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Saudi cloud infrastructure: a security analysis

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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POSTER: Layrub: Layer-centric GPU memory reuse and data migration in extreme-scale deep learning systems

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ACM SIGPLAN Notices 2018年第1期53卷 405-406页

作者： Liu, Bo Jiang, Wenbin Jin, Hai Shi, Xuanhua Ma, Yang 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)9781450349116

Growing accuracy and robustness of Deep Neural Networks (DNN) models are accompanied by growing model capacity (going deeper or wider). However, high memory requirements of those models make it difficult to execute the training process in one GPU. To address it, we first identify the memory usage characteristics for deep and wide convolutional networks, and demonstrate the opportunities of memory reuse on both intra-layer and inter-layer levels. We then present Layrub, a runtime data placement strategy that orchestrates the execution of training process. It achieves layer-centric reuse to reduce memory consumption for extreme-scale deep learning that cannot be run on one single GPU. © 2018 Owner/Author.

关键词： Deep neural networks

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Communication-Efficient and Privacy-Preserving Data Aggregation without Trusted Authority

Communication-Efficient and Privacy-Preserving Data Aggregat...

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IEEE Annual Joint Conference: INFOCOM, IEEE Computer and Communications Societies

作者： Xuhui Gong Qiang-Sheng Hua Lixiang Qian Dongxiao Yu Hai Jin Services Computing Technology and System Lab/Big Data Technology and System Lab/Cluster and Grid Computing Lab Huazhong University of Science and Technology P. R. China

ISBN: (纸本)9781538641293

Privacy-preserving data aggregation has been extensively studied in the past decades. However, most of these works target at specific aggregation functions such as additive or multiplicative aggregation functions. Meanwhile, they assume there exists a trusted authority which facilitates the keys and other information distribution. In this paper, we aim to devise a communication efficient and privacy-preserving protocol that can exactly compute arbitrary data aggregation functions without trusted authority. In our model, there exist one untrusted aggregator and n participants. We assume that all communication channels are insecure and are subject to eavesdropping attacks. Our protocol is designed under the semi-honest model, and it can also tolerate k (k ≤ n-2) collusive adversaries. Our protocol achieves (n - k) -source anonymity. That is, for the source of each collected data aparting from the colluded participants, what the aggregator learns is only from one of the (n - k) non-colluded ones. Compared with recent work [1] that computes arbitrary aggregation functions by collecting all the participants' data using the trusted authority, our protocol increases merely by at most a factor of O(([logn/loglogn]) 2 ) in terms of computation time and communication cost. The key of our protocol is that we have designed algorithms that can efficiently assign unique sequence numbers to each participant without the trusted authority.

关键词： Protocols Data aggregation Computational modeling Privacy Additives Encryption

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SDPMN: Privacy preserving MapReduce network using SDN

arXiv

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arXiv 2018年

作者： Li, He Jin, Hai Services Computing Technology System Lab Cluster Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

MapReduce is a popular programming model and an associated implementation for parallel processing big data in the distributed environment. Since large scaled MapReduce data centers usually provide services to many users, it is an essential problem to preserve the privacy between different applications in the same network. In this paper, we propose SDPMN, a framework that using software defined network (SDN) to distinguish the network between each application, which is a manageable and scalab.e method. We design this framework based on the existing SDN structure and Hadoop networks. Since the rule space of each SDN device is limited, we also propose the rule placement optimization for this framework to maximize the hardware supported isolated application networks. We state this problem in a general MapReduce network and design a heuristic algorithm to find the solution. From the simulation based evaluation, with our algorithm, the given network can support more privacy preserving application networks with SDN switches. Copyright © 2018, The Authors. All rights reserved.

关键词： MapReduce

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Efficient Time-Evolving Stream Processing at Scale

arXiv

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arXiv 2018年

作者： Huang, Yu 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 Wuhan430074 China

—Time-evolving stream datasets exist ubiquitously in many real-world applications where their inherent hot keys often evolve over times. Nevertheless, few existing solutions can provide efficient load balance on these time-evolving datasets while preserving low memory overhead. In this paper, we present a novel grouping approach (named FISH), which can provide the efficient time-evolving stream processing at scale. The key insight of this work is that the keys of time-evolving stream data can have a skewed distribution within any bounded distance of time interval. This enables to accurately identify the recent hot keys for the real-time load balance within a bounded scope. We therefore propose an epoch-based recent hot key identification with specialized intra-epoch frequency counting (for maintaining low memory overhead) and inter-epoch hotness decaying (for suppressing superfluous computation). We also propose to heuristically infer the accurate information of remote workers through computation rather than communication for cost-efficient worker assignment. We have integrated our approach into Apache Storm. Our results on a cluster of 128 nodes for both synthetic and real-world stream datasets show that FISH significantly outperforms state-of-the-art with the average and the 99th percentile latency reduction by 87.12% and 76.34% (vs. W-Choices), and memory overhead reduction by 99.96% (vs. Shuffle Grouping). Copyright © 2018, The Authors. All rights reserved.

