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SSRN 2023年

作者： Wang, Zeli Dai, Weiqi Li, Ming Choo, Kim-Kwang Raymond Zou, Deqing Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Key Laboratory of Cyberspace Big Data Intelligent Security Ministry of Education College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing40065 China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

Existing smart contract vulnerability identification approaches mainly focus on complete program detection. Consequently, lots of known potentially vulnerable locations need manual verification, which is energy-exhaustive and error-prone. Fuzzing is an effective way to detect vulnerabilities. However, current fuzzers cannot effectively probe these suspicious targets because they overlook two key factors: exploring all paths toward the targets and providing high-quality directed seed inputs. This paper presents a Directed vulnerability veriFier (DFier), elaborating effective transaction sequences with directed inputs for the fuzzer, to focus on analyzing target paths and automatically detect whether the given locations are vulnerable. Specifically, DFier fuses the effectiveness of static analysis and the efficiency of fuzzing. The static analysis helps locate target paths and generate directed inputs for the fuzzer under three heuristic strategies. The first prefers inputs with the larger possibility of triggering potential vulnerabilities based on target features. The second guides the value mutation to satisfy the path constraints. The third adequately leverages execution information to help find exploits faster. Extensive experiments demonstrate that DFier is effective in verifying contract security, compared with three existing contract fuzzers (i.e., contractFuzzer, sFuzz, and conFuzzius). And the performance losses are in an acceptable range. © 2023, The Authors. All rights reserved.

关键词： Smart contract

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MovCloud: A Cloud-enabled Framework to Analyse Movement Behaviors

MovCloud: A Cloud-enabled Framework to Analyse Movement Beha...

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IEEE International Conference on Cloud Computing Technology and Science

作者： Shreya Ghosh Soumya K Ghosh Rajkumar Buyya Department of Computer Science and Engineering Indian Institute of Technology Kharagpur India Indian Institute of Technology Kharagpur Kharagpur India Cloud Computing and Distributed Systems (CLOUDS) Lab The University of Melbourne Australia

Understanding human interests and intents from movement data are fundamental challenges for any location-based service. With the pervasiveness of sensor embedded smartphones and wireless networks and communication, the availability of spatio-temporal mobility trace (timestamped location information) is increasingly growing. Analysing these huge amount of mobility data is another major concern. This paper proposes a cloud-based framework named MovCloud to efficiently manage and analyse mobility data. Specifically, the framework presents a hierarchical indexing schema to store trajectory data in different spatio-temporal resolution, clusters the trajectories based on semantic movement behaviour instead of only raw latitude, longitude point and resolves mobility queries using MapReduce paradigm. MovCloud is implemented over Google Cloud Platform (GCP) and an extensive set of experiments on real-life data yield the effectiveness of the proposed framework. MovCloud has achieved ～ 28% better clustering accuracy and also executed three times faster than the baseline methods.

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Improving Output Bounds in the Stochastic Network Calculus Using Lyapunov’s Inequality

Improving Output Bounds in the Stochastic Network Calculus U...

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IFIP Networking Conference

作者： Paul Nikolaus Jens Schmitt Distributed Computer Systems (DISCO) Lab TU Kaiserslautern Germany

ISBN: (纸本)9781538662809;9783903176089

Giving tight estimates for output bounds is key to an accurate network analysis using the stochastic network calculus (SNC) framework. In order to upper bound the delay for a flow of interest in the network, one typically has to calculate output bounds of cross-traffic flows several times. Thus, an improvement in the output bound calculation pays off considerably. In this paper, we propose a new output bound calculation in the SNC framework by making use of Lyapunov's inequality. We prove the bound's validity and also show why it is always at least as accurate as the state-of-the-art method. Numerical evaluations demonstrate that even in small heterogeneous two server topologies, our approach can improve a delay bounds' violation probability by a factor of 340. For a set of randomly generated parameters, the bound is still decreased by a factor of 1.33 on average. Furthermore, our approach can be easily integrated in existing end-to-end analyses.

关键词： Delays Servers Calculus Topology Algebra Standards Upper bound

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The Deterministic Network Calculus Analysis: Reliability Insights and Performance Improvements

The Deterministic Network Calculus Analysis: Reliability Ins...

