Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy最新文献_第4页

Efficient Network Path Verification for Policy-routedQueries 有效的网络路径验证策略路由查询

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2016-03-09 DOI: 10.1145/2857705.2857715

Sushama Karumanchi, Jingwei Li, A. Squicciarini

引用次数: 1

Patching Logic Vulnerabilities for Web Applications using LogicPatcher 使用logicpatchcher修补Web应用程序的逻辑漏洞

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2016-03-09 DOI: 10.1145/2857705.2857727

M. Monshizadeh, P. Naldurg, V. Venkatakrishnan

引用次数: 3

Remix: On-demand Live Randomization Remix:按需现场随机化

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2016-03-09 DOI: 10.1145/2857705.2857726

Yueh-Ting Chen, Zhi Wang, D. Whalley, Long Lu

{"title":"Remix: On-demand Live Randomization","authors":"Yueh-Ting Chen, Zhi Wang, D. Whalley, Long Lu","doi":"10.1145/2857705.2857726","DOIUrl":"https://doi.org/10.1145/2857705.2857726","url":null,"abstract":"Code randomization is an effective defense against code reuse attacks. It scrambles program code to prevent attackers from locating useful functions or gadgets. The key to secure code randomization is achieving high entropy. A practical approach to boost entropy is on-demand live randomization that works on running processes. However, enabling live randomization is challenging in that it often requires manual efforts to solve ambiguity in identifying function pointers. In this paper, we propose Remix, an efficient and practical live randomization system for both user processes and kernel modules. Remix randomly shuffles basic blocks within their respective functions. By doing so, it avoids the complexity of migrating stale function pointers, and allows mixing randomized and non-randomized code to strike a balance between performance and security. Remix randomizes a running process in two steps: it first randomly reorders its basic blocks, and then comprehensively migrates live pointers to basic blocks. Our experiments show that Remix can significantly increase randomness with low performance overhead on both CPU and I/O intensive benchmarks and kernel modules, even at very short randomization intervals.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132735738","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: 65

Decoding the Mystery of the Internet of Things 解读物联网的奥秘

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2016-03-09 DOI: 10.1145/2857705.2857729

J. Voas

引用次数: 0

TPRIVEXEC: Private Execution in Virtual Memory TPRIVEXEC:虚拟内存中的私有执行

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2016-03-09 DOI: 10.1145/2857705.2857724

J. B. Djoko, B. Jennings, Adam J. Lee

引用次数: 3

On the Origin of Mobile Apps: Network Provenance for Android Applications 手机应用的起源:Android应用的网络来源

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2016-03-09 DOI: 10.1145/2857705.2857712

Ryan Stevens, J. Crussell, Hao Chen

{"title":"On the Origin of Mobile Apps: Network Provenance for Android Applications","authors":"Ryan Stevens, J. Crussell, Hao Chen","doi":"10.1145/2857705.2857712","DOIUrl":"https://doi.org/10.1145/2857705.2857712","url":null,"abstract":"Many mobile services consist of two components: a server providing an API, and an application running on smartphones and communicating with the API. An unresolved problem in this design is that it is difficult for the server to authenticate which app is accessing the API. This causes many security problems. For example, the provider of a private network API has to embed secrets in its official app to ensure that only this app can access the API; however, attackers can uncover the secret by reverse-engineering. As another example, malicious apps may send automatic requests to ad servers to commit ad fraud. In this work, we propose a system that allows network API to authenticate the mobile app that sends each request so that the API can make an informed access control decision. Our system, the Mobile Trusted-Origin Policy, consists of two parts: 1) an app provenance mechanism that annotates outgoing HTTP(S) requests with information about which app generated the network traffic, and 2) a code isolation mechanism that separates code within an app that should have different app provenance signatures into mobile origin. As motivation for our work, we present two previously-unknown families of apps that perform click fraud, and examine how the lack of mobile origin information enables the attacks. Based on our observations, we propose Trusted Cross-Origin Requests to handle point (1), which automatically includes mobile origin information in outgoing HTTP requests. Servers may then decide, based on the mobile origin data, whether to process the request or not. We implement a prototype of our system for Android and evaluate its performance, security, and deployability. We find that our system can achieve our security and utility goals with negligible overhead.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133231793","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: 2

Security Constraints in Temporal Role-Based Access-Controlled Workflows 基于临时角色的访问控制工作流中的安全约束

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2015-12-20 DOI: 10.1145/2857705.2857716

Combi Carlo, L. Viganò, Matteo Zavatteri

引用次数: 23

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification 基于特征提取、选择和融合的恶意软件分类方法

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2015-11-13 DOI: 10.1145/2857705.2857713

Mansour Ahmadi, G. Giacinto, Dmitry Ulyanov, Stanislav Semenov, Mikhail Trofimov

{"title":"Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification","authors":"Mansour Ahmadi, G. Giacinto, Dmitry Ulyanov, Stanislav Semenov, Mikhail Trofimov","doi":"10.1145/2857705.2857713","DOIUrl":"https://doi.org/10.1145/2857705.2857713","url":null,"abstract":"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 ($approx$ 0.998) on the Microsoft Malware Challenge dataset.","PeriodicalId":377412,"journal":{"name":"Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114288981","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: 293

Differentially Private K-Means Clustering 差分私有k均值聚类

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 2015-04-22 DOI: 10.1145/2857705.2857708

D. Su, Jianneng Cao, Ninghui Li, E. Bertino, Hongxia Jin

引用次数: 129

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy 第六届ACM数据与应用安全与隐私会议论文集

Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy Pub Date : 1900-01-01 DOI: 10.1145/2857705

引用次数: 1