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MTSS: multi-path traffic scheduling mechanism based on SDN

Journal of Systems Engineering and Electronics 2019年第5期30卷 974-984页

作者： XU Xiaolong CHEN Yun HU Liuyun KUMAR Anup Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China Institute of Big Data Research at Yancheng Nanjing University of Posts and TelecommunicationsYancheng 224000China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China Department of Computer Engineering and Computer Science University of LouisvilleLouisville KY40292USA

Large-scale and diverse businesses based on the cloud computing platform bring the heavy network traffic to cloud data ***,the unbalanced workload of cloud data center network easily leads to the network congestion,the low resource utilization rate,the long delay,the low reliability,and the low *** order to improve the utilization efficiency and the quality of services(QoS)of cloud system,especially to solve the problem of network congestion,we propose MTSS,a multi-path traffic scheduling mechanism based on software defined networking(SDN).MTSS utilizes the data flow scheduling flexibility of SDN and the multi-path feature of the fat-tree structure to improve the traffic balance of the cloud data center network.A heuristic traffic balancing algorithm is presented for MTSS,which periodically monitors the network link and dynamically adjusts the traffic on the heavy link to achieve programmable data forwarding and load *** experimental results show that MTSS outperforms equal-cost multi-path protocol(ECMP),by effectively reducing the packet loss rate and *** addition,MTSS improves the utilization efficiency,the reliability and the throughput rate of the cloud data center network.

关键词： cloud data center software defined networking(SDN) load balancing multi-path transmission OpenFlow

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Congestion Relief in Downlink OFDMA Cellular System Using Inter-Cell Relay

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China Communications 2019年第4期16卷 35-46页

作者： Weifeng Lu Mingqi Zhu Jia Xu Siguang Chen Lijun Yang Jian Xu School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications College of Internet of Things Nanjing University of Posts and Telecommunications School of Computer Science and Engineering Nanjing University of Science and Technology

This paper proposes the concept of inter-cell relay for downlink orthogonal frequency division multiple access(OFDMA) cellular systems, which uses multi-hop to relay calls from overloaded cells to light-load neighboring cells. It is shown that when using inter-cell relay, the number of calls in the congestion cell can be significantly increased. The congestion cell is divided into two parts. One is called non-relay area(NRA), in which a call directly communicates with the base station(BS) of a congested cell. The other is called relay area(RA), in which a call communicates with the BS of a neighboring cell through a relay station(RS). The two parts have different user-call densities. By adjusting the densities of two parts, we will maximize the number of supported calls inside a congested cell. The results show the benefits gained from inter-cell relay in congestion relief, which can reduce cell congestion by fully utilizing the available resources in the neighboring cells.

关键词： OFDMA cellular system congestion relief optimization problem inter-cell relay

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DCEL:classifier fusion model for Android malware detection

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Journal of Systems Engineering and Electronics 2024年第1期35卷 163-177页

作者： XU Xiaolong JIANG Shuai ZHAO Jinbo WANG Xinheng Jiangsu Key Laboratory of Big Data Security&Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023China School of Computing and Engineering University of West LondonLondon W55RFUK

The rapid growth of mobile applications,the popularity of the Android system and its openness have attracted many hackers and even criminals,who are creating lots of Android ***,the current methods of Android malware detection need a lot of time in the feature engineering ***,these models have the defects of low detection rate,high complexity,and poor practicability,*** analyze the Android malware samples,and the distribution of malware and benign software in application programming interface(API)calls,permissions,and other *** classify the software’s threat levels based on the correlation of ***,we propose deep neural networks and convolutional neural networks with ensemble learning(DCEL),a new classifier fusion model for Android malware ***,DCEL preprocesses the malware data to remove redundant data,and converts the one-dimensional data into a two-dimensional gray ***,the ensemble learning approach is used to combine the deep neural network with the convolutional neural network,and the final classification results are obtained by voting on the prediction of each single *** based on the Drebin and Malgenome datasets show that compared with current state-of-art models,the proposed DCEL has a higher detection rate,higher recall rate,and lower computational cost.