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IEEE International Workshop on computer-Aided Modeling, Analysis, and Design of Communication Links and Networks, CAMAD

作者： Alexander Scheffler Markus Fögen Steffen Bondorf Distributed Computer Systems (DISCO) Lab TU Kaiserslautern Germany

ISBN: (纸本)9781538661529

The cost of delay analysis increases fast with size and complexity of a network. Therefore, many recent research efforts in Deterministic Network Calculus (DNC) focused on the tradeoff between accuracy and cost of its delay-bounding analyses. In this paper, we present insights on reliable, reproducible and performance on both branches of DNC, algebraic analysis and optimization, as well as the tools employed by them. We reveal circumstances causing problems for reliability and reproducibility of DNC's optimization analysis and we investigate the potential to improve computational performance of algebraic DNC. To that end, we present theoretical background on the topic of parallelizing the DNC analyses and an implementation in the open-source DiscoDNC tool. With our proposed approach, we achieve a speedup of analysis times of one order of magnitude.

关键词： Delays Complexity theory Reliability Java Analytical models computer network reliability Tools

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ABAC Based Online Collaborations in the Cloud 1st

ABAC Based Online Collaborations in the Cloud

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1st International Conference on Emerging Technologies for Developing Countries, AFRICATEK 2017

作者： Madani, Mohamed Amine Erradi, Mohammed Benkaouz, Yahya Networking and Distributed Systems Research Group SIME Lab ENSIAS Mohammed V University in Rabat Rabat Morocco LCS Department of Computer Science FSR Mohammed V University in Rabat Rabat Morocco

ISBN: (纸本)9783319678368

Nowadays sharing data among organizations plays an important role for their collaboration. During collaborations, the organizations need to access shared information while respecting the access control constraints. In addition, most organizations rely on cloud based solutions to store their data (e.g. openstack). In such platform, data access is regulated by Access Control Lists (ACLs). ACL defines static access rules. It assumes the knowledge of the whole set of users and possible access requests. This make ACL unusable in collaborative context due to the dynamic nature of collaborative sessions. In this paper, we consider ABAC, a flexible and fine-grained model, as an access control model for cloud-based collaborations to overcome the ACL limitations. We provide an architecture that integrate ABAC in the storage level of a cloud platform. © 2018, ICST Institute for computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Digital storage

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Geo-Cloudlet: Time and Power Efficient Geospatial Query Resolution using Cloudlet

Geo-Cloudlet: Time and Power Efficient Geospatial Query Reso...

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International Conference on Advanced Computing, ICAC

作者： Jaydeep Das Anwesha Mukherjee Soumya K. Ghosh Rajkumar Buyya Advanced Technology Development Centre Indian Institute of Technology Kharagpur West Bengal India Department of Computer Science Mahishadal Raj College Purba Medinipur West Bengal India Department of Computer Science and Engineering Indian Institute of Technology Kharagpur West Bengal India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne VIC Australia

ISBN: (数字)9781728152868

ISBN: (纸本)9781728152875

Geospatial data analysis is an emerging area of research today due to the potential to enable varied location-aware services. The existing centralized cloud-based analysis becomes time and computing-intensive for huge amount of geospatial data processing. This paper addresses the challenge of time and power-efficiency in QoS-aware geospatial query resolution. We propose a cloudlet based hierarchical paradigm, namely Geo-Cloudlet, where the cloudlets contain the geospatial data of the districts. The state and national level geospatial data are stored inside the state cloud and country cloud respectively. The query resolution is performed by either the cloudlet or by the state cloud or country cloud depending upon the geographical region related to the query. The experimental analysis illustrates that the proposed architecture Geo-Cloudlet reduces the latency up to 61.3% and power consumption up to 61.1% over the use of only remote cloud servers for geospatial query resolution.

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STLDAS: Secure Two Level Deduplication and Auditing of Shared Data in Cloud

STLDAS: Secure Two Level Deduplication and Auditing of Share...

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IEEE Region 10 Symposium

作者： C M Geeta G Mithila Lakshmi R G Shreyas Raju S Raghavendra Rajkumar Buyya K R Venugopal S S Iyengar L M Patnaik Department of Computer Science and Engineering University Visvesvaraya College of Engineering Bangalore University India Cloud Computing and Distributed Systems (CLOUDS) Lab School of Computing and Information Systems The University of Melbourne Australia Bangalore University Bengaluru India Department of Computer Science and Engineering Florida International University USA INSA National Institute of Advanced Studies Indian Institute of Science Campus Bangalore India