关键词： Fish

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引用

25th IEEE International Conference on Network Protocols, ICNP 2017

作者： Chen, Hanhua Zhang, Fan Jin, Hai 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 Wuhan430074 China

ISBN: (纸本)9781509065011

Real-world stream data with skewed distribution raises unique challenges to distributed stream processing systems. Existing stream workload partitioning schemes usually use a 'one size fits all' design, which leverage either a shuffle grouping or a key grouping strategy for partitioning the stream workloads among multiple processing units, leading to notable problems of unsatisfied system throughput and processing latency. In this paper, we show that the key grouping based schemes result in serious load imbalance and low computation efficiency in the presence of data skewness while the shuffle grouping schemes are not scalab.e in terms of memory space. We argue that the key to efficient stream scheduling is the popularity of the stream data. We propose and implement a differentiated distributed stream processing system, call DStream, which assigns the popular keys using shuffle grouping while assigns unpopular ones using key grouping. We design a novel efficient and light-weighted probabilistic counting scheme for identifying the current hot keys in dynamic real-time streams. Two factors contribute to the power of this design: 1) the probabilistic counting scheme is extremely computation and memory efficient, so that it can be well integrated in processing instances in the system;2) the scheme can adapt to the popularity changes in the dynamic stream processing environment. We implement the DStream system on top of Apache Storm. Experiment results using large-scale traces from real-world systems show that DStream achieves a 2.3× improvement in terms of processing throughput and reduces the processing latency by 64% compared to state-of-the-art designs. © 2017 IEEE.

关键词： Higher order statistics

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Hardware/Software Cooperative Caching for hybrid DRAM/NVM memory architectures 17

Hardware/Software Cooperative Caching for hybrid DRAM/NVM me...

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31st ACM International Conference on Supercomputing, ICS 2017

作者： Liu, Haikun Chen, Yujie Liao, Xiaofei Jin, Hai He, Bingsheng Zheng, Long Guo, Rentong Services Computing Technology and System Lab/Big Data Technology System Lab/Cluster and Grid Computing Lab School of Computing Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computing National University of Singapore Singapore117418 Singapore

ISBN: (纸本)9781450350204

Non-Volatile Memory (NVM) has recently emerged for its nonvolatility, high density and energy efficiency. Hybrid memory systems composed of DRAM and NVM have the best of both worlds, because NVM can offer larger capacity and have near-zero standby power consumption while DRAM provides higher performance. Many studies have advocated to use DRAM as a cache to NVM. However, it is still an open problem on how to manage the DRAM cache effectively and efficiently. In this paper, we propose a novel Hardware/Software Cooperative Caching (HSCC) mechanism that organizes NVM and DRAM in a flat address space while logically supporting a cache/memory hierarchy. HSCC maintains the NVMto-DRAM address mapping and tracks the access counts of NVM pages through a moderate extension to page tables and TLBs. It significantly simplifies the hardware design and offers several optimization opportunities for cache management in software layers. We thus propose utility-based cache filtering policies to improve the efficiency of DRAM cache. Experimental results show that HSCC improves system performance by up to 9.6X (77.2% on average) and reduces energy consumption by 34.3% on average, compared to a hardware-assisted DRAM/NVM memory system. HSCC also presents 15.4% and 14.5% performance improvement against a .ataddressable memory architecture and a Row *** Locality Aware (RBLA) caching policy for hybrid memories, respectively. © 2017 Association for computing Machinery.

关键词： Dynamic random access storage

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DroidAuditor: A framework for auditing covert communication on Android

DroidAuditor: A framework for auditing covert communication ...

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作者： Qiang, Weizhong Xin, Shifan Jin, Hai Sun, Guozhong Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Technology and System Lab Huazhong University of Science and Technology Wuhan430074 China Co. Ltd. Beijing100193 China

Exploitation of covert channels in smartphone operating systems may lead to furtive data transmission between applications with different permissions, which might threaten users' privacy. Restricting the access to shared system resources can effectively prevent the exploitation of known covert channels. However, it inevitably limits the normal usage of those resources. In this paper, we propose a general method that detects covert channel attack at runtime without impacting the accessibility of shared resources in the system. The main idea of the method is to track and audit the use of system resources known as potential covert channel variables and impose interferences on those channels to reduce their capacity once violations are detected. We implement a prototype framework, which is able to audit and interfere covert communication both in the application layer and in the native layer of Android. The experimental results demonstrate that our method can effectively reduce the data rate of user-defined covert channels while the overhead is negligible. Copyright © 2017 John Wiley & Sons, Ltd.

关键词： Android (operating system)

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