Zhengyang Qu, Shahid Alam, Yan Chen, Xiaoyong Zhou, W. Hong, Ryan D. Riley
Android has provided dynamic code loading (DCL) since API level one. DCL allows an app developer to load additional code at runtime. DCL raises numerous challenges with regards to security and accountability analysis of apps. While previous studies have investigated DCL on Android, in this paper we formulate and answer three critical questions that are missing from previous studies: (1) Where does the loaded code come from (remotely fetched or locally packaged), and who is the responsible entity to invoke its functionality? (2) In what ways is DCL utilized to harden mobile apps, specifically, application obfuscation? (3) What are the security risks and implications that can be found from DCL in off-the-shelf apps? We design and implement DyDroid, a system which uses both dynamic and static analysis to analyze dynamically loaded code. Dynamic analysis is used to automatically exercise apps, capture DCL behavior, and intercept the loaded code. Static analysis is used to investigate malicious behavior and privacy leakage in that dynamically loaded code. We have used DyDroid to analyze over 46K apps with little manual intervention, allowing us to conduct a large-scale measurement to investigate five aspects of DCL, such as source identification, malware detection, vulnerability analysis, obfuscation analysis, and privacy tracking analysis. We have several interesting findings. (1) 27 apps are found to violate the content policy of Google Play by executing code downloaded from remote servers. (2) We determine the distribution, pros/cons, and implications of several common obfuscation methods, including DEX encryption/loading. (3) DCL's stealthiness enables it to be a channel to deploy malware, and we find 87 apps loading malicious binaries which are not detected by existing antivirus tools. (4) We found 14 apps that are vulnerable to code injection attacks due to dynamically loading code which is writable by other apps. (5) DCL is mainly used by third-party SDKs, meaning that app developers may not know what sort of sensitive functionality is injected into their apps.
{"title":"DyDroid: Measuring Dynamic Code Loading and Its Security Implications in Android Applications","authors":"Zhengyang Qu, Shahid Alam, Yan Chen, Xiaoyong Zhou, W. Hong, Ryan D. Riley","doi":"10.1109/DSN.2017.14","DOIUrl":"https://doi.org/10.1109/DSN.2017.14","url":null,"abstract":"Android has provided dynamic code loading (DCL) since API level one. DCL allows an app developer to load additional code at runtime. DCL raises numerous challenges with regards to security and accountability analysis of apps. While previous studies have investigated DCL on Android, in this paper we formulate and answer three critical questions that are missing from previous studies: (1) Where does the loaded code come from (remotely fetched or locally packaged), and who is the responsible entity to invoke its functionality? (2) In what ways is DCL utilized to harden mobile apps, specifically, application obfuscation? (3) What are the security risks and implications that can be found from DCL in off-the-shelf apps? We design and implement DyDroid, a system which uses both dynamic and static analysis to analyze dynamically loaded code. Dynamic analysis is used to automatically exercise apps, capture DCL behavior, and intercept the loaded code. Static analysis is used to investigate malicious behavior and privacy leakage in that dynamically loaded code. We have used DyDroid to analyze over 46K apps with little manual intervention, allowing us to conduct a large-scale measurement to investigate five aspects of DCL, such as source identification, malware detection, vulnerability analysis, obfuscation analysis, and privacy tracking analysis. We have several interesting findings. (1) 27 apps are found to violate the content policy of Google Play by executing code downloaded from remote servers. (2) We determine the distribution, pros/cons, and implications of several common obfuscation methods, including DEX encryption/loading. (3) DCL's stealthiness enables it to be a channel to deploy malware, and we find 87 apps loading malicious binaries which are not detected by existing antivirus tools. (4) We found 14 apps that are vulnerable to code injection attacks due to dynamically loading code which is writable by other apps. (5) DCL is mainly used by third-party SDKs, meaning that app developers may not know what sort of sensitive functionality is injected into their apps.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127020542","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}
Smart meters can record fine-grained power consumption data and provide such data to the power supplier through realtime communications. Although smart meters can make power management more efficient and fault-tolerant, they also pose bigger threats to user privacy. Data from smart meters contain fine-grained power consumption information of home appliances and thus can be used to infer the ON/OFF states of home appliances. This problem has received some attention in the literature, however, most of them focus on active power based attacks. This paper focuses on reactive power and demonstrates how attackers can exploit reactive power data to infer appliance usage information. Experiments on real residential smart meter data show that our proposed attack can identify the ON/OFF events of home appliance with high accuracy. To protect users against such attacks, a novel defense technique called Reactive Power Obfuscation (RPO) is proposed. RPO can mask the true reactive power demand from the smart meter by using a capacitor to store and provide reactive power in a controlled manner. We evaluate the performance of RPO based on real household power consumption data. Evaluation results show that the ON/OFF events of home appliances can hardly be revealed from reactive power data when RPO is applied.
