and Computer Engineering Univ. of Arizona Tucson AZ USA Transmission Dept. Telecom Egypt Fayoum Egypt Paragraphe Research Lab. Univ. of Paris VIII France

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

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Audio-Visual Speech Processing Framework for Lip Reading

Audio-Visual Speech Processing Framework for Lip Reading

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International Conference on Information and Communication Technologies: From Theory to Applications, ICTTA

作者： Abdulbaset M. Nasr Abd Rahman Ramli Mohammad Hamiruce Shamala K Subramaniam Robotics Lab Institute of advanced technology Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia Department of Computer and Communications Systems Faculty of Engineering Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia Department of Electrical and Electronics Engineering Faculty of Engineering Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia Department of Communication Technology & Networks Faculty of Computer Science and Information Technology Universiti Putra Malaysia Serdang Selangor Darul Ehsan Malaysia

It is well known that speech production and perception process is inherently bimodal consisting of audio and visual components. Recently there has been increased interest in using the visual modality in combination with the acoustic modality for improved speech processing. This field of study has gained the title of audio-visual speech processing. Lip movement recognition, also known as lip reading, is a communication skill which involves the interpretation of lip movements in order to estimate some important parameters of the lips that include, but not limited to, size, shape and orientation. In this paper, we represent a hybrid framework for lip reading which is based on both audio and visual speech parameters extracted from a video stream of isolated spoken words. The proposed algorithm is self-tuned in the sense that it starts with an estimations of speech parameters based on visual lip features and then the coefficients of the algorithm are fine-tuned based on the extracted audio parameters. In the audio speech processing part, extracted audio features are used to generate a vector containing information of the speech phonemes. These information are used later to enhance the recognition and matching process. For lip feature extraction, we use a modified version of the method used by F. Huang and T. Chen for tracking of multiple faces. This method is based on statistical color modeling and the deformable template. The experiments based on the proposed framework showed interesting results in recognition of isolated words.

关键词： Speech processing Image processing Parameter estimation Data mining Feature extraction Robots computer networks Systems engineering and theory Communications technology computer science

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Across-Platform Detection of Malicious Cryptocurrency Transactions via Account Interaction Learning

arXiv

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arXiv 2024年

作者： Che, Zheng Shen, Meng Tan, Zhehui Du, Hanbiao Zhu, Liehuang Wang, Wei Chen, Ting Zhao, Qinglin Xie, Yong The School of Computer Science Beijing Institute of Technology Beijing100081 China The School of Cyberspace Science and Technology Beijing Institute of Technology Beijing100081 China The Ministry of Education Key Lab for Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China The School of Computer Science and Engineering Macau University of Science and Technology China The School of Computer and Information Science Qinghai Institute of Technology Xining810016 China

With the rapid evolution of Web3.0, cryptocurrency has become a cornerstone of decentralized finance. While these digital assets enable efficient and borderless financial transactions, their pseudonymous nature has also attracted malicious activities such as money laundering, fraud, and other financial crimes. Effective detection of malicious transactions is crucial to maintaining the security and integrity of the Web 3.0 ecosystem. Existing malicious transaction detection methods rely on large amounts of labeled data and suffer from low generalization. label-efficient and generalizable malicious transaction detection remains a challenging task. In this paper, we propose ShadowEyes, a novel malicious transaction detection method. Specifically, We first propose a generalized graph structure named TxGraph as a representation of malicious transaction, which captures the interaction features of each malicious account and its neighbors. Then we carefully design a data augmentation method tailored to simulate the evolution of malicious transactions to generate positive pairs. To alleviate account label scarcity, we further design a graph contrastive mechanism, which enables ShadowEyes to learn discriminative features effectively from unlabeled data, thereby enhancing its detection capabilities in real-world scenarios. We conduct extensive experiments using public datasets to evaluate the performance of ShadowEyes. The results demonstrate that it outperforms state-of-the-art (SOTA) methods in four typical scenarios. Specifically, in the zero-shot learning scenario, it can achieve an F1 score of 76.98% for identifying gambling transactions, surpassing the SOTA method by 12.05%. In the scenario of across-platform malicious transaction detection, ShadowEyes maintains an F1 score of around 90%, which is 10% higher than the SOTA method. Copyright © 2024, The Authors. All rights reserved.

