The rising popularity of mobile apps deployed on battery-constrained devices underlines the need for effectively evaluating their energy properties. However, currently there is a lack of testing tools for evaluating the energy properties of apps. As a result, for energy testing, developers are relying on tests intended for evaluating the functional correctness of apps. Such tests may not be adequate for revealing energy defects and inefficiencies in apps. This paper presents an energy-aware mutation testing framework, called μDROID, that can be used by developers to assess the adequacy of their test suite for revealing energy-related defects. μDROID implements fifty energy-aware mutation operators and relies on a novel, automatic oracle to determine if a mutant can be killed by a test. Our evaluation on real-world Android apps shows the ability of proposed mutation operators for evaluating the utility of tests in revealing energy defects. Moreover, our automated oracle can detect whether tests kill the energy mutants with an overall accuracy of 94%, thereby making it possible to apply μDROID automatically.
{"title":"µDroid: an energy-aware mutation testing framework for Android","authors":"Reyhaneh Jabbarvand, S. Malek","doi":"10.1145/3106237.3106244","DOIUrl":"https://doi.org/10.1145/3106237.3106244","url":null,"abstract":"The rising popularity of mobile apps deployed on battery-constrained devices underlines the need for effectively evaluating their energy properties. However, currently there is a lack of testing tools for evaluating the energy properties of apps. As a result, for energy testing, developers are relying on tests intended for evaluating the functional correctness of apps. Such tests may not be adequate for revealing energy defects and inefficiencies in apps. This paper presents an energy-aware mutation testing framework, called μDROID, that can be used by developers to assess the adequacy of their test suite for revealing energy-related defects. μDROID implements fifty energy-aware mutation operators and relies on a novel, automatic oracle to determine if a mutant can be killed by a test. Our evaluation on real-world Android apps shows the ability of proposed mutation operators for evaluating the utility of tests in revealing energy defects. Moreover, our automated oracle can detect whether tests kill the energy mutants with an overall accuracy of 94%, thereby making it possible to apply μDROID automatically.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129921089","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}
Wearable devices are becoming increasingly popular. These wearable devices run what is known as wearable apps. Wearable apps are packaged with handheld apps, that must be installed on the accompanying handheld device (e.g., phone). Given that wearable apps are tightly coupled with the handheld apps, any wearable permission must also be requested in the handheld version of the app on the Android Wear platform. However, in some cases, the wearable apps may request permissions that do not exist in the handheld app, resulting in a permission mismatch, and causing the wearable app to error or crash. In this paper, we propose a technique to detect wear app permission mismatches. We perform a case study on 2,409 free Android Wear apps and find that 73 released wearable apps suffer from the permission mismatch problem.
{"title":"Detecting wearable app permission mismatches: a case study on Android wear","authors":"Suhaib Mujahid","doi":"10.1145/3106237.3121279","DOIUrl":"https://doi.org/10.1145/3106237.3121279","url":null,"abstract":"Wearable devices are becoming increasingly popular. These wearable devices run what is known as wearable apps. Wearable apps are packaged with handheld apps, that must be installed on the accompanying handheld device (e.g., phone). Given that wearable apps are tightly coupled with the handheld apps, any wearable permission must also be requested in the handheld version of the app on the Android Wear platform. However, in some cases, the wearable apps may request permissions that do not exist in the handheld app, resulting in a permission mismatch, and causing the wearable app to error or crash. In this paper, we propose a technique to detect wear app permission mismatches. We perform a case study on 2,409 free Android Wear apps and find that 73 released wearable apps suffer from the permission mismatch problem.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125098988","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}
M. Aliabadi, Amita Ajith Kamath, Julien Gascon-Samson, K. Pattabiraman
Cyber-Physical Systems (CPSes) are being widely deployed in security critical scenarios such as smart homes and medical devices. Unfortunately, the connectedness of these systems and their relative lack of security measures makes them ripe targets for attacks. Specification-based Intrusion Detection Systems (IDS) have been shown to be effective for securing CPSs. Unfortunately, deriving invariants for capturing the specifications of CPS systems is a tedious and error-prone process. Therefore, it is important to dynamically monitor the CPS system to learn its common behaviors and formulate invariants for detecting security attacks. Existing techniques for invariant mining only incorporate data and events, but not time. However, time is central to most CPS systems, and hence incorporating time in addition to data and events, is essential for achieving low false positives and false negatives. This paper proposes ARTINALI, which mines dynamic system properties by incorporating time as a first-class property of the system. We build ARTINALI-based Intrusion Detection Systems (IDSes) for two CPSes, namely smart meters and smart medical devices, and measure their efficacy. We find that the ARTINALI-based IDSes significantly reduce the ratio of false positives and false negatives by 16 to 48% (average 30.75%) and 89 to 95% (average 93.4%) respectively over other dynamic invariant detection tools.
