2018 16th Annual Conference on Privacy, Security and Trust (PST)最新文献

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Usability and Security Effects of Code Examples on Crypto APIs 加密api代码示例的可用性和安全性影响
2018 16th Annual Conference on Privacy, Security and Trust (PST) Pub Date : 2018-07-03 DOI: 10.1109/PST.2018.8514203
K. Mindermann, Stefan Wagner
{"title":"Usability and Security Effects of Code Examples on Crypto APIs","authors":"K. Mindermann, Stefan Wagner","doi":"10.1109/PST.2018.8514203","DOIUrl":"https://doi.org/10.1109/PST.2018.8514203","url":null,"abstract":"Context: Cryptographic APIs are said to be not usable and researchers suggest to add example code to the documentation. Aim: We wanted to create a free platform for cryptographic code examples that improves the usability and security of created applications by non security experts. Method: We created the open-source web platform CryptoExamples and conducted a controlled experiment where 58 students added symmetric encryption to a Java program. We then measured the usability and security. Results: The participants who used the platform were not only significantly more effective $( +73$ %) but also their code contained significantly less possible security vulnerabilities (-66 %). Conclusions: With CryptoExamples the gap between hard to change API documentation and the need for complete and secure code examples can be closed. Still, the platform needs more code examples.","PeriodicalId":265506,"journal":{"name":"2018 16th Annual Conference on Privacy, Security and Trust (PST)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131383181","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: 14
A Family of Droids-Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis 通过行为建模检测android恶意软件:静态vs动态分析
2018 16th Annual Conference on Privacy, Security and Trust (PST) Pub Date : 2018-03-09 DOI: 10.1109/PST.2018.8514191
Lucky Onwuzurike, Mário Almeida, Enrico Mariconti, Jeremy Blackburn, G. Stringhini, Emiliano De Cristofaro
{"title":"A Family of Droids-Android Malware Detection via Behavioral Modeling: Static vs Dynamic Analysis","authors":"Lucky Onwuzurike, Mário Almeida, Enrico Mariconti, Jeremy Blackburn, G. Stringhini, Emiliano De Cristofaro","doi":"10.1109/PST.2018.8514191","DOIUrl":"https://doi.org/10.1109/PST.2018.8514191","url":null,"abstract":"Following the increasing popularity of the mobile ecosystem, cybercriminals have increasingly targeted mobile ecosystems, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to counter them, detection techniques based on either static or dynamic analysis that model Android malware, have been proposed. While the pros and cons of these analysis techniques are known, they are usually compared in the context of their limitations e.g., static analysis is not able to capture runtime behaviors, full code coverage is usually not achieved during dynamic analysis, etc. Whereas, in this paper, we analyze the performance of static and dynamic analysis methods in the detection of Android malware and attempt to compare them in terms of their detection performance, using the same modeling approach.To this end, we build on MAMADROID, a state-of-the-art detection system that relies on static analysis to create a behavioral model from the sequences of abstracted API calls. Then, aiming to apply the same technique in a dynamic analysis setting, we modify CHIMP, a platform recently proposed to crowdsource human inputs for app testing, in order to extract API calls' sequences from the traces produced while executing the app on a CHIMP virtual device. We call this system AUNTIEDROID and instantiate it by using both automated (Monkey) and usergenerated inputs. We find that combining both static and dynamic analysis yields the best performance, with $F -$measure reaching 0.92. We also show that static analysis is at least as effective as dynamic analysis, depending on how apps are stimulated during execution, and investigate the reasons for inconsistent misclassifications across methods.","PeriodicalId":265506,"journal":{"name":"2018 16th Annual Conference on Privacy, Security and Trust (PST)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125526692","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: 40
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