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International Conference on Mobile Ad-hoc and Sensor Networks, MSN

作者： Yuxiang Liu Huichuwu Li Jiang Xiao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (数字)9781728152127

ISBN: (纸本)9781728152134

Fingerprinting-based indoor localization via WiFi has achieved a great breakthrough in the past decade. However, it suffers from an inherent problem that the localization accuracy declines sharply over time due to the dynamic environment and unstable WiFi devices. Researchers have designed many methods to update the localization model, e.g., crowdsourcing-based model updating, in order to maintain the localization accuracy. Unfortunately, they have not taken the privacy into consideration during the updating process. This will lead to a threat that the eavesdroppers could guess the location providers' private information according to the updating model. For the goal of maintaining the localization accuracy without the risk of privacy breaching, we proposed FLoc, a fingerprinting-based indoor localization system which updates the localization model via a federated learning framework. In FLoc, every provider maintains a local localization model in their own device. They will regularly encrypt the updating parameters and share them to a common model server. At the model server, it aggregates the encrypted information of the local models to maintain a general model, which will be sent back to the local devices for next updating iteration. We evaluate FLoc in an APs unknown lab.ratory corridor. The experiment results show that FLoc has a comparable localization performance. Moreover, it can successfully protect the providers' privacy, since the information transferred is all encrypted.

关键词： Mobile handsets Privacy Fingerprint recognition Servers Wireless fidelity Databases Computational modeling

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Minimizing Overheads of Checkpoints in Distributed Stream Processing systems 7

Minimizing Overheads of Checkpoints in Distributed Stream Pr...

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7th IEEE International Conference on Cloud Networking, CloudNet 2018

作者： Muhammad Abrar Akber, Syed Chen, Hanhua Wang, Yonghui Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Cluster and Grid Computing Lab Services Computing Technology and System Lab Big Data Technology and System Lab Wuhan430074 China

ISBN: (纸本)9781538668313

Failure occurrence in large-scale systems is inevitable, which makes the resilience a key challenge for modern systems. Checkpoints with rollback recovery is a well-known approach to provide fault tolerance in distributed systems. The checkpoint based fault tolerance approach periodically persists the application state to reliable storage, which serves as a recovery point in case of failure. These periodic checkpoints are not inline with the failure rate of the systems as many studies conclude that failure occurrence is not periodic. The optimal size of checkpoint interval is a crucial decision, which directly determines the checkpoint overheads. To minimize the checkpoint overheads, we propose to reduce the number of checkpoints during the application execution. We suggest reducing the number of checkpoints by successively increasing the checkpoint intervals. We consider the failure probability of the underlying infrastructure and iteratively increase the checkpoint intervals. The proposed checkpoint approach tailors the checkpoint initializing based on the failure probability. If failure probability is low, it increases the checkpoint interval, and eventually reduces the total number of checkpoints triggered during application timespan. Reducing the total number of checkpoints during application execution results in decreasing the checkpoint overheads. The experiment results show that the proposed checkpoint policy considerably reduces the checkpoint overheads as compared to periodic checkpoints. © 2018 IEEE.

关键词： Fault tolerance

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Hybrid loss guided densely connected convolutional neural network for Ischemic Stroke Lesion segmentation

Hybrid loss guided densely connected convolutional neural ne...

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International Conference for Convergence of technology (I2CT)

作者： Saqib Qamar Hai Jin Ran Zheng Mohd Faizan Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Department of Electronics Aligarh Muslim University India

ISBN: (数字)9781538680759

ISBN: (纸本)9781538680766

The exact position of dead tissue in ischemic stroke lesions plays an important role to cure the life-threatening condition. However, this issue stays difficult caused by variation of ischemic strokes such as shape and location. Fully convolutional neural networks (FCN) have great potential for semantic image segmentation. Recently, UNet based CNN architectures have secured outstanding performance in the application of medical imaging. The primary problem is data imbalanced in the application of such networks which are very common in medical data application such as lesion applications where non-lesion voxels are higher than lesion voxels. Data imbalance generates low recall and high precision on the prediction of trained networks. Biases towards the particular class are not suitable for many medical works where false positives are less than false negatives. A lot of approaches are developed to tackle this issue including balanced sampling, sample re-weighting, and recently focal loss and similarity loss functions. In this paper, we proposed hyper densely connected network along with hybrid loss functions for ischemic stroke lesions segmentation. The densely connected network exploits the contextual information of multi-modalities, and hybrid loss function uses to overcome the data imbalance. We evaluate our performance on ISLES dataset. Our approach performs well as compared to other existing methods.