{"title":"ARTINALI: dynamic invariant detection for cyber-physical system security","authors":"M. Aliabadi, Amita Ajith Kamath, Julien Gascon-Samson, K. Pattabiraman","doi":"10.1145/3106237.3106282","DOIUrl":"https://doi.org/10.1145/3106237.3106282","url":null,"abstract":"Cyber-Physical Systems (CPSes) are being widely deployed in security critical scenarios such as smart homes and medical devices. Unfortunately, the connectedness of these systems and their relative lack of security measures makes them ripe targets for attacks. Specification-based Intrusion Detection Systems (IDS) have been shown to be effective for securing CPSs. Unfortunately, deriving invariants for capturing the specifications of CPS systems is a tedious and error-prone process. Therefore, it is important to dynamically monitor the CPS system to learn its common behaviors and formulate invariants for detecting security attacks. Existing techniques for invariant mining only incorporate data and events, but not time. However, time is central to most CPS systems, and hence incorporating time in addition to data and events, is essential for achieving low false positives and false negatives. This paper proposes ARTINALI, which mines dynamic system properties by incorporating time as a first-class property of the system. We build ARTINALI-based Intrusion Detection Systems (IDSes) for two CPSes, namely smart meters and smart medical devices, and measure their efficacy. We find that the ARTINALI-based IDSes significantly reduce the ratio of false positives and false negatives by 16 to 48% (average 30.75%) and 89 to 95% (average 93.4%) respectively over other dynamic invariant detection tools.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124080714","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}
Bogdan Vasilescu, Casey Casalnuovo, Premkumar T. Devanbu
Well-chosen variable names are critical to source code readability, reusability, and maintainability. Unfortunately, in deployed JavaScript code (which is ubiquitous on the web) the identifier names are frequently minified and overloaded. This is done both for efficiency and also to protect potentially proprietary intellectual property. In this paper, we describe an approach based on statistical machine translation (SMT) that recovers some of the original names from the JavaScript programs minified by the very popular UglifyJS. This simple tool, Autonym, performs comparably to the best currently available deobfuscator for JavaScript, JSNice, which uses sophisticated static analysis. In fact, Autonym is quite complementary to JSNice, performing well when it does not, and vice versa. We also introduce a new tool, JSNaughty, which blends Autonym and JSNice, and significantly outperforms both at identifier name recovery, while remaining just as easy to use as JSNice. JSNaughty is available online at http://jsnaughty.org.
{"title":"Recovering clear, natural identifiers from obfuscated JS names","authors":"Bogdan Vasilescu, Casey Casalnuovo, Premkumar T. Devanbu","doi":"10.1145/3106237.3106289","DOIUrl":"https://doi.org/10.1145/3106237.3106289","url":null,"abstract":"Well-chosen variable names are critical to source code readability, reusability, and maintainability. Unfortunately, in deployed JavaScript code (which is ubiquitous on the web) the identifier names are frequently minified and overloaded. This is done both for efficiency and also to protect potentially proprietary intellectual property. In this paper, we describe an approach based on statistical machine translation (SMT) that recovers some of the original names from the JavaScript programs minified by the very popular UglifyJS. This simple tool, Autonym, performs comparably to the best currently available deobfuscator for JavaScript, JSNice, which uses sophisticated static analysis. In fact, Autonym is quite complementary to JSNice, performing well when it does not, and vice versa. We also introduce a new tool, JSNaughty, which blends Autonym and JSNice, and significantly outperforms both at identifier name recovery, while remaining just as easy to use as JSNice. JSNaughty is available online at http://jsnaughty.org.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130668249","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}
Sometimes, when programmers use a search engine they know more or less what they need. Other times, programmers use the search engine to look around and generate possible ideas for the programming problem they are working on. The key insight we explore in this paper is that the results found in the latter case tend to serve as inspiration or triggers for the next queries issued. We introduce two search engines, CodeExchange and CodeLikeThis, both of which are specifically designed to enable the user to directly leverage the results in formulating the next query. CodeExchange does this with a set of four features supporting the programmer to use characteristics of the results to find other code with or without those characteristics. CodeLikeThis supports simply selecting an entire result to find code that is analogous, to some degree, to that result. We evaluated how these approaches were used along with two approaches not explicitly supporting iteration, a baseline and Google, in a user study among 24 developers. We find that search engines that support using results to form the next query can improve the programmers’ search experience and different approaches to iteration can provide better experiences depending on the task.