{"title":"Privacy Disclosure through Smart Meters: Reactive Power Based Attack and Defense","authors":"Jingyao Fan, Qinghua Li, G. Cao","doi":"10.1109/DSN.2017.13","DOIUrl":"https://doi.org/10.1109/DSN.2017.13","url":null,"abstract":"Smart meters can record fine-grained power consumption data and provide such data to the power supplier through realtime communications. Although smart meters can make power management more efficient and fault-tolerant, they also pose bigger threats to user privacy. Data from smart meters contain fine-grained power consumption information of home appliances and thus can be used to infer the ON/OFF states of home appliances. This problem has received some attention in the literature, however, most of them focus on active power based attacks. This paper focuses on reactive power and demonstrates how attackers can exploit reactive power data to infer appliance usage information. Experiments on real residential smart meter data show that our proposed attack can identify the ON/OFF events of home appliance with high accuracy. To protect users against such attacks, a novel defense technique called Reactive Power Obfuscation (RPO) is proposed. RPO can mask the true reactive power demand from the smart meter by using a capacitor to store and provide reactive power in a controlled manner. We evaluate the performance of RPO based on real household power consumption data. Evaluation results show that the ON/OFF events of home appliances can hardly be revealed from reactive power data when RPO is applied.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124336952","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}
Coordination services are essential building blocks of today's data centers as they provide processes of distributed applications with means to reliably exchange data. Consequently, coordination services must deliver high performance to ensure that they do not become a bottleneck for the applications depending on them. Unfortunately, the design of existing services such as ZooKeeper prevents them from scaling with the number of cores on a machine. In this paper, we address this problem with Agora, a high-performance coordination service that is able to both effectively and efficiently utilize multi-core machines. Agora relies on a primary-backup replication architecture that partitions the workload on each server to achieve parallelism while still providing similar consistency guarantees as ZooKeeper. Our evaluation shows that Agora scales with the number of cores and thus can fully utilize the network resources available.
{"title":"Agora: A Dependable High-Performance Coordination Service for Multi-cores","authors":"Rainer Schiekofer, J. Behl, T. Distler","doi":"10.1109/DSN.2017.23","DOIUrl":"https://doi.org/10.1109/DSN.2017.23","url":null,"abstract":"Coordination services are essential building blocks of today's data centers as they provide processes of distributed applications with means to reliably exchange data. Consequently, coordination services must deliver high performance to ensure that they do not become a bottleneck for the applications depending on them. Unfortunately, the design of existing services such as ZooKeeper prevents them from scaling with the number of cores on a machine. In this paper, we address this problem with Agora, a high-performance coordination service that is able to both effectively and efficiently utilize multi-core machines. Agora relies on a primary-backup replication architecture that partitions the workload on each server to achieve parallelism while still providing similar consistency guarantees as ZooKeeper. Our evaluation shows that Agora scales with the number of cores and thus can fully utilize the network resources available.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"51 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131086710","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}
This paper studies the impact of logical synthesizers parameters on the performance, power-consumption, area (PPA) and dependability of HW implementations. Deducing optimal synthesis-parameter configurations attending to specific goals is challenging even for simple HW models. The proposal relies on fractional factorial design of experiments to minimize simulation-based fault-injection time. The set of synthesis parameters with an statistically significant impact on PPA and dependability goals is then deduced and regression models are generated to estimate such impact for any synthesis-parameter configuration. Optimal configurations are finally selected attending to specific implementation goals. The whole methodology is automated and applied onto the Xilinx XST synthesizer working on a simplex and TMR version of an enhanced Intel 8051 microcontroller model, but it can be potentially applied to any synthesizer and any HDL-based model. Results show that non-negligible benefits in terms of PPA and dependability can be obtained by simply tuning synthesizer parameters in a proper way.
