经济代写|博弈论代写Game Theory代考|ECON2112

2023年3月24日

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• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
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• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础
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经济代写|博弈论代写Game Theory代考|Cyber-physical System: NPCF Building Automation System

The Blue Waters supercomputer is housed in a dedicated building, the National Petascale Computing Facility (NPCF). The 88000 -square-foot building uses the state-of-the-art building automation system (BAS) that is in charge of regulating the environmental parameters (i.e. pressure, flow, and source of the cooling system) of the building, including the server room. A detailed configuration of the system can be found in Chung et al. (2016). The building automation system (which consists of a control server, a set of programmable logic controllers – PLCs, sensors, and actuators) utilizes a set of measurements collected from the chilled water loop to regulate the chilled water delivered to the cooling cabinet, under three modes of operation: campus mode, mix mode, and economic (econ) mode. Campus mode is the used mode in most data centers that use chilled water bought from external providers. While the chilled water from such providers is well controlled (i.e. the temperature, flows, and pressure are kept within an agreed range), its usage results in an increase in the cost of operation. To reduce the cost of operation, NPCF has a set of dedicated cooling towers (which use cold temperatures to naturally chill the water) by means of which it can deploy an additional mode of operation: economic mode. By taking advantage of cold external temperatures throughout $2 / 3$ of the year to prepare the chilled water, NPCF was able to significantly reduce its cost of operation, which compensated for the construction costs of the water towers after one year of operation. The mix mode is an intermediate mode that was introduced to enable a smooth transition between economic and campus modes.

The CPS operational data set is an archive of all measurements and control command values within the chilled water system of the building facility that was collected from September 2016 to May 2017. The data set contains 47 distinct parameters collected every five minutes. Sample parameters monitored and collected within the chilled water system include differential pressure, flow, and temperature of the campus input (“CAMP.CHW.DP, FLOW, TEMP”), control valve setting and measurements at the high loop (“CHW.HI.CV, TEMP, FLOW”). This data set is essential for analyzing the operation of the CPS and inferring critical information related to failures of the computing infrastructure.

The incident reports log incidents related to the computing infrastructure since the deployment of the system (i.e. December 2012). The incidents recorded in the reports include hardware part failures, cooling-system-related problems, and system-wide outages (SWOs) during which all 28,164 compute nodes were shut down. The incident reports are used for validating our approach by cross-validating the attack strategies derived by our smart malware (i.e. subset of the data set that the malware predicted as “related to an SWO of BWs”) with the ground truth in a given report (i.e. the status of BWs at the during the timeline of the parsed data set and its cause). In Table 15.1, we present a sample of the incident report.

经济代写|博弈论代写Game Theory代考|Protection from Rising Threats

Examples discussed in this paper demonstrate that AI-driven malware is no longer a remote possibility. As attacker capabilities and attack’s sophistication grow, cyber defenders must understand mechanisms and implications of the malicious use of AI to: (i) stay ahead of these threats and (ii) devise and deploy defenses to prevent: data being stolen, systems damaged, or major disruption of critical infrastructure (e.g. electric power grid). AI-driven malware is a relatively new concept, and not everyone considers it practical. The authors of G DATA Software (2019) acknowledge the contribution of Kirat et al. (2018) but indicates that they do not consider the model a real threat. According to the article, such an attack model can be dealt with using existing behavior-based detection methods and signatures that detect usage of certain libraries (i.e. ML libraries) or access to specific files. However, the behavior-based (blacklist) model only applies for known malicious payloads (e.g. the ransomware in Kirat et al. (2018)), so ML-driven threats with non-obvious (hard to differentiate from benign) payloads might not be detectable. In addition, adversarial methods have been reported that bypass signature-based detection methods. For instance, self-learning malware can utilize custom software packages and deploy obfuscation methods to encumber code analysis. In addition to existing cyber security defense methodologies, we find a need to consider additional methods that tackle the new threat (i.e. self-learning malware) and a need for computing infrastructure administrators to develop more comprehensive security awareness.

