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检索条件"任意字段=5th ACM Conference on Data and Application Security and Privacy, CODASPY 2015"

共 43 条记录，以下是11-20 订阅

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Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification 16

Novel Feature Extraction, Selection and Fusion for Effective...

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6th acm conference on data and application security and privacy (codaspy)

作者： Ahmadi, Mansour Ulyanov, Dmitry Semenov, Stanislav Trofimov, Mikhail Giacinto, Giorgio Univ Cagliari Dept Elect & Elect Engn I-09124 Cagliari Italy Skolkovo Inst Sci & Technol Moskovsky Russia Natl Res Univ Higher Sch Econ Moscow Russia Moscow Inst Phys & Technol Dolgoprudnyi Moskovskaya Obl Russia

ISBN: (纸本)9781450339353

Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Categorization of malware samples on the basis of their behaviors is essential for the computer security community, because they receive huge number of malware everyday, and the signature extraction process is usually based on malicious parts characterizing malware families. Microsoft released a malware classification challenge in 2015 with a huge dataset of near 0.5 terabytes of data, containing more than 20K malware samples. the analysis of this dataset inspired the development of a novel paradigm that is effective in categorizing malware variants into their actual family groups. this paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples. Features can be grouped according to different characteristics of malware behavior, and their fusion is performed according to a per-class weighting paradigm. the proposed method achieved a very high accuracy (approximate to 0.998) on the Microsoft Malware Challenge dataset.

关键词： Windows Malware Machine learning Malware family Computer security Classification Microsoft Malware Classification Challenge

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11th International Workshop on data privacy Management and security Assurance, DPM 2016 and 5th International Workshop on Quantitative Aspects in security Assurance, QASA 2016

11th International Workshop on Data Privacy Management and S...

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11th International Workshop on data privacy Management and security Assurance, DPM 2016 and 5th International Workshop on Quantitative Aspects in security Assurance, QASA 2016

ISBN: (纸本)9783319470719

the proceedings contain 17 papers. the special focus in this conference is on Quantitative Aspects in security Assurance, privacy Protection, Identification, Authentication, Authorization, security and Secure applications. the topics include: Metrics for transparency;understanding bifurcation of slow versus fast cyber-attackers;decomposing global quantitative properties into local ones;efficient sat-based pre-image enumeration for quantitative information flow in programs;controlled management of confidentiality-preserving relational interactions;privacy-preserving abuse detection in future decentralised online social networks;searchable encryption for biometric identification revisited;investigating the animation of application permission dialogs;a case study of facebook;Isabelle modelchecking for insider threats;managing and presenting user attributes over a decentralized secure name system;a privacy risk analysis methodology;a study from the data anonymization competition pwscup 2015 and refactoring preserves security.

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Big data security and privacy 15

Big data security and privacy

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5th acm conference on data and application security and privacy, codaspy 2015

作者： thuraisingham, Bhavani University of Texas at Dallas United States

ISBN: (纸本)9781450331913

this paper describes the issues surrounding big data security and privacy and provides a summary of the National Science Foundation sponsored workshop on this topic held in Dallas, Texas on September 16-17, 2014. Our goal is to build a community in big data security and privacy to explore the challenging research problems. Copyright © 2015 acm.

关键词： Big data

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A network security game model 15

A network security game model

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5th acm conference on data and application security and privacy, codaspy 2015

作者： Shandilya, Vivek Shiva, Sajjan Dept of Computer Science University of Memphis MemphisTN38152 United States

ISBN: (纸本)9781450331913

there have been attempts to model the interaction between users, both malicious and benign, and network administrators as games. Building on such works, we here present a game model which is generic enough to capture various modes of such interactions. the model facilitates stochastic games with imperfect information. the imperfect information is due to erroneous sensors leading to incorrect perception of the current state by the players. To model this error in perception distributed over other multiple states, we use Euclidean distances between inputs from the sensors. Copyright © 2015 acm.

关键词： Network security

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Secure information and resource sharing in cloud 15

Secure information and resource sharing in cloud

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5th acm conference on data and application security and privacy, codaspy 2015

作者： Zhang, Yun Krishnan, Ram Sandhu, Ravi Institute for Cyber Security Univ of Texas at San Antonio San AntonioTX78249 United States Dept. of Electrical and Computer Engineering Univ of Texas at San Antonio San AntonioTX78249 United States

ISBN: (纸本)9781450331913

the significant threats from information security breaches in cyber world is one of the most serious security problems. Organizations are facing growing number of sophisticated cyber-attacks every year. Efficient and secure sharing of at- tack and security information during cyber incident response plays increasingly significant role in fixing the problems as well as helping organizations recover fast. While traditional systems are slow and inefficient in sharing information and resources securely, cloud platform provides us a consider- able convenience to facilitate the sharing. In this paper, we propose access control models for secure information and re- source sharing (IARS) in cloud Infrastructure as a Service (IaaS). Copyright © 2015 acm.

关键词： Infrastructure as a service (IaaS)

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Information sharing and user privacy in the third-party identity management landscape 15

Information sharing and user privacy in the third-party iden...

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5th acm conference on data and application security and privacy, codaspy 2015

作者： Vapen, Anna Carlsson, Niklas Mahanti, Anirban Shahmehri, Nahid Linköping University Sweden NICTA Australia

ISBN: (纸本)9781450331913

third-party identity management services enable cross-site information sharing, making Web access seamless but also raise significant privacy implications for the users. Using a combination of manual analysis of identified third-party identity management relationships and targeted case stud- ies we capture how the protocol usage and third-party se- lection is changing, profile what information is requested to be shared (and actions to be performed) between web- sites, and identify privacy issues and practical problems that occur when using multiple accounts (associated with these services). the study highlights differences in the privacy leakage risks associated with different classes of websites, and shows that the use of multiple third-party websites, in many cases, can cause the user to lose (at least) partial con- trol over which information is shared/posted on their behalf. Copyright © 2015 acm.