关键词： Android malware detection deep learning ensemble learning model fusion

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Raft Consensus Algorithm Based on Credit Model in Consortium Blockchain

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Wuhan University Journal of Natural Sciences 2020年第2期25卷 146-154页

作者： CHEN Yunfang LIU Ping ZHANG Wei School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023JiangsuChina Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023JiangsuChina

As one of the underlying technologies of the blockchain,the consensus algorithm plays a vital role in ensuring security and *** a consensus algorithm for the private blockchain,Raft has better performance than the rest of the consensus algorithms,and it does not cause problems such as the concentrated hashing power,resource waste and ***,Raft can only be used in a non-byzantine environment with a small network *** order to enable Raft to be used in a large-scale network with a certain number of byzantine nodes,this paper combines Raft and credit model to propose a Raft blockchain consensus algorithm based on credit model *** the node credit evaluation phase,RBF-based support vector machine is used as the anomaly detection method,and the node credit evaluation model is *** the Trust Nodes List(TNL)mechanism is introduced to make the consensus phase in a creditable network ***,the common node is synchronized to the consensus node to update the blockchain of the entire *** show that CRaft has better throughput and lower latency than the commonly used consortium blockchain consensus algorithm PBFT(Practical Byzantine Fault Tolerance).

关键词： credit model consensus algorithm consortium blockchain P2P network

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Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

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ACM Transactions on Internet Technology 2021年第1期22卷 1-23页

作者： Xu, Jia Zhou, Yuanhang Chen, Gongyu Ding, Yuqing Yang, Dejun Liu, Linfeng Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications 9 Wenyuan Rd Jiangsu Nanjing210023 China Colorado School of Mines 1500 Illinois St. GoldenCO80401 United States

Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion (BFTD) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average. © 2021 Association for Computing Machinery.

关键词： Crowdsourcing

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Personalized Review Recommendation based on Implicit dimension mining

Personalized Review Recommendation based on Implicit dimensi...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Xu, Bei Xu, Yifan School of Computer Science Nanjing University of Posts and Telecommunications Nanjing210023 China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing210023 China

Users usually browse product reviews before buying products from e-commerce websites. Lots of e-commerce websites can recommend reviews. However, existing research on review recommendation mainly focuses on the general usefulness of reviews and ignores personalized and implicit requirements. To address the issue, we propose a Large language model driven Personalized Review Recommendation model based on Implicit dimension mining (PRR-LI). The model mines implicit dimensions from reviews and requirements, and encodes them in the form of "text + dimension". The experiments show that our model significantly outperforms other state-of-the-art textual models on the Amazon-MRHP dataset, with some of the metrics outperforming the state-of-the-art multimodal models. And we prove that encoding "text + dimension" is better than encoding "text" and "dimension" separately in review recommendation. © 2024 Association for Computational Linguistics.

关键词： Signal encoding

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Multi-source Infection Pattern Mining Algorithms over Moving Objects 25

Multi-source Infection Pattern Mining Algorithms over Moving...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Chen, Yu Dai, Hua Yang, Geng Chen, Yanli Nanjing University of Posts and Telecommunications School of Computer Science Nanjing210003 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing210003 China

ISBN: (纸本)9781665405270

Using the trajectory data of moving objects to analyze and study the infection mode of viruses or germs has practical application value. The definition of infection pattern in existing works only considers one-to-one infection mode rather than many-to-one mode, and thus some infection events could be ignored. This paper presents multi-source infection pattern mining algorithms oriented to moving objects for the first time. The multi-source infection event (MSIE) is defined. On the basis of MSIE, the multi-source infection pattern mining algorithm (MIPM) is proposed, which uses sliding window mechanism. The sliding window is used to record the set of candidate infection source that may infect each normal object at each time point, and then the determined infection events are mined. To improve performance, an optimized mining algorithm (MIPM+) based on the R-tree index is proposed, which can reduce the number of objects to be detected at each time point. The experimental results show that our proposed multi-source infection pattern mining algorithms can mine more potential infection events. © 2022 IEEE.