关键词： Decentralized finance

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Online distributed job dispatching with outdated and partially-observable information

arXiv

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arXiv 2020年

作者： Hong, Yuncong Lv, Bojie Wang, Rui Tan, Haisheng Han, Zhenhua Zhou, Hao Lau, Francis C.M. LINKE Lab University of Science and Technology of China Hefei China Department of Electrical and Electronic Engineering Southern University of Science and Technology Shenzhen China Department of Computer Science The University of Hong Kong Hong Kong Research Center of Networks and Communications Peng Cheng Laboratory Shenzhen China

In this paper, we investigate online distributed job dispatching in an edge computing system residing in a Metropolitan Area Network (MAN). Specifically, job dispatchers are implemented on access points (APs) which collect jobs from mobile users and distribute each job to a server at the edge or the cloud. A signaling mechanism with periodic broadcast is introduced to facilitate cooperation among APs. The transmission latency is non-negligible in MAN, which leads to outdated information sharing among APs. Moreover, the fully-observed system state is discouraged as reception of all broadcast is time consuming. Therefore, we formulate the distributed optimization of job dispatching strategies among the APs as a Markov decision process with partial and outdated system state, i.e., partially observable Markov Decision Process (POMDP). The conventional solution for POMDP is impractical due to huge time complexity. We propose a novel low-complexity solution framework for distributed job dispatching, based on which the optimization of job dispatching policy can be decoupled via an alternative policy iteration algorithm, so that the distributed policy iteration of each AP can be made according to partial and outdated observation. A theoretical performance lower bound is proved for our approximate MDP solution. Furthermore, we conduct extensive simulations based on the Google Cluster trace. The evaluation results show that our policy can achieve as high as 20.67% reduction in average job response time compared with heuristic baselines, and our algorithm consistently performs well under various parameter settings. Copyright © 2020, The Authors. All rights reserved.

关键词： Markov processes

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MV-CC: Mask Enhanced Video Model for Remote Sensing Change Caption

arXiv

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arXiv 2024年

作者： Liu, Ruixun Li, Kaiyu Song, Jiayi Sun, Dongwei Cao, Xiangyong The School of Automation Xi’an Jiaotong University Xi’an710049 China The School of Software Engineering Xi’an Jiaotong University Xi’an710049 China The School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China

Remote sensing image change caption (RSICC) aims to provide natural language descriptions for bi-temporal remote sensing images. Since Change Caption (CC) task requires both spatial and temporal features, previous works follow an encoder-fusion-decoder architecture. They use an image encoder to extract spatial features and the fusion module to integrate spatial features and extract temporal features, which leads to increasingly complex manual design of the fusion module. In this paper, we introduce a novel video model-based paradigm without design of the fusion module and propose a Mask-enhanced Video model for Change Caption (MV-CC). Specifically, we use the off-the-shelf video encoder to simultaneously extract the temporal and spatial features of bi-temporal images. Furthermore, the types of changes in the CC are set based on specific task requirements, and to enable the model to better focus on the regions of interest, we employ masks obtained from the Change Detection (CD) method to explicitly guide the CC model. Experimental results demonstrate that our proposed method can obtain better performance compared with other state-of-the-art RSICC methods. The code is available at https://***/liuruixun/MV-CC. Copyright © 2024, The Authors. All rights reserved.

关键词： Change detection

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Cooperative jamming for secure transmission with both active and passive eavesdroppers

arXiv

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arXiv 2020年

作者： Si, Jiangbo Cheng, Zihao Li, Zan Cheng, Julian Wang, Hui-Ming Al-Dhahir, Naofal Integrated Service Networks Lab of Xidian University Xi’an710071 China School of Engineering University of British Columbia KelownaBCV1V 1V7 Canada School of Electronic and Information Engineering Xian Jiaotong University Xian710049 China Department of Electrical and Computer Engineering University of Texas at Dallas RichardsonTX75080 United States

Secrecy transmission is investigated for a cooperative jamming scheme, where a multi-antenna jammer generates artificial noise (AN) to confuse eavesdroppers. Two kinds of eavesdroppers are considered: passive eavesdroppers who only overhear the legitimate information, and active eavesdroppers who not only overhear the legitimate information but also jam the legitimate signal. Existing works only treat the passive and active eavesdroppers separately. Different from the existing works, we investigate the achievable secrecy rate in presence of both active and passive eavesdroppers. For the considered system model, we assume that the instantaneous channel state information (CSI) of the active eavesdroppers is available at the jammer, while only partial CSI of the passive eavesdroppers is available at the jammer. A new zero-forcing beamforming scheme is proposed in the presence of both active and passive eavesdroppers. For both the perfect and imperfect CSI cases, the total transmission power allocation between the information and AN signals is optimized to maximize the achievable secrecy rate. Numerical results show that imperfect CSI between the jammer and the legitimate receiver will do more harm to the achievable secrecy rate than imperfect CSI between the jammer and the active eavesdropper. Copyright © 2020, The Authors. All rights reserved.