{"title":"Dependability-Aware Design Space Exploration for Optimal Synthesis Parameters Tuning","authors":"I. Tuzov, D. Andrés, J. Ruiz","doi":"10.1109/DSN.2017.18","DOIUrl":"https://doi.org/10.1109/DSN.2017.18","url":null,"abstract":"This paper studies the impact of logical synthesizers parameters on the performance, power-consumption, area (PPA) and dependability of HW implementations. Deducing optimal synthesis-parameter configurations attending to specific goals is challenging even for simple HW models. The proposal relies on fractional factorial design of experiments to minimize simulation-based fault-injection time. The set of synthesis parameters with an statistically significant impact on PPA and dependability goals is then deduced and regression models are generated to estimate such impact for any synthesis-parameter configuration. Optimal configurations are finally selected attending to specific implementation goals. The whole methodology is automated and applied onto the Xilinx XST synthesizer working on a simplex and TMR version of an enhanced Intel 8051 microcontroller model, but it can be potentially applied to any synthesizer and any HDL-based model. Results show that non-negligible benefits in terms of PPA and dependability can be obtained by simply tuning synthesizer parameters in a proper way.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"221 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132550898","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}
Software-defined networking (SDN) has recently attracted interest as a way to provide cyber resiliency because of its programmable and logically centralized nature. However, the security of the SDN architecture itself against malicious attacks is not well understood and must be ensured in order to provide cyber resiliency to systems that use SDNs. In this paper, we present ATTAIN, an attack injection framework for OpenFlow-based SDN architectures. First, we define an attack model that relates system components to an attacker's capability to influence control plane behavior. Second, we define an attack language for writing control plane attacks that can be used to evaluate SDN implementations. Third, we describe an attack injector architecture that actuates attacks in networks. Finally, we evaluate our framework with an enterprise network case study by writing and running attacks with popular SDN controllers.