关键词： Lesions Image segmentation Magnetic resonance imaging Biomedical imaging Convolutional neural networks Training Computer architecture

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VulDeeLocator: A deep learning-based fine-grained vulnerability detector

arXiv

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arXiv 2020年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Chen, Zhaoxuan Zhu, Yawei Jin, Hai The National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Cyber Security and Computer Hebei University Baoding071002 China The Department of Computer Science University of Colorado Colorado Springs CO80918 United States University of Texas San Antonio United States

Automatically detecting software vulnerabilities is an important problem that has attracted much attention from the academic research community. However, existing vulnerability detectors still cannot achieve the vulnerability detection capability and the locating precision that would warrant their adoption for real-world use. In this paper, we present a vulnerability detector that can simultaneously achieve a high detection capability and a high locating precision, dubbed Vulnerability Deep learning-based Locator (VulDeeLocator). In the course of designing VulDeeLocator, we encounter difficulties including how to accommodate semantic relations between the definitions of types as well as macros and their uses across files, how to accommodate accurate control flows and variable define-use relations, and how to achieve high locating precision. We solve these difficulties by using two innovative ideas: (i) leveraging intermediate code to accommodate extra semantic information, and (ii) using the notion of granularity refinement to pin down locations of vulnerabilities. When applied to 200 files randomly selected from three real-world software products, VulDeeLocator detects 18 confirmed vulnerabilities (i.e., true-positives). Among them, 16 vulnerabilities correspond to known vulnerabilities;the other two are not reported in the National Vulnerability Database (NVD) but have been "silently" patched by the vendor of Libav when releasing newer versions. Copyright © 2020, The Authors. All rights reserved.

关键词： Location

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Modeling Distributed Stream Processing systems Under Heavy Workload

Modeling Distributed Stream Processing Systems Under Heavy W...

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International Conference on Cyberworlds

作者： Muhammad Mudassar Qureshi Hanhua Chen Hai Jin Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Cluster and Grid Computing Lab Services Computing Technology and System Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Big data applications play a significant role in diverse fields. Distributed Stream Processing Engines (DSPEs) are widely used to support real time applications efficiently. Partitioning algorithms are used to partition data streams into multiple nodes to process in parallel to gain efficient performance. Aggregation cost is an important factor when process stateful streaming applications using such partitioning algorithms because it plays an important role on performance when final result is being produced in stateful streaming applications. However, impact of aggregation cost in stream processing is not discussed comprehensively in existing literature. We use performance modeling to identify the importance of aggregation cost when workload is high. We implement performance model on a multi-node cluster to predict the same behavior as on single resource performance model. We demonstrate that stateful streaming applications need more resources as compare to stateless applications when workload is high and both stateful and stateless applications are running in the same DSPE. Experiments results show that a stateful streaming application needs more resources compared to a stateless streaming application when both applications are running on the same DSPE when the workload is high. Further experiment results show that the performance modeling may be helpful to predict maximum workload that can be process on a DSPE and increase in parallelism level is not guaranteed to increase the performance of streaming applications.

关键词： Partitioning algorithms Data models Predictive models Heuristic algorithms Big Data applications Throughput

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A survey on graph processing accelerators: Challenges and opportunities

arXiv

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arXiv 2019年

作者： Gui, Chuang-Yi Zheng, Long He, Bing-Sheng Liu, Cheng Chen, Xin-Yu Liao, Xiao-Fei Jin, Hai National Engineering Research Center for Big Data Technology and System 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 Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computing National University of Singapore 117418 Singapore

Graph is a well known data structure to represent the associated relationships in a variety of applications, e.g., data science and machine learning. Despite a wealth of existing efforts on developing graph processing systems for improving the performance and/or energy efficiency on traditional architectures, dedicated hardware solutions, also referred to as graph processing accelerators, are essential and emerging to provide the benefits significantly beyond those pure software solutions can offer. In this paper, we conduct a systematical survey regarding the design and implementation of graph processing accelerator. Specifically, we review the relevant techniques in three core components toward a graph processing accelerator: preprocessing, parallel graph computation and runtime scheduling. We also examine the benchmarks and results in existing studies for evaluating a graph processing accelerator. Interestingly, we find that there is not an absolute winner for all three aspects in graph acceleration due to the diverse characteristics of graph processing and complexity of hardware configurations. We finially present to discuss several challenges in details, and to further explore the opportunities for the future research. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy efficiency

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CNLoc: Channel state information assisted indoor WLAN localization using nomadic access points 1

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15th IFIP International Conference on Network and Parallel computing, NPC 2018

作者： Xiao, Jiang Li, Huichuwu Li, He 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 Department of Information and Electronic Engineering Muroran Institute of Technology MuroranHokkaido Japan

ISBN: (数字)9783030056773

ISBN: (纸本)9783030056766

Wireless local area network (WLAN) based indoor localization is expanding its fast-paced adoption to facilitate a variety of indoor location-based services (ILBS). Unfortunately, the performance of current WLAN localization systems relying on fixed access points (APs) deployment is constrained by the spatial localizability variance (SLV) problem that different locations may exhibit significantly distinct localization resolution. Prior approaches tackle this problem through nomadic APs with favorable mobility to dynamically adjust the network topology. However, the lack of prior knowledge of nomadic AP’s position has been a challenge for location distinction and will lead to prohibitive performance degradation. In this paper, we propose and develop CNLoc, a novel CSI-based (Channel State Information) indoor WLAN localization framework to overcome the location uncertainty of nomadic APs. Our implementation and evaluation show that CNLoc can improve the accuracy with unknown location information of nomadic APs. We also discuss some open issues and new possibilities in future nomadic AP based indoor localization. © IFIP International Federation for Information Processing 2018.

