{"title":"The Jean-Claude Laprie Award","authors":"","doi":"10.1109/dsn.2019.00012","DOIUrl":"https://doi.org/10.1109/dsn.2019.00012","url":null,"abstract":"","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126307088","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}
The safety of critical cyber-physical IoT devices hinges on the security of their embedded software that implements control algorithms for monitoring and control of the associated physical processes, e.g., robotics and drones. Reverse engineering of the corresponding embedded controller software binaries enables their security analysis by extracting high-level, domain-specific, and cyber-physical execution semantic information from executables. We present MISMO, a domain-specific reverse engineering framework for embedded binary code in emerging cyber-physical IoT control application domains. The reverse engineering outcomes can be used for firmware vulnerability assessment, memory forensics analysis, targeted memory data attacks, or binary patching for dynamic selective memory protection (e.g., important control algorithm parameters). MISMO performs semantic-matching at an algorithmic level that can help with the understanding of any possible cyber-physical security flaws. MISMO compares low-level binary symbolic values and high-level algorithmic expressions to extract domain-specific semantic information for the binary's code and data. MISMO enables a finer-grained understanding of the controller by identifying the specific control and state estimation algorithms used. We evaluated MISMO on 2,263 popular firmware binaries by 30 commercial vendors from 6 application domains including drones, self-driving cars, smart homes, robotics, 3D printers, and the Linux kernel controllers. The results show that MISMO can accurately extract the algorithm-level semantics of the embedded binary code and data regions. We discovered a zero-day vulnerability in the Linux kernel controllers versions 3.13 and above.
{"title":"Tell Me More Than Just Assembly! Reversing Cyber-Physical Execution Semantics of Embedded IoT Controller Software Binaries","authors":"Pengfei Sun, Luis Garcia, S. Zonouz","doi":"10.1109/DSN.2019.00045","DOIUrl":"https://doi.org/10.1109/DSN.2019.00045","url":null,"abstract":"The safety of critical cyber-physical IoT devices hinges on the security of their embedded software that implements control algorithms for monitoring and control of the associated physical processes, e.g., robotics and drones. Reverse engineering of the corresponding embedded controller software binaries enables their security analysis by extracting high-level, domain-specific, and cyber-physical execution semantic information from executables. We present MISMO, a domain-specific reverse engineering framework for embedded binary code in emerging cyber-physical IoT control application domains. The reverse engineering outcomes can be used for firmware vulnerability assessment, memory forensics analysis, targeted memory data attacks, or binary patching for dynamic selective memory protection (e.g., important control algorithm parameters). MISMO performs semantic-matching at an algorithmic level that can help with the understanding of any possible cyber-physical security flaws. MISMO compares low-level binary symbolic values and high-level algorithmic expressions to extract domain-specific semantic information for the binary's code and data. MISMO enables a finer-grained understanding of the controller by identifying the specific control and state estimation algorithms used. We evaluated MISMO on 2,263 popular firmware binaries by 30 commercial vendors from 6 application domains including drones, self-driving cars, smart homes, robotics, 3D printers, and the Linux kernel controllers. The results show that MISMO can accurately extract the algorithm-level semantics of the embedded binary code and data regions. We discovered a zero-day vulnerability in the Linux kernel controllers versions 3.13 and above.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116254789","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}
We present an implementation of an eventually perfect failure detector in an arbitrarily connected, partitionable network. We assume ADD channels: for each one there exist constants K, D, not known to the processes, such that for every K consecutive messages sent in one direction, at least one is delivered within time D. The best previous implementation used messages of bounded size, but exponential in n, the number of nodes. The main contribution of this paper is a novel use of time-to-live values in the design of failure detectors, obtaining a flexible implementation that uses messages of size O(n log n)
{"title":"An Eventually Perfect Failure Detector for Networks of Arbitrary Topology Connected with ADD Channels Using Time-To-Live Values","authors":"Karla Vargas, S. Rajsbaum","doi":"10.1109/DSN.2019.00038","DOIUrl":"https://doi.org/10.1109/DSN.2019.00038","url":null,"abstract":"We present an implementation of an eventually perfect failure detector in an arbitrarily connected, partitionable network. We assume ADD channels: for each one there exist constants K, D, not known to the processes, such that for every K consecutive messages sent in one direction, at least one is delivered within time D. The best previous implementation used messages of bounded size, but exponential in n, the number of nodes. The main contribution of this paper is a novel use of time-to-live values in the design of failure detectors, obtaining a flexible implementation that uses messages of size O(n log n)","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125479324","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}
Erasure coding offers a storage-efficient redundancy mechanism for maintaining data availability guarantees in large-scale storage clusters, yet it also incurs high performance overhead in failure repair. Recent developments in accurate disk failure prediction allow soon-to-fail (STF) nodes to be repaired in advance, thereby opening new opportunities for accelerating failure repair in erasure-coded storage. To this end, we present a fast predictive repair solution called FastPR, which carefully couples two repair methods, namely migration (i.e., relocating the chunks of an STF node) and reconstruction (i.e., decoding the chunks of an STF node through erasure coding), so as to fully parallelize the repair operation across the storage cluster. FastPR solves a bipartite maximum matching problem and schedules both migration and reconstruction in a parallel fashion. We show that FastPR significantly reduces the repair time over the baseline repair approaches via mathematical analysis, large-scale simulation, and Amazon EC2 experiments.