{"title":"ATTAIN: An Attack Injection Framework for Software-Defined Networking","authors":"Benjamin E. Ujcich, Uttam Thakore, W. Sanders","doi":"10.1109/DSN.2017.59","DOIUrl":"https://doi.org/10.1109/DSN.2017.59","url":null,"abstract":"Software-defined networking (SDN) has recently attracted interest as a way to provide cyber resiliency because of its programmable and logically centralized nature. However, the security of the SDN architecture itself against malicious attacks is not well understood and must be ensured in order to provide cyber resiliency to systems that use SDNs. In this paper, we present ATTAIN, an attack injection framework for OpenFlow-based SDN architectures. First, we define an attack model that relates system components to an attacker's capability to influence control plane behavior. Second, we define an attack language for writing control plane attacks that can be used to evaluate SDN implementations. Third, we describe an attack injector architecture that actuates attacks in networks. Finally, we evaluate our framework with an enterprise network case study by writing and running attacks with popular SDN controllers.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"56 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125089150","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}
Online social networks are known to be vulnerable to the so-called Sybil attack, in which an attacker maintains massive fake accounts (also called Sybils) and uses them to perform various malicious activities. Therefore, Sybil detection is a fundamental security research problem in online social networks. Random walk based methods, which leverage the structure of an online social network to distribute reputation scores for users, have been demonstrated to be promising in certain real-world online social networks. In particular, random walk based methods have three desired features: they can have theoretically guaranteed performance for online social networks that have the fast-mixing property, they are accurate when the social network has strong homophily property, and they can be scalable to large-scale online social networks. However, existing random walk based methods suffer from several key limitations: 1) they can only leverage either labeled benign users or labeled Sybils, but not both, 2) they have limited detection accuracy for weak-homophily social networks, and 3) they are not robust to label noise in the training dataset. In this work, we propose a new random walk based Sybil detection method called SybilWalk. SybilWalk addresses the limitations of existing random walk based methods while maintaining their desired features. We perform both theoretical and empirical evaluations to compare SybilWalk with previous random walk based methods. Theoretically, for online social networks with the fast-mixing property, SybilWalk has a tighter asymptotical bound on the number of Sybils that are falsely accepted into the social network than all existing random walk based methods. Empirically, we compare SybilWalk with previous random walk based methods using both social networks with synthesized Sybils and a large-scale Twitter dataset with real Sybils. Our empirical results demonstrate that 1) SybilWalk is substantially more accurate than existing random walk based methods for weakhomophily social networks, 2) SybilWalk is substantially more robust to label noise than existing random walk based methods, and 3) SybilWalk is as scalable as the most efficient existing random walk based methods. In particular, on the Twitter dataset, SybilWalk achieves a false positive rate of 1.3% and a false negative rate of 17.3%.
{"title":"Random Walk Based Fake Account Detection in Online Social Networks","authors":"Jinyuan Jia, Binghui Wang, N. Gong","doi":"10.1109/DSN.2017.55","DOIUrl":"https://doi.org/10.1109/DSN.2017.55","url":null,"abstract":"Online social networks are known to be vulnerable to the so-called Sybil attack, in which an attacker maintains massive fake accounts (also called Sybils) and uses them to perform various malicious activities. Therefore, Sybil detection is a fundamental security research problem in online social networks. Random walk based methods, which leverage the structure of an online social network to distribute reputation scores for users, have been demonstrated to be promising in certain real-world online social networks. In particular, random walk based methods have three desired features: they can have theoretically guaranteed performance for online social networks that have the fast-mixing property, they are accurate when the social network has strong homophily property, and they can be scalable to large-scale online social networks. However, existing random walk based methods suffer from several key limitations: 1) they can only leverage either labeled benign users or labeled Sybils, but not both, 2) they have limited detection accuracy for weak-homophily social networks, and 3) they are not robust to label noise in the training dataset. In this work, we propose a new random walk based Sybil detection method called SybilWalk. SybilWalk addresses the limitations of existing random walk based methods while maintaining their desired features. We perform both theoretical and empirical evaluations to compare SybilWalk with previous random walk based methods. Theoretically, for online social networks with the fast-mixing property, SybilWalk has a tighter asymptotical bound on the number of Sybils that are falsely accepted into the social network than all existing random walk based methods. Empirically, we compare SybilWalk with previous random walk based methods using both social networks with synthesized Sybils and a large-scale Twitter dataset with real Sybils. Our empirical results demonstrate that 1) SybilWalk is substantially more accurate than existing random walk based methods for weakhomophily social networks, 2) SybilWalk is substantially more robust to label noise than existing random walk based methods, and 3) SybilWalk is as scalable as the most efficient existing random walk based methods. In particular, on the Twitter dataset, SybilWalk achieves a false positive rate of 1.3% and a false negative rate of 17.3%.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129879489","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}
Fan Yao, Yongbo Li, Yurong Chen, Hongfa Xue, Tian Lan, Guru Venkataramani
Identifying vulnerabilities in software systems is crucial to minimizing the damages that result from malicious exploits and software failures. This often requires proper identification of vulnerable execution paths that contain program vulnerabilities or bugs. However, with rapid rise in software complexity, it has become notoriously difficult to identify such vulnerable paths through exhaustively searching the entire program execution space. In this paper, we propose StatSym, a novel, automated Statistics-Guided Symbolic Execution framework that integrates the swiftness of statistical inference and the rigorousness of symbolic execution techniques to achieve precision, agility and scalability in vulnerable program path discovery. Our solution first leverages statistical analysis of program runtime information to construct predicates that are indicative of potential vulnerability in programs. These statistically identified paths, along with the associated predicates, effectively drive a symbolic execution engine to verify the presence of vulnerable paths and reduce their time to solution. We evaluate StatSym on four real-world applications including polymorph, CTree, Grep and thttpd that come from diverse domains. Results show that StatSym is able to assist the symbolic executor, KLEE, in identifying the vulnerable paths for all of the four cases, whereas pure symbolic execution fails in three out of four applications due to memory space overrun.
