### 经济代写|博弈论代写Game Theory代考|ECOS3012

statistics-lab™ 为您的留学生涯保驾护航 在代写博弈论Game Theory方面已经树立了自己的口碑, 保证靠谱, 高质且原创的统计Statistics代写服务。我们的专家在代写博弈论Game Theory代写方面经验极为丰富，各种代写博弈论Game Theory相关的作业也就用不着说。

• Statistical Inference 统计推断
• Statistical Computing 统计计算
• Advanced Probability Theory 高等概率论
• Advanced Mathematical Statistics 高等数理统计学
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 经济代写|博弈论代写Game Theory代考|Artificial Intelligence and Cybersecurity

Artificial intelligence (AI) refers to a set of computing technologies that are designed to accomplish tasks that require intelligence, which can be broadly defined as interpreting observations of the environment, using knowledge and problem solving to select actions to efficiently accomplish goals, and adapting to new information and situations as they arise. AI encompasses a large

number of subdisciplines including knowledge representation, reasoning and decision-making, different types of learning, language processing and vision, robotics, and many others. While general-purpose “human level” AI has not yet been achieved and is not likely to emerge in the near future, AI technologies are rapidly improving in many areas and are being used in many new applications with increasing complexity and impact on our everyday lives. For example, AI agents have already met or exceeded the performance of humans in complex games of strategy such as Go (Singh et al. 2017) and Poker (Brown and Sandholm 2019), and they are also achieving success in many more general tasks such as medical diagnostics (Ker et al. 2017) and self-driving vehicles (Badue et al. 2019), to name just a few examples. For specific, well-defined domains and tasks, it is typical for AI systems to already be on par with human performance, and in many cases, A] systems excel at tasks in which humans perform very poorly. Given the rapid advances in AI, there is a great opportunity to deploy AI methods to make progress on the most challenging problems we face as a society.

Cybersecurity represents one of the great emerging challenges of the twenty-first century. This is driven fundamentally by the rapid development and adoption of computing and networking technologies throughout all areas of society. While providing dramatic benefits and fundamentally changing many areas of business, government, media, military, and personal life, the general paradigm has been to push out new technology and features as quickly as possible without great diligence given to security and privacy implications. The complexity of the interactions of the systems combined with the human users and administrators that interact with them has led to the current situation in which cyber attacks are a constant and very costly threat (Lewis 2018).

As we rely ever more on computing systems to perform critical functions (e.g., controlling the electric grid, driving a vehicle, or making medical diagnoses), the importance of addressing the cybersecurity problem becomes paramount. In addition, future conflicts among nation states as well as terrorist actors will involve attacks and defense in cyberspace as major elements. Those groups with the most robust and resilient computing infrastructure will have a significant security advantage in other areas as well.

AI is already used in many areas of cybersecurity, and new applications of AI will be a key part of the solution for improving cybersecurity in the future. Users, software developers, network administrators, and cyber response teams all have a limited ability to respond to cyber threats, and they need better automated tools for configuration, threat detection and evaluation, risk assessment, automated response, etc., to improve accuracy and reduce the costs of providing security. AI is especially important in cyberspace, because many of the events and responses in security situations take place either at speeds that are much too fast for an effective human response or at scales that are much too large for humans to process effectively. However, the many unique aspects of the cybersecurity domain present novel challenges for developing and deploying AI solutions. This book provides a broad view of recent progress in AI related to cybersecurity, including deep dives into many specific techniques and applications, as well as discussions of ongoing challenges and future work.

## 经济代写|博弈论代写Game Theory代考|Game Theory for Cybersecurity

Many problems in cybersecurity are fundamentally decision-making problems under complex, uncertain, multi-agent conditions. Game theory provides a foundational set of mathematical tools for modeling these types of decision problems that can allow us to make better decisions (both as

humans and automated systems), as well as understand the reasons for these decisions and what assumptions and data they depend on. While many problems in cybersecurity can be modeled as (approximately) zero-sum games between a defender and an attacker, there are also more complex games that are not zero-sum or that involve relationships among multiple attackers and defenders (Alpcan and Başar 2010; Manshaei et al. 2013; Pawlick and Zhu 2021). Game theory is not a single idea or approach, but a very diverse collection of modeling techniques and solution concepts and algorithms that can be applied to many different situations. Therefore, it is not a simple “off the shelf” technology that can be easily adapted to any problem. Rather, it is a powerful set of techniques that requires a clear understanding of the problem being modeled, as well as the strengths and limitations of different solution techniques to arrive at solutions that can be highly effective in practice.

In this book, we start with a general overview of basic game theory concepts and then cover a variety of specific modeling and solution techniques, as well as applications of these techniques in cybersecurity applications. These examples are intended to provide a good representation of common approaches in this very active research area, where new problems are being solved and new approaches are being developed at a rapid pace.

## 经济代写|博弈论代写Game Theory代考|Machine Learning for Cybersecurity

Another fundamental problem in cybersecurity is using data to make predictions, identify patterns, perform classification, or adapt strategies over time. All of these tasks fall under the general domain of machine learning, which encompasses different paradigms including supervised, unsupervised, and reinforcement learning. Machine learning is a core discipline within AI that studies how agents can use historical data to adapt and improve their performance on future tasks. This area of AI has seen especially dramatic progress and success in certain tasks in recent years, especially with the advancement of “deep learning” approaches that focus on using the data to find sophisticated internal representations of features, rather than having a human specify features to the algorithm. Deep learning techniques have been very successful in many areas including specific cybersecurity tasks such as intrusion detection and malware analysis (Xin et al. 2018; Berman et al. 2019). However, deep learning does typically require large data sets and lots of computing resources to achieve good results, so it is not the best solutions for all problems.

In this book, we cover a range of topics in machine learning for cybersecurity, including examples of specific applications, theoretical and empirical evaluations of new techniques, the connections between machine learning and game theory in multi-agent settings, and analysis of some of the problems that can arise when applying machine learning in an adversarial context. Here, we briefly overview some of the main challenges for applying machine learning to cybersecurity problems that are addressed throughout this book:

1. Problem definition and data sets. Similar to the game theory approaches, machine learning approaches must also define exactly what the problem is and what data can be used to learn the desired model. First, one faces the problems of specifying exactly what you are trying to predict, how this will be useful to the decision-maker, and how you will characterize uncertainty about the predictions. Next, you need to be able to identify data that will actually be useful for making these predictions; usually this needs to be data in sufficient quantity and without too much noise or data quality issues to be useful. In addition, it may be necessary to label the data based on human inputs, which can be expensive. In cybersecurity, there is the additional challenge that high-quality data sets are hard to acquire due to privacy and security concerns, and it is also hard to get direct data about attacker activities. All of this means that the first problem that must be solved before machine learning can be useful is to identify good data sources.

## 经济代写|博弈论代写Game Theory代考|Machine Learning for Cybersecurity

1. 问题定义和数据集。与博弈论方法类似，机器学习方法还必须准确定义问题是什么以及可以使用哪些数据来学习所需的模型。首先，人们面临的问题是准确地说明您要预测的内容，这对决策者有何用处，以及您将如何描述预测的不确定性。接下来，您需要能够识别对做出这些预测实际上有用的数据；通常这需要有足够数量的数据，并且没有太多的噪音或数据质量问题才能有用。此外，可能需要根据人工输入标记数据，这可能很昂贵。在网络安全方面，由于隐私和安全问题，难以获得高质量的数据集是一个额外的挑战，而且也很难获得有关攻击者活动的直接数据。所有这些意味着在机器学习有用之前必须解决的第一个问题是识别好的数据源。

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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。