{"title":"Understanding the impact of support for iteration on code search","authors":"Lee Martie","doi":"10.1145/3106237.3106293","DOIUrl":"https://doi.org/10.1145/3106237.3106293","url":null,"abstract":"Sometimes, when programmers use a search engine they know more or less what they need. Other times, programmers use the search engine to look around and generate possible ideas for the programming problem they are working on. The key insight we explore in this paper is that the results found in the latter case tend to serve as inspiration or triggers for the next queries issued. We introduce two search engines, CodeExchange and CodeLikeThis, both of which are specifically designed to enable the user to directly leverage the results in formulating the next query. CodeExchange does this with a set of four features supporting the programmer to use characteristics of the results to find other code with or without those characteristics. CodeLikeThis supports simply selecting an entire result to find code that is analogous, to some degree, to that result. We evaluated how these approaches were used along with two approaches not explicitly supporting iteration, a baseline and Google, in a user study among 24 developers. We find that search engines that support using results to form the next query can improve the programmers’ search experience and different approaches to iteration can provide better experiences depending on the task.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"119 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130805428","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}
Data race is a major source of concurrency bugs. Dynamic data race detection tools (e.g., FastTrack) monitor the execu-tions of a program to report data races occurring in runtime. However, such tools incur significant overhead that slows down and perturbs executions. To address the issue, the state-of-the-art dynamic data race detection tools (e.g., LiteRace) ap-ply sampling techniques to selectively monitor memory access-es. Although they reduce overhead, they also miss many data races as confirmed by existing studies. Thus, practitioners face a dilemma on whether to use FastTrack, which detects more data races but is much slower, or LiteRace, which is faster but detects less data races. In this paper, we propose a new sam-pling approach to address the major limitations of current sampling techniques, which ignore the facts that a data race involves two threads and a program under testing is repeatedly executed. We develop a tool called AtexRace to sample memory accesses across both threads and executions. By selectively monitoring the pairs of memory accesses that have not been frequently observed in current and previous executions, AtexRace detects as many data races as FastTrack at a cost as low as LiteRace. We have compared AtexRace against FastTrack and LiteRace on both Parsec benchmark suite and a large-scale real-world MySQL Server with 223 test cases. The experiments confirm that AtexRace can be a replacement of FastTrack and LiteRace.
{"title":"AtexRace: across thread and execution sampling for in-house race detection","authors":"Yu-dong Guo, Yan Cai, Z. Yang","doi":"10.1145/3106237.3106242","DOIUrl":"https://doi.org/10.1145/3106237.3106242","url":null,"abstract":"Data race is a major source of concurrency bugs. Dynamic data race detection tools (e.g., FastTrack) monitor the execu-tions of a program to report data races occurring in runtime. However, such tools incur significant overhead that slows down and perturbs executions. To address the issue, the state-of-the-art dynamic data race detection tools (e.g., LiteRace) ap-ply sampling techniques to selectively monitor memory access-es. Although they reduce overhead, they also miss many data races as confirmed by existing studies. Thus, practitioners face a dilemma on whether to use FastTrack, which detects more data races but is much slower, or LiteRace, which is faster but detects less data races. In this paper, we propose a new sam-pling approach to address the major limitations of current sampling techniques, which ignore the facts that a data race involves two threads and a program under testing is repeatedly executed. We develop a tool called AtexRace to sample memory accesses across both threads and executions. By selectively monitoring the pairs of memory accesses that have not been frequently observed in current and previous executions, AtexRace detects as many data races as FastTrack at a cost as low as LiteRace. We have compared AtexRace against FastTrack and LiteRace on both Parsec benchmark suite and a large-scale real-world MySQL Server with 223 test cases. The experiments confirm that AtexRace can be a replacement of FastTrack and LiteRace.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131572813","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}
Leonid Glanz, Sven Amann, Michael Eichberg, Michael Reif, Ben Hermann, Johannes Lerch, M. Mezini