关键词： Jamming

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Microservice-Based Fog Testbed for 6G Applications

Microservice-Based Fog Testbed for 6G Applications

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International Conference on Advanced Communication Technology (ICACT)

作者： Ekaterina Kuzmina Meriem Tefikova Artem Volkov Ammar Muthanna Abdelhamied A. Ateya Andrey Koucheryavy Department of Telecommunication Networks and Data Transmission Saint-Petersburg State University of Telecommunications Saint Petersburg Russia Department of Electronics and Communications Engineering Zagazig University Zagazig Egypt EIAS Data Science Lab College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

This paper provides a real-time fog computing model based on a microservice architecture that enables testing and modeling of eventual implementations of ultra-reliable low-latency communications (uRLLC) services. The work provides fog-based architecture for sixth-generation cellular (6G) applications, including telepresence and uRLLC. A testbed of a robot swarm was developed to prototype the proposed network architecture. Computing tasks are offloaded and handled based on a proposed microservice scheme introduced to meet the 6G requirements. Furthermore, we developed a novel migration scheme for the proposed architecture to support the mobility of end devices. The optimum server for migrating computing tasks is allocated by solving a proposed optimization problem using particle swarm optimization (PSO). All proposed algorithms were implemented in the developed prototype. The proposed work is introduced to provide an architectural foundation for testing fog-based 6G applications and services and to implement and test novel network methods in the future.

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SemiCD-VL: Visual-Language Model Guidance Makes Better Semi-supervised Change Detector

arXiv

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arXiv 2024年

作者： Li, Kaiyu Cao, Xiangyong Deng, Yupeng Song, Jiayi Liu, Junmin Meng, Deyu Wang, Zhi School of Software Engineering Xi’an Jiaotong University Xi’an710049 China School of Computer Science and Technology Ministry of Education Key Lab For Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Aerospace Information Research Institute Chinese Academy of Sciences Beijing100094 China School of Mathematics and Statistics Ministry of Education Key Lab of Intelligent Networks and Network Security Xi’an Jiaotong University Shaanxi Xi’an China Guangdong Guangzhou China

Change Detection (CD) aims to identify pixels with semantic changes between images. However, annotating massive numbers of pixel-level images is labor-intensive and costly, especially for multi-temporal images, which require pixel-wise comparisons by human experts. Considering the excellent performance of visual language models (VLMs) for zero-shot, open-vocabulary, etc. with prompt-based reasoning, it is promising to utilize VLMs to make better CD under limited labeled data. In this paper, we propose a VLM guidance-based semisupervised CD method, namely SemiCD-VL. The insight of SemiCD-VL is to synthesize free change labels using VLMs to provide additional supervision signals for unlabeled data. However, almost all current VLMs are designed for single-temporal images and cannot be directly applied to bi- or multi-temporal images. Motivated by this, we first propose a VLM-based mixed change event generation (CEG) strategy to yield pseudo labels for unlabeled CD data. Since the additional supervised signals provided by these VLM-driven pseudo labels may conflict with the original pseudo labels from the consistency regularization paradigm (e.g. FixMatch), we propose the dual projection head for de-entangling different signal sources. Further, we explicitly decouple the bi-temporal images semantic representation through two auxiliary segmentation decoders, which are also guided by VLM. Finally, to make the model more adequately capture change representations, we introduce contrastive consistency regularization by constructing feature-level contrastive loss in auxiliary branches. Extensive experiments show the advantage of SemiCD-VL. For instance, SemiCD-VL improves the FixMatch baseline by +5.3 IoUc on WHU-CD and by +2.4 IoUc on LEVIR-CD with 5% labels, and SemiCD-VL requires only 5% to 10% of the labels to achieve performance similar to the supervised methods. In addition, our CEG strategy, in an un-supervised manner, can achieve performance far superior to state-of-t

关键词： Visual languages

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