关键词： Channel state information

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Saudi cloud infrastructure: a security analysis

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Science China(Information Sciences) 2017年第12期60卷 152-164页

作者： Wahid RAJEH Hai JIN Deqing ZOU Services Computing Technology and System Lab Big Data Technology and System LabCluster and Grid Computing Lab School of Computer Science and TechnologyHuazhong University of Science and Technology School of Computers and Information Technology University of Tabuk

The growing demand and dependence upon cloud services have garnered an increasing level of threat to user data and security. Some of such critical web and cloud platforms have become constant targets for persistent malicious attacks that attempt to breach security protocol and access user data and information in an unauthorized manner. While some of such security compromises may result from insider data and access leaks, a substantial proportion continues to remain attributed to security flaws that may exist within the core web technologies with which such critical infrastructure and services are developed. This paper explores the direct impact and significance of security in the Software Development Life Cycle(SDLC) through a case study that covers some 70 public domain web and cloud platforms within Saudi Arabia. Additionally, the major sources of security vulnerabilities within the target platforms as well as the major factors that drive and influence them are presented and discussed through experimental evaluation. The paper reports some of the core sources of security flaws within such critical infrastructure by implementation with automated security auditing and manual static code analysis. The work also proposes some effective approaches, both automated and manual, through which security can be ensured through-out the SDLC and safeguard user data integrity within the cloud.

关键词： cloud security vulnerability detection web security Saudi infrastructure cloud service

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Privacy preserving search over multiple servers

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International Journal of Autonomous and Adaptive Communications systems 2017年第4期10卷 426-442页

作者： Ibrahim, Ayad Jin, Hai Yassin, Ali A. Zou, Deqing Cluster and Grid Computing Lab Services Computing Technology and System Lab Huazhong University of Science and Technology Wuhan430074 China

Search over encrypted data is a hot topic. In this paper, we propose a secure scheme for searching the encrypted servers. Such scheme enables the authorised user to search multiple servers with multi-keyword queries and retrieve the most relevant documents in a descending order with respect to their relevance to the query, without compromising the privacy of his query and the contents of documents he retrieved. In such a setting, each data owner builds his own searchable index, and associates with each document in that index its weight score, which facilitates document ranking. To protect the privacy of these weights, we utilise the privacy preserving mapping (PPM) primitive, while preserving their capability to perform the ranking process. We have conducted several empirical analyses on a real dataset to demonstrate the performance of our proposed scheme. © 2017 Inderscience Enterprises Ltd.

关键词： Search engines

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MalScan: Fast Market-Wide Mobile Malware Scanning by Social-Network Centrality Analysis

MalScan: Fast Market-Wide Mobile Malware Scanning by Social-...

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IEEE International Conference on Automated Software Engineering (ASE)

作者： Yueming Wu Xiaodi Li Deqing Zou Wei Yang Xin Zhang Hai Jin Cluster and Grid Computing Lab Services Computing Technology and System Lab National Engineering Research Center for Big Data Technology and System School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China University of Texas at Dallas Shenzhen Huazhong University of Science and Technology Research Institute School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Malware scanning of an app market is expected to be scalab.e and effective. However, existing approaches use either syntax-based features which can be evaded by transformation attacks or semantic-based features which are usually extracted by performing expensive program analysis. Therefor, in this paper, we propose a lightweight graph-based approach to perform Android malware detection. Instead of traditional heavyweight static analysis, we treat function call graphs of apps as social networks and perform social-network-based centrality analysis to represent the semantic features of the graphs. Our key insight is that centrality provides a succinct and fault-tolerant representation of graph semantics, especially for graphs with certain amount of inaccurate information (e.g., inaccurate call graphs). We implement a prototype system, MalScan, and evaluate it on datasets of 15,285 benign samples and 15,430 malicious samples. Experimental results show that MalScan is capable of detecting Android malware with up to 98% accuracy under one second which is more than 100 times faster than two state-of-the-art approaches, namely MaMaDroid and Drebin. We also demonstrate the feasibility of MalScan on market-wide malware scanning by performing a statistical study on over 3 million apps. Finally, in a corpus of dataset collected from Google-Play app market, MalScan is able to identify 18 zero-day malware including malware samples that can evade detection of existing tools.

关键词： Malware Social networking (online) Feature extraction Semantics Analysis of variance Static analysis Robustness

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