An established way to steal the income of app developers, or to trick users into installing malware, is the creation of repackaged apps. These are clones of - typically - successful apps. To conceal their nature, they are often obfuscated by their creators. But, given that it is a common best practice to obfuscate apps, a trivial identification of repackaged apps is not possible. The problem is further intensified by the prevalent usage of libraries. In many apps, the size of the overall code base is basically determined by the used libraries. Therefore, two apps, where the obfuscated code bases are very similar, do not have to be repackages of each other. To reliably detect repackaged apps, we propose a two step approach which first focuses on the identification and removal of the library code in obfuscated apps. This approach - LibDetect - relies on code representations which abstract over several parts of the underlying bytecode to be resilient against certain obfuscation techniques. Using this approach, we are able to identify on average 70% more used libraries per app than previous approaches. After the removal of an app's library code, we then fuzzy hash the most abstract representation of the remaining app code to ensure that we can identify repackaged apps even if very advanced obfuscation techniques are used. This makes it possible to identify repackaged apps. Using our approach, we found that ≈ 15% of all apps in Android app stores are repackages
{"title":"CodeMatch: obfuscation won't conceal your repackaged app","authors":"Leonid Glanz, Sven Amann, Michael Eichberg, Michael Reif, Ben Hermann, Johannes Lerch, M. Mezini","doi":"10.1145/3106237.3106305","DOIUrl":"https://doi.org/10.1145/3106237.3106305","url":null,"abstract":"An established way to steal the income of app developers, or to trick users into installing malware, is the creation of repackaged apps. These are clones of - typically - successful apps. To conceal their nature, they are often obfuscated by their creators. But, given that it is a common best practice to obfuscate apps, a trivial identification of repackaged apps is not possible. The problem is further intensified by the prevalent usage of libraries. In many apps, the size of the overall code base is basically determined by the used libraries. Therefore, two apps, where the obfuscated code bases are very similar, do not have to be repackages of each other. To reliably detect repackaged apps, we propose a two step approach which first focuses on the identification and removal of the library code in obfuscated apps. This approach - LibDetect - relies on code representations which abstract over several parts of the underlying bytecode to be resilient against certain obfuscation techniques. Using this approach, we are able to identify on average 70% more used libraries per app than previous approaches. After the removal of an app's library code, we then fuzzy hash the most abstract representation of the remaining app code to ensure that we can identify repackaged apps even if very advanced obfuscation techniques are used. This makes it possible to identify repackaged apps. Using our approach, we found that ≈ 15% of all apps in Android app stores are repackages","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131908095","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}
Agile Development values working software over documentation. Therefore, in maintenance stages of existing software, the source code is the sole software artifact that developers have for analyzing the viability and impact of a new user story. Since functionality is often spread in hundreds of lines of code, it is hard for the developer to understand the system, which may lead to under-/overestimation of the new feature cost and rework/delays in the subsequent phases of development. In a previous work, we proposed a Model-Driven Reverse Engineering approach for obtaining software visualizations from source code. Two case studies of comprehension of applications written in statically typed languages have shown the applicability of this approach. A recent experience with an industrial partner, where the systems are developed on dynamically typed languages, has motivated us to adapt the previous proposal to take as input not only the source code but also the application data schema to complete the information that is missing in the code, and then automatically generate more meaningful diagrams that help developers in maintenance tasks. In this article, we present the adaptation of the general approach to support data schema as an additional input and its instrumentation in an industrial case study where the technology is Ruby on Rails. The paper ends by explaining the precision and performance of the instrumentation when used in a Colombian company as well as lessons learned.
敏捷开发更重视工作软件而不是文档。因此，在现有软件的维护阶段，源代码是开发人员用来分析新用户场景的可行性和影响的唯一软件工件。由于功能通常分布在数百行代码中，因此开发人员很难理解系统，这可能导致对新功能成本的低估/高估，以及在后续开发阶段的返工/延迟。在之前的工作中，我们提出了一种模型驱动的逆向工程方法，用于从源代码中获得软件可视化。理解用静态类型语言编写的应用程序的两个案例研究表明了这种方法的适用性。最近与一个工业合作伙伴的经验，其中系统是用动态类型语言开发的，这促使我们调整了之前的建议，不仅将源代码作为输入，还将应用程序数据模式作为输入，以完成代码中缺失的信息，然后自动生成更有意义的图，帮助开发人员完成维护任务。在本文中，我们将介绍对通用方法的修改，以支持数据模式作为附加输入，并在一个工业案例研究中使用Ruby on Rails技术。本文最后解释了该仪器在哥伦比亚一家公司使用时的精度和性能以及吸取的教训。
{"title":"Improving understanding of dynamically typed software developed by agile practitioners","authors":"Jair García, Kelly Garcés","doi":"10.1145/3106237.3117772","DOIUrl":"https://doi.org/10.1145/3106237.3117772","url":null,"abstract":"Agile Development values working software over documentation. Therefore, in maintenance stages of existing software, the source code is the sole software artifact that developers have for analyzing the viability and impact of a new user story. Since functionality is often spread in hundreds of lines of code, it is hard for the developer to understand the system, which may lead to under-/overestimation of the new feature cost and rework/delays in the subsequent phases of development. In a previous work, we proposed a Model-Driven Reverse Engineering approach for obtaining software visualizations from source code. Two case studies of comprehension of applications written in statically typed languages have shown the applicability of this approach. A recent experience with an industrial partner, where the systems are developed on dynamically typed languages, has motivated us to adapt the previous proposal to take as input not only the source code but also the application data schema to complete the information that is missing in the code, and then automatically generate more meaningful diagrams that help developers in maintenance tasks. In this article, we present the adaptation of the general approach to support data schema as an additional input and its instrumentation in an industrial case study where the technology is Ruby on Rails. The paper ends by explaining the precision and performance of the instrumentation when used in a Colombian company as well as lessons learned.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122895520","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}