{"title":"StatSym: Vulnerable Path Discovery through Statistics-Guided Symbolic Execution","authors":"Fan Yao, Yongbo Li, Yurong Chen, Hongfa Xue, Tian Lan, Guru Venkataramani","doi":"10.1109/DSN.2017.57","DOIUrl":"https://doi.org/10.1109/DSN.2017.57","url":null,"abstract":"Identifying vulnerabilities in software systems is crucial to minimizing the damages that result from malicious exploits and software failures. This often requires proper identification of vulnerable execution paths that contain program vulnerabilities or bugs. However, with rapid rise in software complexity, it has become notoriously difficult to identify such vulnerable paths through exhaustively searching the entire program execution space. In this paper, we propose StatSym, a novel, automated Statistics-Guided Symbolic Execution framework that integrates the swiftness of statistical inference and the rigorousness of symbolic execution techniques to achieve precision, agility and scalability in vulnerable program path discovery. Our solution first leverages statistical analysis of program runtime information to construct predicates that are indicative of potential vulnerability in programs. These statistically identified paths, along with the associated predicates, effectively drive a symbolic execution engine to verify the presence of vulnerable paths and reduce their time to solution. We evaluate StatSym on four real-world applications including polymorph, CTree, Grep and thttpd that come from diverse domains. Results show that StatSym is able to assist the symbolic executor, KLEE, in identifying the vulnerable paths for all of the four cases, whereas pure symbolic execution fails in three out of four applications due to memory space overrun.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126106331","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 Linux kernel is highly configurable, and thus, in principle, any line of code can be included or excluded from the compiled kernel based on configuration operations. Configurability complicates the task of a kernel janitor, who cleans up faults across the code base. A janitor may not be familiar with the configuration options that trigger compilation of a particular code line, leading him to believe that a fix has been compile-checked when this is not the case. We propose JMake, a mutation-based tool for signaling changed lines that are not subjected to the compiler. JMake shows that for most of the 12,000 file-modifying commits between Linux v4.3 and v4.4 the configuration chosen by the kernel allyesconfig option is sufficient, once the janitor chooses the correct architecture. For most commits, this check requires only 30 seconds or less. We then characterize the situations in which changed code is not subjected to compilation in practice.
