基于静态分析的强制访问控制框架的正确性验证

现阶段对操作系统的强制访问控制框架的正确性验证的研究主要集中于对授权钩子放置的验证.文中基于TrustedBSD MAC框架对强制访问控制框架的正确性验证问题进行了研究,在授权钩子放置验证的基础上,提出了安全标记的完全初始化验证和完全销毁验证.为了实现上述验证,文中提出了一个路径敏感的、基于用户自定义检查规则的静态分析方法.该方法通过对集成于编译器的静态分析工具mygcc进行扩展来验证强制访问控制框架的钩子放置的准确性和完备性.该方法具有完全的路径覆盖性,且具有低的误报率和时间开销.

二进制程序安全缺陷静态分析方法的研究综述

对现有二进制程序安全缺陷静态分析方法进行了综述和分析,提出了整个程序分析过程中的关键问题以及二进制程序安全分析的主要研究方向.通过对二进制程序缺陷静态分析流程的总结,发现二进制程序信息恢复是整个分析过程的关键,构造内容丰富的、通用的中间表示是二进制程序缺陷分析的重要研究方向.

Vacuum window of intermediate energy heavy-ion microbeam irradiation facility

The beam must be extracted into the air through the vacuum window to irradiate the living cell. In the window design, the material and thickness must be chosen to compromise the beam spot size broadening and the window safety. The structure-static analysis on the window of different structures and materials is done with the finite element analysis method, and the deformation and the equivalent stress axe simulated. The safety of these candidates is investigated using the intensity theory. In addition, the small angle scattering and the transverse range of ions are simulated using SRIM code, including all the effects on the beam spot size broadening, such as the incident ion energy, the material and the thickness of the window and the air composing. At last, the appropriate vacuum windows are presented, including the structure, material and thickness.

remote attestation for custom-built software

Huazhong Univ Sci & Technol, Natl Tech Univ Ukraine, Huazhong Normal Univ, Harbin Inst Technol, IEEE Ukraine Sect, I& M/CI Joint Chapter

节能型自治式多功能海洋环境监测系统的静力分析和姿态计算

海洋监测是人类认识海洋、研究海洋的有利工具，海洋自动观测仪器又是海洋监测技术最基本的硬件单元。文章介绍了一种具有自主知识产权的海洋仪器产品——节能型自治式多功能海洋环境监测系统，并对其做了静力分析和姿态计算。 该测量系统将定点锚泊潜标和浮标技术相结合，并创新性的融进了节能技术，使其具有长期、安全和多参数测量的工作特点。 为确保系统能在水下长期可靠地工作，文章对系统在水下的受力状况和姿态进行了计算。由于系统潜标式主浮体在水下几十米，海面波浪对其影响较小，仅对系统在海流的作用下进行水下静力分析。计算中首先根据总体技术要求进行合理的简化和假设，建立水下系统的数学模型，编制系统在水下的受力和姿态计算软件，完成系统的结构配置、受力分析和水下姿态的计算。 值得注意的是，该系统的潜标式主浮体受力分析方法与传统简单构形（一个浮体，一根索和一个锚）的分析方法不同，因为该潜标式主浮体不但受到下端缆绳的拉力，而且还受到连结上端搭载平台缆绳的拉力，同时还要考虑进变化海流对缆绳的作用。 最后，用MATLAB编制了相应程序。该程序操作方便，每次任务确定之后，只需向计算机输入阻尼系数、浮力重力值、浮球直径及潜标长度等参数，程序将自动计算出水下系统在 流速范围内各部件的横倾角、缆绳拉力及拉力与水平线（垂直线）夹角等有关参数，供系统配置做参考。

《机器人》杂志论文及作者的统计分析研究

本文通过对我国核心科技期刊《机器人》杂志 1990～ 1999年 10年间发表的论文及作者的统计分析 ,展示了我国在机器人学方面理论和应用研究的发展水平 ,揭示了从事机器人及相关技术理论与应用研究人员的现状和地区 ,系统分布的特点与规律 ,并以文献计量学的方法确定本刊的核心作者 ,活跃作者群的数量及比例 .对作者的年龄和性别结构、篇均作者数、作者增变量、滞稿期和论文基金项目等的统计结果进行综合分析 ,并提出几点看法 .

