2013 International Conference on Security and Cryptography (SECRYPT)最新文献_第2页

Practical and exposure-resilient hierarchical ID-based authenticated key exchange without random oracles 实用且抗暴露的分层id认证密钥交换，无需随机oracle

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.1587/TRANSFUN.E97.A.1335

Kazuki Yoneyama

引用次数: 3

Development of device identity using WiFi layer 2 management frames for combating Rogue APs 利用WiFi第二层管理框架开发设备身份识别以对抗流氓ap

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004506404880493

J. Milliken, Valerio Selis, K. Yap, A. Marshall

引用次数: 3

A model-driven approach for securing software architectures 用于保护软件体系结构的模型驱动方法

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004611305950602

M. A. Neri, M. Guarnieri, E. Magri, S. Mutti, S. Paraboschi

引用次数: 5

Policy-based non-interactive outsourcing of computation using multikey FHE and CP-ABE 使用多键FHE和CP-ABE的基于策略的非交互式计算外包

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004534304440452

Michael Clear, C. McGoldrick

引用次数: 7

Secure computation of hidden Markov models 隐马尔可夫模型的安全计算

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004533502420253

Mehrdad Aliasgari, Marina Blanton

引用次数: 19

AVON: A fast Hash function for Intel SIMD architectures 雅芳:一个快速哈希函数为英特尔SIMD架构

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004502104820487

Matt Henricksen, S. Kiyomoto

引用次数: 0

Privacy-enhanced perceptual hashing of audio data 增强隐私的音频数据感知哈希

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004532605490554

H. Knospe

引用次数: 4

Extending the ciphertext-policy attribute based encryption scheme for supporting flexible access control 扩展了基于cipher -policy属性的加密方案，支持灵活的访问控制

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004525801470157

B. Lang, Runhua Xu, Yawei Duan

{"title":"Extending the ciphertext-policy attribute based encryption scheme for supporting flexible access control","authors":"B. Lang, Runhua Xu, Yawei Duan","doi":"10.5220/0004525801470157","DOIUrl":"https://doi.org/10.5220/0004525801470157","url":null,"abstract":"Ciphertext-Policy Attribute Based Encryption (CP-ABE) is recognized as an important data protection mechanism in cloud computing environment for its flexible, scalable and fine-grained access control features. For enhancing its security, efficiency and policy flexibility, researchers have proposed different schemes of CP-ABE which have different kinds of access policy structures. However, as far as we know, most of these structures only support AND, OR and threshold attribute operations. In order to achieve more effective data self-protection mechanisms in open environments such as Cloud computing, CP-ABE needs to support more flexible attribute based policies, most of which are described using operators of NOT, <, ≤, >, ≥. This paper proposed an Extended CP-ABE(ECP-ABE) scheme based on the existing CP-ABE scheme. The ECP-ABE scheme can express any access policy represented by arithmetic comparison and logical expressions that involve NOT, <, ≤, >, ≥ operators in addition to AND, OR and threshold operators. We prove the Chosen-plaintext Attack (CPA) security of our scheme under the Decisional Bilinear Diffie-Hellman (DBDH) assumption in the standard model, and also discuss the experimental results of the efficiency of ECP-ABE.","PeriodicalId":174026,"journal":{"name":"2013 International Conference on Security and Cryptography (SECRYPT)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128452076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

LMM: A common component for software license management on cloud LMM:用于云上软件许可证管理的通用组件

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004504502840295

S. Kiyomoto, André Rein, Yuto Nakano, C. Rudolph, Yutaka Miyake

引用次数: 3

Behavior-based Malware analysis using profile hidden Markov models 基于行为的恶意软件分析使用配置文件隐马尔可夫模型

2013 International Conference on Security and Cryptography (SECRYPT) Pub Date : 2013-07-29 DOI: 10.5220/0004528201950206

S. Ravi, N. Balakrishnan, Bharath Venkatesh

{"title":"Behavior-based Malware analysis using profile hidden Markov models","authors":"S. Ravi, N. Balakrishnan, Bharath Venkatesh","doi":"10.5220/0004528201950206","DOIUrl":"https://doi.org/10.5220/0004528201950206","url":null,"abstract":"In the area of malware analysis, static binary analysis techniques are becoming increasingly difficult with the code obfuscation methods and code packing employed when writing the malware. The behavior-based analysis techniques are being used in large malware analysis systems because of this reason. In these dynamic analysis systems, the malware samples are executed and monitored in a controlled environment using tools such as CWSandbox(Willems et al., 2007). In previous works, a number of clustering and classification techniques from machine learning and data mining have been used to classify the malwares into families and to identify even new malware families, from the behavior reports. In our work, we propose to use the Profile Hidden Markov Model to classify the malware files into families or groups based on their behavior on the host system. PHMM has been used extensively in the area of bioinformatics to search for similar protein and DNA sequences in a large database. We see that using this particular model will help us overcome the hurdle posed by polymorphism that is common in malware today. We show that the classification accuracy is high and comparable with the state-of-art-methods, even when using very few training samples for building models. The experiments were on a dataset with 24 families initially, and later using a larger dataset with close to 400 different families of malware. A fast clustering method to group malware with similar behaviour following the scoring on the PHMM profile database was used for the large dataset. We have presented the challenges in the evaluation methods and metrics of clustering on large number of malware files and show the effectiveness of using profile hidden model models for known malware families.","PeriodicalId":174026,"journal":{"name":"2013 International Conference on Security and Cryptography (SECRYPT)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117013082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 11