关键词： Decision trees

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Software Defect Prediction via Deep Belief Network

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Chinese Journal of Electronics 2019年第5期28卷 925-932页

作者： WEI Hua SHAN Chun HU Changzhen ZHANG Yu YU Xiao School of Computer Science and Technology Beijing Institute of Technology China Information Technology Security Evaluation Center Beijing Key Laboratory of Software Security Engineering Technology Beijing Institute of Technology School of Electrical and Information Engineering and Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture State Key Laboratory in China for Geomechanics and Deep Underground Engineering (Beijing) China University of Mining and Technology School of Computer Science and Technology Shandong University of Technology

Defect distribution prediction is a meaningful topic because software defects are the fundamental cause of many attacks and data loss. Building accurate prediction models can help developers find bugs and prioritize their testing efforts. Previous researches focus on exploring different machine learning algorithms based on the features that encode the characteristics of *** problem of data redundancy exists in software defect data set, which has great influence on prediction *** propose a defect distribution prediction model(Deep belief network prediction model, DBNPM), a system for detecting whether a program module contains defects. The key insight of DBNPM is Deep belief network(DBN)technology, which is an effective deep learning technique in image processing and natural language processing,whose features are similar to defects in source *** results show that DBNPM can efficiently extract and process the data characteristics of source program and the performance is better than Support vector machine(SVM), Locally linear embedding SVM(LLE-SVM), and Neighborhood preserving embedding SVM(NPE-SVM).

关键词： Defect prediction Software security Deep belief network(DBN)

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Recognition of Disaster Images Based on Self-Supervised Learning 7

Recognition of Disaster Images Based on Self-Supervised Lear...

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7th International Conference on Cloud Computing and big data Analytics, ICCCBDA 2022

作者： Wang, Linyong Wu, Ruiqi Li, Cen Zou, Zhiqiang Nanjing University of Posts and Telecommunications NanJing China College of Computer Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanJing China

ISBN: (纸本)9781665487115

Existing supervised deep learning model requires large amounts of labeled training data to learn new tasks. This is a limitation for many practical applications in disaster areas as well as in many other fields such as medical domain, where numerous labour and time are required to label datasets. According to our literature survey, there is few self-supervised learning model with high accuracy for image recognition about natural disasters. This paper proposed a self-supervised learning model for disaster image recognition based on MoCo model, called MoCo-Q, including image preprocessing, model training and transfer learning after fine-Tuning. After being trained by learning a couple of features of disaster images without artificial labels, the trained neural network is applied to downstream tasks, which achieves the recognition of a large number of disaster images at a high accuracy without the manual intervention and labeling. With the implement of the MoCo-Q, images of natural disasters can be quickly recognized without manual labeling. The work can provide scientific decision-making support for the government and it is of great significance for natural disaster assessment and disaster relief. Finally, experiments on real-world natural disaster datasets, CrisisMMD and ASONAM17, are performed, and we compared with the state-of-The-Art supervised method (ResNet50) that uses same datasets. The result presents that the accuracy of the MoCo-Q (91.56%) is 5.71% higher than that of the supervised learning model (85.85%). © 2022 IEEE.

关键词： Training Image recognition Neural networks Transfer learning Supervised learning Training data Manuals

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Distributed Image Retrieval Base on LSH Indexing on Spark 1st

Distributed Image Retrieval Base on LSH Indexing on Spark

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1st International Conference on big data and security, ICBDS 2019

作者： Hou, Zelei Huang, Chao Wu, Jiagao Liu, Linfeng School of Computer Nanjing University of Posts and Telecommunications NanjingJiangsu210023 China Jiangsu Key Laboratory of Big Data Security and Intelligent Processing NanjingJiangsu210023 China

ISBN: (纸本)9789811575297

With the advent of the era of big data, how to process massive image, video and other multimedia data timely and accurately has become a new challenge in related fields. Aiming at the computational bottleneck and inefficiency of traditional image content retrieval system, in this paper, a distributed image retrieval framework base on Location Sensitive Hash (LSH) indexing is proposed on Spark, which combines with the distributed storage and computing characteristics of Spark big data platform. Then, distributed K-means based Bag of Visual Word (BoVW) algorithm and LSH algorithm are proposed to build LSH index vectors on Spark in parallel. The experiment shows that the retrieval time of our framework can be reduced significantly, and both retrieval recall and precision are high compared with the traditional retrieval method. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Indexing (of information)

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