关键词： Websites

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privacy-preserving scanning of big content for sensitive data exposure with mapreduce 15

Privacy-preserving scanning of big content for sensitive dat...

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5th acm conference on data and application security and privacy, codaspy 2015

作者： Liu, Fang Shu, Xiaokui Yao, Danfeng Daphne Butt, Ali R. Department Computer Science Virginia Tech BlacksburgVA United States

ISBN: (纸本)9781450331913

the exposure of sensitive data in storage and transmission poses a serious threat to organizational and personal secu- rity. data leak detection aims at scanning content (in stor- age or transmission) for exposed sensitive data. Because of the large content and data volume, such a screening algo- rithm needs to be scalable for a timely detection. Our solu- tion uses the MapReduce framework for detecting exposed sensitive content, because it has the ability to arbitrarily scale and utilize public resources for the task, such as Ama- zon EC2. We design new MapReduce algorithms for com- puting collection intersection for data leak detection. Our prototype implemented with the Hadoop system achieves 225 Mbps analysis throughput with 24 nodes. Our algo- rithms support a useful privacy-preserving data transfor- mation. this transformation enables the privacy-preserving technique to minimize the exposure of sensitive data during the detection. this transformation supports the secure out- sourcing of the data leak detection to untrusted MapReduce and cloud providers. Copyright © 2015 acm.

关键词： MapReduce

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Rapid screening of transformed data leaks with efficient algorithms and parallel computing 15

Rapid screening of transformed data leaks with efficient alg...

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5th acm conference on data and application security and privacy, codaspy 2015

作者： Shu, Xiaokui Zhang, Jing Yao, Danfeng Daphne Feng, Wu-Chun Department of Computer Science Virginia Tech BlacksburgVA United States

ISBN: (纸本)9781450331913

the leak of sensitive data on computer systems poses a seri- ous threat to organizational security. Organizations need to identify the exposure of sensitive data by screening the con- tent in storage and transmission, i.e., to detect sensitive in- formation being stored or transmitted in the clear. However, detecting the exposure of sensitive information is challenging due to data transformation in the content. Transformations (such as insertion, deletion) result in highly unpredictable leak patterns. Existing automata-based string matching algorithms are impractical for detecting transformed data leaks, because of its formidable complexity when model- ing the required regular expressions. We design two new algorithms for detecting long and transformed data leaks. Our system achieves high detection accuracy in recogniz- ing transformed leaks compared to the state-of-the-art in- spection methods. We parallelize our prototype on graphics processing unit and demonstrate the strong scalability of our detection solution required by a sizable organization. Copyright © 2015 acm.

关键词： Sensitive data

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SemaDroid: A privacy-aware sensor management framework for smartphones 15

SemaDroid: A privacy-aware sensor management framework for s...

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5th acm conference on data and application security and privacy, codaspy 2015

作者： Xu, Zhi Zhu, Sencun Pennsylvania State University University ParkPA United States

ISBN: (纸本)9781450331913

While mobile sensing applications are booming, the sensor man- agement mechanisms in current smartphone operating systems are left behind - they are incomprehensive and coarse-grained, expos- ing a huge attack surface for malicious or aggressive third party apps to steal user's private information through mobile sensors. In this paper, we propose a privacy-aware sensor management framework, called SemaDroid, which extends the existing sensor management framework on Android to provide comprehensive and fine-grained access control over onboard sensors. SemaDroid al- lows the user to monitor the sensor usage of installed apps, and to control the disclosure of sensing information while not affect- ing the app's usability. Furthermore, SemaDroid supports context- aware and quality-of-sensing based access control policies. the en- forcement and update of the policies are in real-time. Detailed de- sign and implementation of SemaDroid on Android are presented to show that SemaDroid works compatible with the existing An- droid security framework. Demonstrations are also given to show the capability of SemaDroid on sensor management and on defeat- ing emerging sensor-based attacks. Finally, we show the high effi- ciency and security of SemaDroid. Copyright © 2015 acm.

关键词： Smartphones

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Combining ORAM with PIR to minimize bandwidth costs 15

Combining ORAM with PIR to minimize bandwidth costs

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5th acm conference on data and application security and privacy, codaspy 2015

作者： Dautrich, Jonathan Ravishankar, Chinya Google Inc. IrvineCA United States Computer Science and Engineering University of California Riverside United States

ISBN: (纸本)9781450331913

Cloud computing allows customers to outsource the burden of data management and benefit from economy of scale, but privacy concerns limit its reach. Even if the stored data are encrypted, access patterns may leak valuable information. Oblivious RAM (ORAM) protocols guarantee full access pattern privacy, but even the most efficient ORAMs proposed to date incur large bandwidth costs. We combine Private Information Retrieval (PIR) techniques with the most bandwidth-efficient existing ORAM scheme known to date (ObliviStore), to create OS+PIR, a new ORAM with bandwidth costs only half those of ObliviStore. For data block counts ranging from 220 to 230, OS+PIR achieves a total bandwidth cost of only 11X-13X blocks transferred per client block read+write, down from Oblivi- Store's 18X-26X. OS+PIR introduces several enhancements in addition to PIR in order to achieve its lower costs, including mechanisms for eliminating unused dummy blocks. Copyright © 2015 acm.

关键词： data privacy

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