{"title":"Fast Predictive Repair in Erasure-Coded Storage","authors":"Zhirong Shen, Xiaolu Li, P. Lee","doi":"10.1109/DSN.2019.00062","DOIUrl":"https://doi.org/10.1109/DSN.2019.00062","url":null,"abstract":"Erasure coding offers a storage-efficient redundancy mechanism for maintaining data availability guarantees in large-scale storage clusters, yet it also incurs high performance overhead in failure repair. Recent developments in accurate disk failure prediction allow soon-to-fail (STF) nodes to be repaired in advance, thereby opening new opportunities for accelerating failure repair in erasure-coded storage. To this end, we present a fast predictive repair solution called FastPR, which carefully couples two repair methods, namely migration (i.e., relocating the chunks of an STF node) and reconstruction (i.e., decoding the chunks of an STF node through erasure coding), so as to fully parallelize the repair operation across the storage cluster. FastPR solves a bipartite maximum matching problem and schedules both migration and reconstruction in a parallel fashion. We show that FastPR significantly reduces the repair time over the baseline repair approaches via mathematical analysis, large-scale simulation, and Amazon EC2 experiments.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116548350","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}
{"title":"William C. Carter Award","authors":"","doi":"10.1109/dsn.2019.00011","DOIUrl":"https://doi.org/10.1109/dsn.2019.00011","url":null,"abstract":"","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133090603","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}
Jiaqi Peng, Feng Li, Bingchang Liu, Lili Xu, Binghong Liu, Kai Chen, Wei Huo
Discovering 1-day vulnerabilities in binary patches is worthwhile but challenging. One of the key difficulties lies in generating inputs that could reach the patched code snippet while making the unpatched program crash. In this paper, we named it as a target-oriented input generation problem or a ToIG problem for clarity. Existing solutions for the ToIG problem either suffer from path explosion or may get stuck by complex checks. In the paper, we present a new solution to improve the efficiency of ToIG which leverage a combination of a distance-based directed fuzzing mechanism and a dominator-based directed symbolic execution mechanism. To demonstrate its efficiency, we design and implement 1dVul, a tool for 1-day vulnerability discovering at binary-level, based on the solution. Demonstrations show that 1dVul has successfully generated inputs for 130 targets from a total of 209 patch targets identified from applications in DARPA Cyber Grant Challenge, while the state-of-the-art solutions AFLGo and Driller can only reach 99 and 107 targets, respectively, within the same limited time budget. Further-more, 1dVul runs 2.2X and 3.6X faster than AFLGo and Driller, respectively, and has confirmed 96 vulnerabilities from the unpatched programs.
