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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Ou, Xinyu Jin, Hai Wang, Sujuan Deng, Zhijun Zhong, Yuyi 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 School of Cyber Security and Computer Hebei University Shenzhen Huazhong University of Science and Technology Research Institute Department of Computer Science University of Texas at San Antonio

The automatic detection of software vulnerabilities is an important research problem. However, existing solutions to this problem rely on human experts to define features and often miss many vulnerabilities (i.e., incurring high false negative rate). In this paper, we initiate the study of using deep learning-based vulnerability detection to relieve human experts from the tedious and subjective task of manually defining features. Since deep learning is motivated to deal with problems that are very different from the problem of vulnerability detection, we need some guiding principles for applying deep learning to vulnerability detection. In particular, we need to find representations of software programs that are suitable for deep learning. For this purpose, we propose using code gadgets to represent programs and then transform them into vectors, where a code gadget is a number of (not necessarily consecutive) lines of code that are semantically related to each other. This leads to the design and implementation of a deep learning-based vulnerability detection system, called Vulnerability Deep Pecker (VulDeePecker). In order to evaluate VulDeePecker, we present the first vulnerability dataset for deep learning approaches. Experimental results show that VulDeePecker can achieve much fewer false negatives (with reasonable false positives) than other approaches. We further apply VulDeePecker to 3 software products (namely Xen, Seamonkey, and Libav) and detect 4 vulnerabilities, which are not reported in the National Vulnerability Database but were "silently" patched by the vendors when releasing later versions of these products;in contrast, these vulnerabilities are almost entirely missed by the other vulnerability detection systems we experimented with. Copyright © 2018, The Authors. All rights reserved.

关键词： Computer software

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Sysevr: A framework for using deep learning to detect software vulnerabilities

arXiv

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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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Scalab.e 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 scalab.e 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 scalab.lity handling growing amount of data. We have implemented a prototype of the scalab.e 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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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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Secure cryptographic functions via virtualization-based outsourced computing

Secure cryptographic functions via virtualization-based outs...

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作者： Qiang, Weizhong Zhang, Kang Dai, Weiqi Jin, Hai Services Computing Technology and System Lab and Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

Cryptographic functions, such as encryption/decryption libraries, are common and important tools for applications to enhance confidentiality of the data. However, these functions could be compromised by subtle attacks launched by untrusted operating system or other applications, and sensitive keys or cryptographic procedures could then be compromised. In this paper, based on virtualization technology, we propose a novel approach that outsources the cryptographic functions in one virtual machine (VM) into another dedicated VM, so that sensitive keys and the cryptographic procedures are only contained by this VM with specific purpose. We also propose a prototype, called cryptographic function assurance (CFA), to enhance the security of cryptographic functions. Taking OpenSSL as an example, CFA allows those applications that use OpenSSL library to transparently utilize CFA to protect the cryptographic functions. We present the detailed implementation, as well as the security analysis of CFA. We also give the performance evaluation for OpenSSL's interfaces and Apache httpd, to show the overhead caused by the integration of CFA. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： Virtualization

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Runtime-aware adaptive scheduling in stream processing

Runtime-aware adaptive scheduling in stream processing

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作者： Liu, Yuan Shi, Xuanhua Jin, Hai 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

Long-running stream applications usually share the same fundamental computational infrastructure. To improve the efficiency of data processing in stream processing systems, a data analysis operator could be partitioned into n parallel tasks. The partitioned tasks are usually deployed on m nodes coexisting with other application operators. Because the node performance can vary in unpredictable ways (i.e., (1) stream input rates may fluctuate and (2) computational resource availab.lity varies as other applications are affected), the nodes have different processing steps, and the slow node determines the operator performance. Hence, the tasks should be redistributed at runtime for stream applications to meet their strict latency requirements. Our key idea is to redistribute the tasks to the best node dynamically adaptive to resource or load fluctuations. In this paper, we present a runtime-aware adaptive schedule mechanism that aims at minimizing the operator processing latency and minimizing the latency difference between different nodes' tasks. We propose a new abstraction called performance cost ratio (PCR) that evaluates the node performance. The higher the node's PCR is, the less cost the node will pay for processing one tuple, and the more tasks should be deployed on it. In a scheduling, we first sort tasks descendingly by their loads and sort nodes by their PCR. Then we reassign the amount of computation according to the node's PCR to keep the node's PCR and its input rate the same or in similar proportion in all PCRs. The PCR-based quantitative algorithm applies itself to make tasks loads quantized to the processing capacity of nodes, move the minimum amount of operator's tasks, and keep the tasks local at the same time. We have implemented a runtime-aware adaptive scheduler as an extension to Storm and evaluated this strategy. We achieve the optimization goal using less computational resources. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wil

关键词： Scheduling

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