Stolen passwords, compromised medical records, taking the internet out through video cameras– cybersecurity breaches are in the news every day. Despite all this, the practice of cybersecurity today is generally reactive rather than proactive. That is, rather than improving their defenses in advance, organizations react to attacks once they have occurred by patching the individual vulnerabilities that led to those attacks. Researchers engineer solutions to the latest form of attack. What we need, instead, are scientifically founded design principles for building in security mechanisms from the beginning, giving protection against broad classes of attacks. Through scientific measurement, we can improve our ability to make decisions that are evidence-based, proactive, and long-sighted. Recognizing these needs, the US National Security Agency (NSA) devised a new framework for collaborative research, the “Lablet” structure, with the intent to more aggressively advance the science of cybersecurity. A key motivation was to catalyze a shift in relevant areas towards a more organized and cohesive scientific community. The NSA named Carnegie Mellon University, North Carolina State University, and the University of Illinois – Urbana Champaign its initial Lablets in 2011, and added the University of Maryland in 2014. This talk will reflect on the structure of the collaborative research efforts of the Lablets, lessons learned in the transition to more scientific concepts to cybersecurity, research results in solving five hard security problems, and methods that are being used for the measurement of scientific progress of the Lablet research.
{"title":"Verifying the forecast: how climate models are developed and tested (invited talk)","authors":"S. Easterbrook","doi":"10.1145/3106237.3121271","DOIUrl":"https://doi.org/10.1145/3106237.3121271","url":null,"abstract":"Stolen passwords, compromised medical records, taking the internet out through video cameras– cybersecurity breaches are in the news every day. Despite all this, the practice of cybersecurity today is generally reactive rather than proactive. That is, rather than improving their defenses in advance, organizations react to attacks once they have occurred by patching the individual vulnerabilities that led to those attacks. Researchers engineer solutions to the latest form of attack. What we need, instead, are scientifically founded design principles for building in security mechanisms from the beginning, giving protection against broad classes of attacks. Through scientific measurement, we can improve our ability to make decisions that are evidence-based, proactive, and long-sighted. Recognizing these needs, the US National Security Agency (NSA) devised a new framework for collaborative research, the “Lablet” structure, with the intent to more aggressively advance the science of cybersecurity. A key motivation was to catalyze a shift in relevant areas towards a more organized and cohesive scientific community. The NSA named Carnegie Mellon University, North Carolina State University, and the University of Illinois – Urbana Champaign its initial Lablets in 2011, and added the University of Maryland in 2014. This talk will reflect on the structure of the collaborative research efforts of the Lablets, lessons learned in the transition to more scientific concepts to cybersecurity, research results in solving five hard security problems, and methods that are being used for the measurement of scientific progress of the Lablet research.","PeriodicalId":313494,"journal":{"name":"Proceedings of the 2017 11th Joint Meeting on Foundations of Software Engineering","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124549396","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}
Julian Thomé, Lwin Khin Shar, D. Bianculli, L. Briand
JoanAudit is a static analysis tool to assist security auditors in auditing Web applications and Web services for common injection vulnerabilities during software development. It automatically identifies parts of the program code that are relevant for security and generates an HTML report to guide security auditors audit the source code in a scalable way. JoanAudit is configured with various security-sensitive input sources and sinks relevant to injection vulnerabilities and standard sanitization procedures that prevent these vulnerabilities. It can also automatically fix some cases of vulnerabilities in source code — cases where inputs are directly used in sinks without any form of sanitization — by using standard sanitization procedures. Our evaluation shows that by using JoanAudit, security auditors are required to inspect only 1% of the total code for auditing common injection vulnerabilities. The screen-cast demo is available at https://github.com/julianthome/joanaudit.
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