{"title":"1dVul: Discovering 1-Day Vulnerabilities through Binary Patches","authors":"Jiaqi Peng, Feng Li, Bingchang Liu, Lili Xu, Binghong Liu, Kai Chen, Wei Huo","doi":"10.1109/DSN.2019.00066","DOIUrl":"https://doi.org/10.1109/DSN.2019.00066","url":null,"abstract":"Discovering 1-day vulnerabilities in binary patches is worthwhile but challenging. One of the key difficulties lies in generating inputs that could reach the patched code snippet while making the unpatched program crash. In this paper, we named it as a target-oriented input generation problem or a ToIG problem for clarity. Existing solutions for the ToIG problem either suffer from path explosion or may get stuck by complex checks. In the paper, we present a new solution to improve the efficiency of ToIG which leverage a combination of a distance-based directed fuzzing mechanism and a dominator-based directed symbolic execution mechanism. To demonstrate its efficiency, we design and implement 1dVul, a tool for 1-day vulnerability discovering at binary-level, based on the solution. Demonstrations show that 1dVul has successfully generated inputs for 130 targets from a total of 209 patch targets identified from applications in DARPA Cyber Grant Challenge, while the state-of-the-art solutions AFLGo and Driller can only reach 99 and 107 targets, respectively, within the same limited time budget. Further-more, 1dVul runs 2.2X and 3.6X faster than AFLGo and Driller, respectively, and has confirmed 96 vulnerabilities from the unpatched programs.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130498237","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}
Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful "0-day" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those "0-day" attacks to at least "n-day" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.
{"title":"Detecting \"0-Day\" Vulnerability: An Empirical Study of Secret Security Patch in OSS","authors":"Xinda Wang, Kun Sun, A. Batcheller, S. Jajodia","doi":"10.1109/DSN.2019.00056","DOIUrl":"https://doi.org/10.1109/DSN.2019.00056","url":null,"abstract":"Security patches in open source software (OSS) not only provide security fixes to identified vulnerabilities, but also make the vulnerable code public to the attackers. Therefore, armored attackers may misuse this information to launch N-day attacks on unpatched OSS versions. The best practice for preventing this type of N-day attacks is to keep upgrading the software to the latest version in no time. However, due to the concerns on reputation and easy software development management, software vendors may choose to secretly patch their vulnerabilities in a new version without reporting them to CVE or even providing any explicit description in their change logs. When those secretly patched vulnerabilities are being identified by armored attackers, they can be turned into powerful \"0-day\" attacks, which can be exploited to compromise not only unpatched version of the same software, but also similar types of OSS (e.g., SSL libraries) that may contain the same vulnerability due to code clone or similar design/implementation logic. Therefore, it is critical to identify secret security patches and downgrade the risk of those \"0-day\" attacks to at least \"n-day\" attacks. In this paper, we develop a defense system and implement a toolset to automatically identify secret security patches in open source software. To distinguish security patches from other patches, we first build a security patch database that contains more than 4700 security patches mapping to the records in CVE list. Next, we identify a set of features to help distinguish security patches from non-security ones using machine learning approaches. Finally, we use code clone identification mechanisms to discover similar patches or vulnerabilities in similar types of OSS. The experimental results show our approach can achieve good detection performance. A case study on OpenSSL, LibreSSL, and BoringSSL discovers 12 secret security patches.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"176 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121793114","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}
Minesh Patel, Jeremie S. Kim, Hasan Hassan, O. Mutlu
Experimental characterization of DRAM errors is a powerful technique for understanding DRAM behavior and provides valuable insights for improving overall system performance, energy efficiency, and reliability. Unfortunately, recent DRAM technology scaling issues are forcing manufacturers to adopt on-die error-correction codes (ECC), which pose a significant challenge for DRAM error characterization studies by obfuscating raw error distributions using undocumented, proprietary, and opaque error-correction hardware. As we show in this work, errors observed in devices with on-die ECC no longer follow expected, well-studied distributions (e.g., lognormal retention times) but rather depend on the particular ECC scheme used.
{"title":"Understanding and Modeling On-Die Error Correction in Modern DRAM: An Experimental Study Using Real Devices","authors":"Minesh Patel, Jeremie S. Kim, Hasan Hassan, O. Mutlu","doi":"10.1109/DSN.2019.00017","DOIUrl":"https://doi.org/10.1109/DSN.2019.00017","url":null,"abstract":"Experimental characterization of DRAM errors is a powerful technique for understanding DRAM behavior and provides valuable insights for improving overall system performance, energy efficiency, and reliability. Unfortunately, recent DRAM technology scaling issues are forcing manufacturers to adopt on-die error-correction codes (ECC), which pose a significant challenge for DRAM error characterization studies by obfuscating raw error distributions using undocumented, proprietary, and opaque error-correction hardware. As we show in this work, errors observed in devices with on-die ECC no longer follow expected, well-studied distributions (e.g., lognormal retention times) but rather depend on the particular ECC scheme used.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124211873","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}
P. Ramalhete, Andreia Correia, P. Felber, Nachshon Cohen
A persistent transactional memory (PTM) library provides an easy-to-use interface to programmers for using byte-addressable non-volatile memory (NVM). Previously proposed PTMs have, so far, been blocking. We present OneFile, the first wait-free PTM with integrated wait-free memory reclamation. We have designed and implemented two variants of the OneFile, one with lock-free progress and the other with bounded wait-free progress. We additionally present software transactional memory (STM) implementations of the lock-free and wait-free algorithms targeting volatile memory. Each of our PTMs and STMs is implemented as a single C++ file with ~1,000 lines of code, making them versatile to use. Equipped with these PTMs and STMs, non-expert developers can design and implement their own lock-free and wait-free data structures on NVM, thus making lock-free programming accessible to common software developers.