{"title":"JMake: Dependable Compilation for Kernel Janitors","authors":"J. Lawall, Gilles Muller","doi":"10.1109/DSN.2017.62","DOIUrl":"https://doi.org/10.1109/DSN.2017.62","url":null,"abstract":"The Linux kernel is highly configurable, and thus, in principle, any line of code can be included or excluded from the compiled kernel based on configuration operations. Configurability complicates the task of a kernel janitor, who cleans up faults across the code base. A janitor may not be familiar with the configuration options that trigger compilation of a particular code line, leading him to believe that a fix has been compile-checked when this is not the case. We propose JMake, a mutation-based tool for signaling changed lines that are not subjected to the compiler. JMake shows that for most of the 12,000 file-modifying commits between Linux v4.3 and v4.4 the configuration chosen by the kernel allyesconfig option is sufficient, once the janitor chooses the correct architecture. For most commits, this check requires only 30 seconds or less. We then characterize the situations in which changed code is not subjected to compilation in practice.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130932651","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}
Multipath communication not only allows improved throughput but can also be used to leverage different path characteristics to best fulfill each application's objective. In particular, certain delay-sensitive applications, such as real-time voice and video communications, can usually withstand packet loss and aim to maximize throughput while keeping latency at a reasonable level. In such a context, one hard problem is to determine along which path the data should be transmitted or retransmitted. In this paper, we formulate this problem as a linear optimization, show bounds on the performance that can be obtained in a multipath paradigm, and show that path diversity is a strong asset for improving network performance. We also discuss how these theoretical limits can be approached in practice and present simulation results.
{"title":"Deadline-Aware Multipath Communication: An Optimization Problem","authors":"L. Chuat, A. Perrig, Yih-Chun Hu","doi":"10.1109/DSN.2017.32","DOIUrl":"https://doi.org/10.1109/DSN.2017.32","url":null,"abstract":"Multipath communication not only allows improved throughput but can also be used to leverage different path characteristics to best fulfill each application's objective. In particular, certain delay-sensitive applications, such as real-time voice and video communications, can usually withstand packet loss and aim to maximize throughput while keeping latency at a reasonable level. In such a context, one hard problem is to determine along which path the data should be transmitted or retransmitted. In this paper, we formulate this problem as a linear optimization, show bounds on the performance that can be obtained in a multipath paradigm, and show that path diversity is a strong asset for improving network performance. We also discuss how these theoretical limits can be approached in practice and present simulation results.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"281 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116203141","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}
Function recognition is one of the key tasks in binary analysis, instrumentation and reverse engineering. Previous approaches for this problem have relied on matching code patterns commonly observed at the beginning and end of functions. While early efforts relied on compiler idioms and expert-identified patterns, more recent works have systematized the process using machine-learning techniques. In contrast, we develop a novel static analysis based method in this paper. In particular, we combine a low-level technique for enumerating candidate functions with a novel static analysis for determining if these candidates exhibit the properties associated with a function interface. Both control-flow properties (e.g., returning to the location at the stack top at the function entry point) and data-flow properties (e.g., parameter passing via registers and the stack, and the degree of adherence to application-binary interface conventions) are checked. Our approach achieves an F1-score above 99% across a broad range of programs across multiple languages and compilers. More importantly, it achieves a 4x or higher reduction in error rate over best previous results.
{"title":"Function Interface Analysis: A Principled Approach for Function Recognition in COTS Binaries","authors":"Rui Qiao, R. Sekar","doi":"10.1109/DSN.2017.29","DOIUrl":"https://doi.org/10.1109/DSN.2017.29","url":null,"abstract":"Function recognition is one of the key tasks in binary analysis, instrumentation and reverse engineering. Previous approaches for this problem have relied on matching code patterns commonly observed at the beginning and end of functions. While early efforts relied on compiler idioms and expert-identified patterns, more recent works have systematized the process using machine-learning techniques. In contrast, we develop a novel static analysis based method in this paper. In particular, we combine a low-level technique for enumerating candidate functions with a novel static analysis for determining if these candidates exhibit the properties associated with a function interface. Both control-flow properties (e.g., returning to the location at the stack top at the function entry point) and data-flow properties (e.g., parameter passing via registers and the stack, and the degree of adherence to application-binary interface conventions) are checked. Our approach achieves an F1-score above 99% across a broad range of programs across multiple languages and compilers. More importantly, it achieves a 4x or higher reduction in error rate over best previous results.","PeriodicalId":426928,"journal":{"name":"2017 47th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116769015","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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