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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Jin, Hai Zhu, Yawei Chen, Zhaoxuan Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by many vulnerabilities reported on a daily basis. This calls for machine learning methods to automate vulnerability detection. Deep learning is attractive for this purpose because it does not require human experts to manually define features. Despite the tremendous success of deep learning in other domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability Database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the products. Copyright © 2018, The Authors. All rights reserved.

关键词： Syntactics

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Theory and methodology of research on cloud security

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Science China(Information Sciences) 2016年第5期59卷 15-17页

作者： Hai JIN Weiqi DAI Deqing ZOU Services Computing Technology and System Lab Big Data Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

Cloud computing is a model for enabling ubiquitous,convenient,on-demand access to a shared pool of configurable computing *** to Gartner’s survey,cloud computing has been at the head of a list of the top ten IT strategic technologies for some *** computing is currently undergoing vigorous development and promotion by many leading Chinese and foreign enterprises,and it has become the basis for a new

关键词： TPM Theory and methodology of research on cloud security

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Exploring the Impact of Processing Guarantees on Performance of Stream Data Processing

Exploring the Impact of Processing Guarantees on Performance...

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2017 17th IEEE International Conference on Communication technology (ICCT 2017)

作者： Syed Muhammad Abrar Akber Changfu Lin Hanhua Chen Fan Zhang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology

Processing semantics ensure tuple processing guarantees during system recovery from failures. It determines whether particular tuples are re-processed or not upon system recovery. Processing semantics facilitate to maintain real-time response as well as correctness of results in stream data processing. Existing literature is in real deficit to provide sufficient resources exclusively on processing semantics. This work focuses on reviewing processing semantics in stream data processing *** article aims at exploring the impact of processing guaran-tees on performance of stream data processing. We summarize current research directions to guarantee processing semantics,based on prominent stream processing engines. Furthermore,we compare throughput for a couple of representative stream processing engines under all possible processing *** experiments show that at-most once processing guarantee provides the highest throughput among other guarantees. Lastly,challenges for further research are also discussed in this paper.

关键词： stream data processing processing semantic processing guarantee at-least once at-most once exactly-once

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A Protective Mechanism for the Access Control system in the Virtual Domain

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China Communications 2016年第11期13卷 129-142页

作者： Jinan Shen Deqing Zou Hai Jin Kai Yang Bin Yuan Weiming Li Services Computing Technology and System Laboratory Cluster and Grid Computing LaboratorySchool of Computing Science and TechnologyHuazhong University of Science and Technology School of Information Engineering Hubei University for Nationalities

In traditional framework,mandatory access control(MAC) system and malicious software are run in kernel mode. Malicious software can stop MAC systems to be started and make it do invalid. This problem cannot be solved under the traditional framework if the operating system(OS) is comprised since malwares are running in ring 0 level. In this paper,we propose a novel way to use hypervisors to protect kernel integrity and the access control system in commodity operating systems. We separate the access control system into three parts: policy management(PM),security server(SS) and policy enforcement(PE). Policy management and the security server reside in the security domain to protect them against malware and the isolation feather of the hypervisor can protect them from attacks. We add an access vector cache(AVC) between SS and PE in the guest OS,in order to speed up communication between the guest OS and the security domain. The policy enforcement module is retained in the guest OS for performance. The security of AVC and PE can be ensured by using a memory protection mechanism. The goal of protecting the OS kernel is to ensure the security of the execution path. We implementthe system by a modified Xen hypervisor. The result shows that we can secure the security of the access control system in the guest OS with no overhead compared with modules in the latter. Our system offers a centralized security policy for virtual domains in virtual machine ***: hypervisor; virtualization; memo-

关键词： hypervisor virtualization memory protection guest OS access control system

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Scalable Hash Ripple Join on Spark

Scalable Hash Ripple Join on Spark

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Hao Liu Jiang Xiao Fengchao Peng The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Services Computing Technology and System Lab Cluster and Grid Computing Lab Shool of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (纸本)9781538632086

Hash Ripple join is an online aggregation algorithm that can rapidly give good approximate join results increases with the progress of the join operation and converges to the real result when the join finishes. Luo et al. proposed a parallel hash ripple join (PHRJ) that runs in a distributed setting. However, the PHRJ has two drawbacks when handling large-scale data: 1) PHRJ updates approximate results in fine grain which induces extra communication cost in a distributed environment 2) When data is out of memory, PHRJ cannot provide unbiased approximate result. In this thesis, a scalable hash ripple join is proposed that 1) runs on a distributed framework that can process distributed data in coarse-grain to speed up the join performance; 2) continuously gives unbiased and consistent approximate join results even in the presence of memory overflow; and 3) has good scalability handling growing amount of data. We have implemented a prototype of the scalable hash ripple join (SHRJ) algorithm on Spark. Experiment results show that SHRJ can give good approximate join result while taking less than 10% of the time of Spark's own join operator.

关键词： Sparks Distributed databases Approximation algorithms clustering algorithms Indexes Standards Computer science

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Corrigendum to “Fine-Grained Control-Flow Integrity Based on Points-to Analysis for CPS”

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Security and Communication Networks 2018年第1期2018卷

作者： Weizhong Qiang Shizhen Wang Hai Jin Jiangying Zhong Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 ***

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Core maintenance in dynamic graphs: A parallel approach based on matching

arXiv

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arXiv 2017年

作者： Wang, Na Yu, Dongxiao Jin, Hai Hua, Qiang-Sheng Shi, Xuanhua Xie, Xia Services Computing Technology and System Lab Big Data Technology and System Lab Clusters and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

—The core number of a vertex is a basic index depicting cohesiveness of a graph, and has been widely used in large-scale graph analytics. In this paper, we study the update of core numbers of vertices in dynamic graphs with edge insertions/deletions, which is known as the core maintenance problem. Different from previous approaches that just focus on the case of single-edge insertion/deletion and sequentially handle the edges when multiple edges are inserted/deleted, we investigate the parallelism in the core maintenance procedure. Specifically, we show that if the inserted/deleted edges constitute a matching, the core number update with respect to each inserted/deleted edge can be handled in parallel. Based on this key observation, we propose parallel algorithms for core maintenance in both cases of edge insertions and deletions. We conduct extensive experiments to evaluate the efficiency, stability, parallelism and scalability of our algorithms on different types of real-world and synthetic graphs. Comparing with sequential approaches, our algorithms can improve the core maintenance efficiency significantly. Copyright © 2017, The Authors. All rights reserved.