{"title":"OneFile: A Wait-Free Persistent Transactional Memory","authors":"P. Ramalhete, Andreia Correia, P. Felber, Nachshon Cohen","doi":"10.1109/DSN.2019.00028","DOIUrl":"https://doi.org/10.1109/DSN.2019.00028","url":null,"abstract":"A persistent transactional memory (PTM) library provides an easy-to-use interface to programmers for using byte-addressable non-volatile memory (NVM). Previously proposed PTMs have, so far, been blocking. We present OneFile, the first wait-free PTM with integrated wait-free memory reclamation. We have designed and implemented two variants of the OneFile, one with lock-free progress and the other with bounded wait-free progress. We additionally present software transactional memory (STM) implementations of the lock-free and wait-free algorithms targeting volatile memory. Each of our PTMs and STMs is implemented as a single C++ file with ~1,000 lines of code, making them versatile to use. Equipped with these PTMs and STMs, non-expert developers can design and implement their own lock-free and wait-free data structures on NVM, thus making lock-free programming accessible to common software developers.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129869355","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}
Onur Zungur, Guillermo Suarez-Tangil, G. Stringhini, Manuel Egele
Companies adopt Bring Your Own Device (BYOD) policies extensively, for both convenience and cost management. The compelling way of putting private and business related applications (apps) on the same device leads to the widespread usage of employee owned devices to access sensitive company data and services. Such practices create a security risk as a legitimate app may send business-sensitive data to third party servers through detrimental app functions or packaged libraries. In this paper, we propose BorderPatrol, a system for extracting contextual data that businesses can leverage to enforce access control in BYOD-enabled corporate networks through fine-grained policies. BorderPatrol extracts contextual information, which is the stack trace of the app function that generated the network traffic, on provisioned user devices and transfers this data in IP headers to enforce desired policies at network routers. BorderPatrol provides a way to selectively prevent undesired functionalities, such as analytics activities or advertisements, and help enforce information dissemination policies of the company while leaving other functions of the app intact. Using 2,000 apps, we demonstrate that BorderPatrol is effective in preventing packets which originate from previously identified analytics and advertisement libraries from leaving the network premises. In addition, we show BorderPatrol's capability in selectively preventing undesirable app functions using case studies.
{"title":"BorderPatrol: Securing BYOD using Fine-Grained Contextual Information","authors":"Onur Zungur, Guillermo Suarez-Tangil, G. Stringhini, Manuel Egele","doi":"10.1109/DSN.2019.00054","DOIUrl":"https://doi.org/10.1109/DSN.2019.00054","url":null,"abstract":"Companies adopt Bring Your Own Device (BYOD) policies extensively, for both convenience and cost management. The compelling way of putting private and business related applications (apps) on the same device leads to the widespread usage of employee owned devices to access sensitive company data and services. Such practices create a security risk as a legitimate app may send business-sensitive data to third party servers through detrimental app functions or packaged libraries. In this paper, we propose BorderPatrol, a system for extracting contextual data that businesses can leverage to enforce access control in BYOD-enabled corporate networks through fine-grained policies. BorderPatrol extracts contextual information, which is the stack trace of the app function that generated the network traffic, on provisioned user devices and transfers this data in IP headers to enforce desired policies at network routers. BorderPatrol provides a way to selectively prevent undesired functionalities, such as analytics activities or advertisements, and help enforce information dissemination policies of the company while leaving other functions of the app intact. Using 2,000 apps, we demonstrate that BorderPatrol is effective in preventing packets which originate from previously identified analytics and advertisement libraries from leaving the network premises. In addition, we show BorderPatrol's capability in selectively preventing undesirable app functions using case studies.","PeriodicalId":271955,"journal":{"name":"2019 49th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123691507","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}
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