A Type System For Safe SN Resource Allocation

snBench is a platform on which novice users compose and deploy distributed Sense and Respond programs for simultaneous execution on a shared, distributed infrastructure. It is a natural imperative that we have the ability to (1) verify the safety/correctness of newly submitted tasks and (2) derive the resource requirements for these tasks such that correct allocation may occur. To achieve these goals we have established a multi-dimensional sized type system for our functional-style Domain Specific Language (DSL) called Sensor Task Execution Plan (STEP). In such a type system data types are annotated with a vector of size attributes (e.g., upper and lower size bounds). Tracking multiple size aspects proves essential in a system in which Images are manipulated as a first class data type, as image manipulation functions may have specific minimum and/or maximum resolution restrictions on the input they can correctly process. Through static analysis of STEP instances we not only verify basic type safety and establish upper computational resource bounds (i.e., time and space), but we also derive and solve data and resource sizing constraints (e.g., Image resolution, camera capabilities) from the implicit constraints embedded in program instances. In fact, the static methods presented here have benefit beyond their application to Image data, and may be extended to other data types that require tracking multiple dimensions (e.g., image "quality", video frame-rate or aspect ratio, audio sampling rate). In this paper we present the syntax and semantics of our functional language, our type system that builds costs and resource/data constraints, and (through both formalism and specific details of our implementation) provide concrete examples of how the constraints and sizing information are used in practice.

The Sensor Network Workbench: Towards Functional Specification, Verification and Deployment of Constrained Distributed Systems

As the commoditization of sensing, actuation and communication hardware increases, so does the potential for dynamically tasked sense and respond networked systems (i.e., Sensor Networks or SNs) to replace existing disjoint and inflexible special-purpose deployments (closed-circuit security video, anti-theft sensors, etc.). While various solutions have emerged to many individual SN-centric challenges (e.g., power management, communication protocols, role assignment), perhaps the largest remaining obstacle to widespread SN deployment is that those who wish to deploy, utilize, and maintain a programmable Sensor Network lack the programming and systems expertise to do so. The contributions of this thesis centers on the design, development and deployment of the SN Workbench (snBench). snBench embodies an accessible, modular programming platform coupled with a flexible and extensible run-time system that, together, support the entire life-cycle of distributed sensory services. As it is impossible to find a one-size-fits-all programming interface, this work advocates the use of tiered layers of abstraction that enable a variety of high-level, domain specific languages to be compiled to a common (thin-waist) tasking language; this common tasking language is statically verified and can be subsequently re-translated, if needed, for execution on a wide variety of hardware platforms. snBench provides: (1) a common sensory tasking language (Instruction Set Architecture) powerful enough to express complex SN services, yet simple enough to be executed by highly constrained resources with soft, real-time constraints, (2) a prototype high-level language (and corresponding compiler) to illustrate the utility of the common tasking language and the tiered programming approach in this domain, (3) an execution environment and a run-time support infrastructure that abstract a collection of heterogeneous resources into a single virtual Sensor Network, tasked via this common tasking language, and (4) novel formal methods (i.e., static analysis techniques) that verify safety properties and infer implicit resource constraints to facilitate resource allocation for new services. This thesis presents these components in detail, as well as two specific case-studies: the use of snBench to integrate physical and wireless network security, and the use of snBench as the foundation for semester-long student projects in a graduate-level Software Engineering course.

Automatic relative debugging of OpenMP programs

In this work we show how automatic relative debugging can be used to find differences in computation between a correct serial program and an OpenMP parallel version of that program that does not yield correct results. Backtracking and re-execution are used to determine the first OpenMP parallel region that produces a difference in computation that may lead to an incorrect value the user has indicated. Our approach also lends itself to finding differences between parallel computations, where executing with M threads produces expected results but an N thread execution does not (M, N > 1, M ≠ N). OpenMP programs created using a parallelization tool are addressed by utilizing static analysis and directive information from the tool. Hand-parallelized programs, where OpenMP directives are inserted by the user, are addressed by performing data dependence and directive analysis.