Prevention. Risk of AI-driven threats can be reduced through fine control and management of data access. Without proper data, only limited intelligence can be inferred, regardless of the effectiveness of the learning model. However, proper management of data access is not a trivial task. New vulnerabilities for open-source or off-the-shelf software packages are reported every day, and defining a breach-prone policy requires modeling of varying use cases and system specifics. In response to vulnerabilities, security patches and version updates become available to users. Nonetheless, there are situations in which such updates are not easily applicable. For instance, although the vulnerabilities in ROS have been known for years, they cannot be removed from the framework without a face-lifting upgrade of ROS, which would require a complete reprogramming of the robotic applications (Chung et al. 2019b). Instead, the authors in (Chung et al. 2019b) (as a mitigation method against AI-driven malware) identify a unique signature that indicates malicious attempts that exploit the vulnerabilities and propose proactive responses to the threat through blocking of the source of the attempt and returning of the robot to a predefined safe state. Deployment of similar monitors/systems can prevent ML-driven advanced threats from making unauthorized data access.

Encryption is a common method deployed in production systems to prevent unauthorized access to the system and its data. However, there are some limitations that keep encryption from becoming the golden key solution to security. In particular, the latency introduced by the encryption/decryption scheme can be crucial for real-time critical systems (such as the Raven-II surgical robot). For instance, referring to a recent measurement with the ROS2 framework (Kim et al. 2018), the deployment of the cryptographic algorithm added a non-constant delay that becomes significant as the size of the data packet increases. Furthermore, due to the number of sequential message passing for a single robot iteration, the latency builds up. In fact, not all systems are time-sensitive and allow the deployment of cryptographic algorithms. However, there are instances within the system where the data are processed in plain text. Hence, with the additional constraints, self-learning malware (instead of intercepting packets in the network layer) only needs to target that particular instance while keeping the current procedure.

博弈论代考

经济代写|博弈论代写Game Theory代考|Cyber-physical System: NPCF Building Automation System

Blue Waters 超级计算机位于一座专用建筑内，即国家千万亿次计算设施 (NPCF)。这座 88000 平方英尺的建筑使用最先进的楼宇自动化系统 (BAS)，负责调节建筑的环境参数（即压力、流量和冷却系统的来源），包括服务器机房。系统的详细配置可以在 Chung 等人中找到。(2016)。楼宇自动化系统（由控制服务器、一组可编程逻辑控制器——PLC、传感器和执行器组成）利用从冷冻水回路收集的一组测量值来调节输送到冷却柜的冷冻水，低于三个运作模式：校园模式、混合模式和经济（econ）模式。园区模式是大多数数据中心使用的模式，这些数据中心使用从外部供应商处购买的冷冻水。虽然来自这些供应商的冷冻水得到很好的控制（即温度、流量和压力保持在约定的范围内），但其使用会导致运营成本增加。为了降低运营成本，NPCF 有一套专用冷却塔（利用低温自然冷却水），通过它可以部署额外的运营模式：经济模式。通过充分利用寒冷的外部温度 NPCF 有一套专用冷却塔（使用低温自然冷却水），通过它可以部署额外的运行模式：经济模式。通过充分利用寒冷的外部温度 NPCF 有一套专用冷却塔（使用低温自然冷却水），通过它可以部署额外的运行模式：经济模式。通过充分利用寒冷的外部温度2/3年准备冷冻水，NPCF 能够显着降低其运营成本，这在运营一年后补偿了水塔的建设成本。混合模式是一种中间模式，旨在实现经济模式和校园模式之间的平稳过渡。

CPS 运行数据集是 2016 年 9 月至 2017 年 5 月收集的建筑设施冷冻水系统内所有测量和控制命令值的存档。该数据集包含每五分钟收集一次的 47 个不同参数。在冷冻水系统中监测和收集的样本参数包括园区输入的压差、流量和温度（“CAMP.CHW.DP、FLOW、TEMP”）、控制阀设置和高回路测量（“CHW.HI .CV、温度、流量”）。该数据集对于分析 CPS 的运行和推断与计算基础设施故障相关的关键信息至关重要。

有限元方法代写

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MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。