Game Theory and Machine Learning for Cyber Security最新文献_第2页

Semi‐automated Parameterization of a Probabilistic Model Using Logistic Regression—A Tutorial 使用逻辑回归的概率模型的半自动参数化-教程

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch22

S. Rass, Sandra König, S. Schauer

{"title":"Semi‐automated Parameterization of a Probabilistic Model Using Logistic Regression—A Tutorial","authors":"S. Rass, Sandra König, S. Schauer","doi":"10.1002/9781119723950.ch22","DOIUrl":"https://doi.org/10.1002/9781119723950.ch22","url":null,"abstract":"Many practical situations require some modeling of uncertainty, and often, this means speaking about events whose likelihood to occur is conveniently expressible by probability parameters, say, a scalar 0 ≤ p ≤ 1 \u0000. The semantics of such values can be arbitrarily complex, ranging from simple probabilities, up to conditional likelihoods, or factors of mere subjective interpretation, such as hyper‐parameters in Bayesian models. This chapter addresses the often untold story of how to find a value for a generic probability parameter p \u0000, or a whole set of such parameters. The simplicity of embodying opaque background dynamics in the mantle of uncertainty, brought into a model by a parameter p \u0000, is often bought at the challenge for the user of a model to find a good value for it. This tutorial is a step‐by‐step guidance through the idea of finding values for probability parameters “by examples.” Provided that a parameter p refers to the likelihood of an event to occur, or conditionally occur under certain settings of other parameters, we describe how to use logistic regression, as an instance of machine learning, to parameterize models using sets of examples. The method is explained in the R programming language and demonstrated along a running showcase application.","PeriodicalId":332247,"journal":{"name":"Game Theory and Machine Learning for Cyber Security","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115554696","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}

引用次数: 2

Scalable Algorithms for Identifying Stealthy Attackers in a Game‐Theoretic Framework Using Deception 利用欺骗在博弈论框架中识别隐形攻击者的可扩展算法

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch3

Anjon Basak, C. Kamhoua, S. Venkatesan, Marcus Gutierrez, Ahmed H. Anwar, Christopher Kiekintveld

引用次数: 1

Manipulating Reinforcement Learning: Stealthy Attacks on Cost Signals 操纵强化学习:对成本信号的隐形攻击

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch19

Yunhan Huang, Quanyan Zhu

引用次数: 4

Adversarial Gaussian Process Regression in Sensor Networks 传感器网络中的对抗高斯过程回归

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch9

Yi Li, X. Koutsoukos, Yevgeniy Vorobeychik

引用次数: 0

Minimax Detection (MAD) for Computer Security: A Dynamic Program Characterization 计算机安全的极大极小检测(MAD):一种动态程序表征

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch7

M. O. Sayin, D. Sahabandu, Muhammad Aneeq uz Zaman, R. Poovendran, T. Başar

引用次数: 0

Evading Machine Learning Based Network Intrusion Detection Systems with GANs 基于GANs的逃避机器学习网络入侵检测系统

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch17

B. Zolbayar, Ryan Sheatsley, P. Mcdaniel, Mike Weisman

{"title":"Evading Machine Learning Based Network Intrusion Detection Systems with GANs","authors":"B. Zolbayar, Ryan Sheatsley, P. Mcdaniel, Mike Weisman","doi":"10.1002/9781119723950.ch17","DOIUrl":"https://doi.org/10.1002/9781119723950.ch17","url":null,"abstract":"Machine learning (ML) is fundamentally changing our way of life with the recent availability of high computational power and big data. Emerging ML‐based techniques of network intrusion detection systems (NIDS) can detect complex cyberattacks, undetectable by conventional techniques. In this chapter, we evaluate the threat of a generative adversarial networks (GAN) aided‐attack on these systems. In our threat model, an adversarial attacker, given access to a training data of the NIDS, adds a minimal perturbation to the feature values of attack traffic to change the DNN's prediction from “malicious” to “benign.” We evaluate our attack algorithm against two state‐of‐the‐art DNN models as well as our own well‐trained DNN model achieving nearly 100% success rates in whitebox setting. We also show that adversarial traffic crafted on these three DNN models also transfer and fool the NIDS models trained with classic ML algorithms with a high accuracy: logistic regression, support vector machine, decision tree and k \u0000‐nearest neighbors. Our work shows that ML‐based NIDS are vulnerable to adversarial network traffic crafted by our GAN‐based attack algorithm.","PeriodicalId":332247,"journal":{"name":"Game Theory and Machine Learning for Cyber Security","volume":"1148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114163844","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}

引用次数: 2

Resilient Distributed Adaptive Cyber‐Defense Using Blockchain 使用区块链的弹性分布式自适应网络防御

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch23

G. Cybenko, Roger A. Hallman

引用次数: 3

Concealment Charm ( ConcealGAN ): Automatic Generation of Steganographic Text Using Generative Models to Bypass Censorship 隐藏咒(ConcealGAN):使用生成模型自动生成隐写文本以绕过审查

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch18

Nurpeiis Baimukan, Quanyan Zhu

引用次数: 1

Trinity: Trust, Resilience and Interpretability of Machine Learning Models 三位一体:机器学习模型的信任、弹性和可解释性

Game Theory and Machine Learning for Cyber Security Pub Date : 2021-09-12 DOI: 10.1002/9781119723950.ch16

Susmit Jha, Brian Jalaian, Anirban Roy, Gunjan Verma

引用次数: 3

Sensor Manipulation Games in Cyber Security 网络安全中的传感器操作游戏

Game Theory and Machine Learning for Cyber Security Pub Date : 1900-01-01 DOI: 10.1002/9781119723950.ch8

J. Hespanha

引用次数: 2