With the cloud repository service furnished by the cloud computing, users can comfortably arrange themselves as a cluster and distribute information effectively. In order to empower public verifier to audit the distributed information, clients in the cluster need to Figure out signatures on complete chunks of collaborative information. Every client in the cluster modifies and signs his respective chunks, and deploys in the cloud server. Hence specific chunks of shared information are normally signed by specific clients. If anyone of the customers' is found malicious, he is immediately repudiated from the cluster. The prevailing clients in the cluster are permitted to re-sign the chunks that were earlier signed by this eliminated client. This approach is inefficient due to the massive amount of collaborative information in the cloud. By exploiting the approach of proxy re-signatures, the CSP is acknowledged to re-sign chunks in support of the prevailing clients during customer repudiation. When many clients deploy the same information to the cloud repository, repository space has identical copies, hence deduplication technology is usually utilized to lower the capacity and bandwidth prerequisites of the utilities by removing repetitious information and hoarding only an original replica of them. In order to assimilate both data honesty and deduplication in cloud, we present a novel Secure Two Level Deduplication and Auditing of Shared Data in Cloud (STLDAS) mechanism. Experimental results show that our mechanism achieves secure deduplication and appreciable improvement in tag generation.

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Multi-view Vector-valued Manifold Regularization for Multi-label Image Classification

arXiv

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arXiv 2019年

作者： Luo, Yong Tao, Dacheng Xu, Chang Xu, Chao Liu, Hong Wen, Yonggang School of Electronics Engineering and Computer Science Peking University Beijing100871 China Centre for Quantum Computation and Intelligent Systems University of Technology Sydney Jones Street Ultimo SydneyNSW2007 Australia Engineering Lab on Intelligent Perception for Internet of Things Shenzhen Graduate School Peking University China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Singapore

—In computer vision, image datasets used for classification are naturally associated with multiple labels and comprised of multiple views, because each image may contain several objects (e.g. pedestrian, bicycle and tree) and is properly characterized by multiple visual features (e.g. color, texture and shape). Currently available tools ignore either the label relationship or the view complementary. Motivated by the success of the vector-valued function that constructs matrix-valued kernels to explore the multi-label structure in the output space, we introduce multi-view vector-valued manifold regularization (MV3MR) to integrate multiple features. MV3MR exploits the complementary property of different features and discovers the intrinsic local geometry of the compact support shared by different features under the theme of manifold regularization. We conducted extensive experiments on two challenging, but popular datasets, PASCAL VOC’ 07 (VOC) and MIR Flickr (MIR), and validated the effectiveness of the proposed MV3MR for image classification. Copyright © 2019, The Authors. All rights reserved.

关键词： Image classification

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Correction to: A compressed matrix sequence method for solving normal equations of bundle adjustment

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Machine Vision and Applications 2021年第4期32卷 1-1页

作者： Peng, Jiaxin Liu, Jie Wei, Hua Laboratory of Software Engineering for Complex Systems School of Computer Science National University of Defense Technology Changsha China Parallel and Distributed Processing Laboratory School of Computer Science National University of Defense Technology Changsha China Algorithm Lab Changsha Morale Network Technology Co. Ltd. Changsha China Water and Land Resources Research Center of the Middle Reaches of Yangtze River School of Resources Environment Science and Engineering Hubei University of Science and Technology Xianning China

A correction to this paper has been published: https://***/10.1007/s00138-021-01222-5

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Integrating Fractional Brownian Motion Arrivals into the Statistical Network Calculus

Integrating Fractional Brownian Motion Arrivals into the Sta...

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International Teletraffic Congress, ITC 21

作者： Paul Nikolaus Sebastian Henningsen Michael Beck Jens Schmitt TU Kaiserslautern Distributed Computer Systems (DISCO) Lab Germany Humboldt-Univcrsirär zu Berlin Germany University of Manitoba Canada

Stochastic network calculus (SNC) is a versatile framework to derive probabilistic performance bounds. Recently, it was proposed in [1] to replace the typical a priori assumptions on arrival processes with measurement observations and to incorporate the corresponding statistical uncertainty into calculation of the bounds. This so-called statistical network calculus (StatNC) opens the door for many applications with limited traffic information. However, the important traffic class of self-similar processes such as fractional Brownian Motion (fBm) was left open in [1], thus, e.g., depriving the usage of the StatNC for Internet traffic. In this work, we close this gap by integrating fBm arrivals into the StatNC. To this end, we analyze the impact imposed by the uncertainty on the backlog bound and show in numerical evaluations that the additional inaccuracy is only of moderate size.

关键词： Calculus Brownian motion Upper bound Uncertainty Stochastic processes Estimation Mathematical model

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