关键词： Maintenance

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LaLDPC: Latency-aware LDPC for read performance improvement of solid state drives 33

LaLDPC: Latency-aware LDPC for read performance improvement ...

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33rd International Conference on Massive Storage systems and technology, MSST 2017

作者： Du, Yajuan Zou, Deqing Li, Qiao Shi, Liang Jin, Hai Xuey, Chun Jason Service Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technol. and Syst. Lab Sch. of Comp. Sci. and Technology Huazhong Univ. of Sci. and Technol. Wuhan430074 China Department of Computer Science City University of Hong Kong Kowloon Hong Kong College of Computer Science Chongqing University Chongqing China

High-density Solid State Drives (SSDs) have to use Low-Density Parity-Check (LDPC) codes to store data reliably. Current LDPC implementations apply multiple read-retry steps to find the appropriate set of multiple Reference Voltage (RVs) for successful decoding. These appropriate RVs, number of which is represented by read level, cannot be obtained in advance, instead, an iterative read-retry LDPC process happens that starts with the first read level with the smallest number of RVs and then increases the level until that the data have been successfully decoded. Only read-retry with the last read level, called the appropriate read level, is necessary. Read latency can be improved by avoiding read-retries. This becomes particularly important in the late stage of SSD life when high read levels are needed. To avoid unnecessary read-retries, this paper proposes Latency-aware LDPC (LaLDPC). We study read level characteristics along with data retention and observe that a single read level stays appropriate for a long time. During this time, all reads in the same page have probably the same appropriate read level, which we call the temporal read level locality. Using this value, LaLDPC estimates the starting read voltage level, which we store in the flash translation layer of the controller. As an example, we show how to integrate LaLDPC into the Demand-based Flash Translation Layer (DFTL). A new cache eviction algorithm is also proposed to leave entries with high read levels in the cache as long as possible. Experimental results show that LaLDPC saves 56% of read retries and improves SSD read performance by 18% on average compared with the existing LDPC method. © 33rd International Conference on Massive Storage systems and technology, MSST 2017. All rights reserved.

关键词： Forward error correction

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Maximizing the Profit of Cloud Broker with Priority Aware Pricing

Maximizing the Profit of Cloud Broker with Priority Aware Pr...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Xinhou Wang Song Wu Kezhi Wang Sheng Di Hai Jin Kun Yang Shumao Ou Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China Northumbria University UK Argonne National Laboratory USA University of Essex UK Oxford Brookes University UK

ISBN: (纸本)9781538632086

A practical problem facing Infrastructure-as-a-Service (IaaS) cloud users is how to minimize their costs by choosing different pricing options based on their own demands. Recently, cloud brokerage service is introduced to tackle this problem. But due to the perishability of cloud resources, there still exists a large amount of idle resource waste during the reservation period of reserved instances. This idle resource waste problem is challenging cloud broker when buying reserved instances to accommodate users' job requests. To solve this challenge, we find that cloud users always have low priority jobs (e.g., non latency-sensitive jobs) which can be delayed to utilize these idle resources. With considering the priority of jobs, two problems need to be solved. First, how can cloud broker leverage jobs' priorities to reserve resources for profit maximization? Second, how to fairly price users' job requests with different priorities when previous studies either adopt pricing schemes from IaaS clouds or just ignore the pricing issue. To solve these problems, we first design a fair and priority aware pricing scheme, PriorityPricing, for the broker which charges users with different prices based on priorities. Then we propose three dynamic algorithms for the broker to make resource reservations with the objective of maximizing its profit. Experiments show that the broker's profit can be increased up to 2.5× than that without considering priority for offline algorithm, and 3.7× for online algorithm.

关键词： Cloud computing Pricing Google Heuristic algorithms Monitoring Virtual machining

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Efficient multiple-reference temporal error concealment algorithm based on H.264/AVC 2nd

Efficient multiple-reference temporal error concealment algo...

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2nd International Conference on Human Centered computing, HCC 2016

作者： Yu, Chen Cheng, Chao Jin, Hai Zhou, Jingli Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9783319318530

Temporal error concealment at the video communication receiver recovers damaged blocks using temporal information redundancy. To enhance the quality of reconstructed image, a multiple-reference temporal error concealment algorithm based on H.264/AVC was proposed. Firstly, a pre-concealment is implemented to improve video frame;then the fractional pixel boundary matching algorithm and search algorithm are used to evaluate the candidate motion vectors from a number of reference frames, and the motion vector (MV) giving the minimum boundary matching distortion (MBMD) is selected as optimal motion vector;finally it is used to recover the damaged block and the video frame quality can be improved further. For various video sequences and different macro-block loss ratio, experimental results show that the proposed algorithm is significantly better than traditional temporal error concealment algorithms such as BMA and BM from both objectively and subjectively. At the loss ratio of 10 %, improvements of the proposed algorithm are more than 0.8~3.7 dB compared to that of other algorithms. © Springer International Publishing Switzerland 2016.

关键词： Image enhancement

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