A profile-based tool for finding pipeline parallelism in sequential programs

Traditional static analysis fails to auto-parallelize programs with a complex control and data flow. Furthermore, thread-level parallelism in such programs is often restricted to pipeline parallelism, which can be hard to discover by a programmer. In this paper we propose a tool that, based on profiling information, helps the programmer to discover parallelism. The programmer hand-picks the code transformations from among the proposed candidates which are then applied by automatic code transformation techniques.

This paper contributes to the literature by presenting a profiling tool for discovering thread-level parallelism. We track dependencies at the whole-data structure level rather than at the element level or byte level in order to limit the profiling overhead. We perform a thorough analysis of the needs and costs of this technique. Furthermore, we present and validate the belief that programs with complex control and data flow contain significant amounts of exploitable coarse-grain pipeline parallelism in the program’s outer loops. This observation validates our approach to whole-data structure dependencies. As state-of-the-art compilers focus on loops iterating over data structure members, this observation also explains why our approach finds coarse-grain pipeline parallelism in cases that have remained out of reach for state-of-the-art compilers. In cases where traditional compilation techniques do find parallelism, our approach allows to discover higher degrees of parallelism, allowing a 40% speedup over traditional compilation techniques. Moreover, we demonstrate real speedups on multiple hardware platforms.

Inference and Declaration of Independence in Task-Parallel Programs

The inherent difficulty of thread-based shared-memory programming has recently motivated research in high-level, task-parallel programming models. Recent advances of Task-Parallel models add implicit synchronization, where the system automatically detects and satisfies data dependencies among spawned tasks. However, dynamic dependence analysis incurs significant runtime overheads, because the runtime must track task resources and use this information to schedule tasks while avoiding conflicts and races.
We present SCOOP, a compiler that effectively integrates static and dynamic analysis in code generation. SCOOP combines context-sensitive points-to, control-flow, escape, and effect analyses to remove redundant dependence checks at runtime. Our static analysis can work in combination with existing dynamic analyses and task-parallel runtimes that use annotations to specify tasks and their memory footprints. We use our static dependence analysis to detect non-conflicting tasks and an existing dynamic analysis to handle the remaining dependencies. We evaluate the resulting hybrid dependence analysis on a set of task-parallel programs.

Android Malware Detection Using Parallel Machine Learning Classifiers

Mobile malware has continued to grow at an alarming rate despite on-going mitigation efforts. This has been much more prevalent on Android due to being an open platform that is rapidly overtaking other competing platforms in the mobile smart devices market. Recently, a new generation of Android malware families has emerged with advanced evasion capabilities which make them much more difficult to detect using conventional methods. This paper proposes and investigates a parallel machine learning based classification approach for early detection of Android malware. Using real malware samples and benign applications, a composite classification model is developed from parallel combination of heterogeneous classifiers. The empirical evaluation of the model under different combination schemes demonstrates its efficacy and potential to improve detection accuracy. More importantly, by utilizing several classifiers with diverse characteristics, their strengths can be harnessed not only for enhanced Android malware detection but also quicker white box analysis by means of the more interpretable constituent classifiers.

High accuracy android malware detection using ensemble learning

With over 50 billion downloads and more than 1.3 million apps in Google’s official market, Android has continued to gain popularity amongst smartphone users worldwide. At the same time there has been a rise in malware targeting the platform, with more recent strains employing highly sophisticated detection avoidance techniques. As traditional signature based methods become less potent in detecting unknown malware, alternatives are needed for timely zero-day discovery. Thus this paper proposes an approach that utilizes ensemble learning for Android malware detection. It combines advantages of static analysis with the efficiency and performance of ensemble machine learning to improve Android malware detection accuracy. The machine learning models are built using a large repository of malware samples and benign apps from a leading antivirus vendor. Experimental results and analysis presented shows that the proposed method which uses a large feature space to leverage the power of ensemble learning is capable of 97.3 % to 99% detection accuracy with very low false positive rates.

Viabilidade do reforço sísmico de um edifício de pequeno porte em alvenaria de pedra ordinária

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Civil na Área de Especialização de Estruturas