Machine Learning Security With Azure

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Microsoft Azure Essentials Azure Machine Learning

Jeff Barnes

Author : Jeff Barnes
Publisher : Microsoft Press
Page : 336 pages
File Size : 45,9 Mb
Release : 2015-04-25
Category : Computers
ISBN : 9780735698185

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Microsoft Azure Essentials Azure Machine Learning by Jeff Barnes Pdf

Microsoft Azure Essentials from Microsoft Press is a series of free ebooks designed to help you advance your technical skills with Microsoft Azure. This third ebook in the series introduces Microsoft Azure Machine Learning, a service that a developer can use to build predictive analytics models (using training datasets from a variety of data sources) and then easily deploy those models for consumption as cloud web services. The ebook presents an overview of modern data science theory and principles, the associated workflow, and then covers some of the more common machine learning algorithms in use today. It builds a variety of predictive analytics models using real world data, evaluates several different machine learning algorithms and modeling strategies, and then deploys the finished models as machine learning web services on Azure within a matter of minutes. The ebook also expands on a working Azure Machine Learning predictive model example to explore the types of client and server applications you can create to consume Azure Machine Learning web services. Watch Microsoft Press’s blog and Twitter (@MicrosoftPress) to learn about other free ebooks in the Microsoft Azure Essentials series.

Machine Learning Security with Azure

Georgia Kalyva

Author : Georgia Kalyva
Publisher : Packt Publishing Ltd
Page : 310 pages
File Size : 47,5 Mb
Release : 2023-12-28
Category : Computers
ISBN : 9781805123958

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Machine Learning Security with Azure by Georgia Kalyva Pdf

Implement industry best practices to identify vulnerabilities and protect your data, models, environment, and applications while learning how to recover from a security breach Key Features Learn about machine learning attacks and assess your workloads for vulnerabilities Gain insights into securing data, infrastructure, and workloads effectively Discover how to set and maintain a better security posture with the Azure Machine Learning platform Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionWith AI and machine learning (ML) models gaining popularity and integrating into more and more applications, it is more important than ever to ensure that models perform accurately and are not vulnerable to cyberattacks. However, attacks can target your data or environment as well. This book will help you identify security risks and apply the best practices to protect your assets on multiple levels, from data and models to applications and infrastructure. This book begins by introducing what some common ML attacks are, how to identify your risks, and the industry standards and responsible AI principles you need to follow to gain an understanding of what you need to protect. Next, you will learn about the best practices to secure your assets. Starting with data protection and governance and then moving on to protect your infrastructure, you will gain insights into managing and securing your Azure ML workspace. This book introduces DevOps practices to automate your tasks securely and explains how to recover from ML attacks. Finally, you will learn how to set a security benchmark for your scenario and best practices to maintain and monitor your security posture. By the end of this book, you’ll be able to implement best practices to assess and secure your ML assets throughout the Azure Machine Learning life cycle.What you will learn Explore the Azure Machine Learning project life cycle and services Assess the vulnerability of your ML assets using the Zero Trust model Explore essential controls to ensure data governance and compliance in Azure Understand different methods to secure your data, models, and infrastructure against attacks Find out how to detect and remediate past or ongoing attacks Explore methods to recover from a security breach Monitor and maintain your security posture with the right tools and best practices Who this book is for This book is for anyone looking to learn how to assess, secure, and monitor every aspect of AI or machine learning projects running on the Microsoft Azure platform using the latest security and compliance, industry best practices, and standards. This is a must-have resource for machine learning developers and data scientists working on ML projects. IT administrators, DevOps, and security engineers required to secure and monitor Azure workloads will also benefit from this book, as the chapters cover everything from implementation to deployment, AI attack prevention, and recovery.

Hands-On Machine Learning with Azure

Thomas K Abraham,Parashar Shah,Jen Stirrup,Lauri Lehman,Anindita Basak

Author : Thomas K Abraham,Parashar Shah,Jen Stirrup,Lauri Lehman,Anindita Basak
Publisher : Packt Publishing Ltd
Page : 340 pages
File Size : 44,6 Mb
Release : 2018-10-31
Category : Computers
ISBN : 9781789130270

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Hands-On Machine Learning with Azure by Thomas K Abraham,Parashar Shah,Jen Stirrup,Lauri Lehman,Anindita Basak Pdf

Implement machine learning, cognitive services, and artificial intelligence solutions by leveraging Azure cloud technologies Key FeaturesLearn advanced concepts in Azure ML and the Cortana Intelligence Suite architectureExplore ML Server using SQL Server and HDInsight capabilitiesImplement various tools in Azure to build and deploy machine learning modelsBook Description Implementing Machine learning (ML) and Artificial Intelligence (AI) in the cloud had not been possible earlier due to the lack of processing power and storage. However, Azure has created ML and AI services that are easy to implement in the cloud. Hands-On Machine Learning with Azure teaches you how to perform advanced ML projects in the cloud in a cost-effective way. The book begins by covering the benefits of ML and AI in the cloud. You will then explore Microsoft’s Team Data Science Process to establish a repeatable process for successful AI development and implementation. You will also gain an understanding of AI technologies available in Azure and the Cognitive Services APIs to integrate them into bot applications. This book lets you explore prebuilt templates with Azure Machine Learning Studio and build a model using canned algorithms that can be deployed as web services. The book then takes you through a preconfigured series of virtual machines in Azure targeted at AI development scenarios. You will get to grips with the ML Server and its capabilities in SQL and HDInsight. In the concluding chapters, you’ll integrate patterns with other non-AI services in Azure. By the end of this book, you will be fully equipped to implement smart cognitive actions in your models. What you will learnDiscover the benefits of leveraging the cloud for ML and AIUse Cognitive Services APIs to build intelligent botsBuild a model using canned algorithms from Microsoft and deploy it as a web serviceDeploy virtual machines in AI development scenariosApply R, Python, SQL Server, and Spark in AzureBuild and deploy deep learning solutions with CNTK, MMLSpark, and TensorFlowImplement model retraining in IoT, Streaming, and Blockchain solutionsExplore best practices for integrating ML and AI functions with ADLA and logic appsWho this book is for If you are a data scientist or developer familiar with Azure ML and cognitive services and want to create smart models and make sense of data in the cloud, this book is for you. You’ll also find this book useful if you want to bring powerful machine learning services into your cloud applications. Some experience with data manipulation and processing, using languages like SQL, Python, and R, will aid in understanding the concepts covered in this book

Machine Learning Techniques and Analytics for Cloud Security

Rajdeep Chakraborty,Anupam Ghosh,Jyotsna Kumar Mandal

Author : Rajdeep Chakraborty,Anupam Ghosh,Jyotsna Kumar Mandal
Publisher : John Wiley & Sons
Page : 484 pages
File Size : 52,7 Mb
Release : 2021-12-21
Category : Computers
ISBN : 9781119762256

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Machine Learning Techniques and Analytics for Cloud Security by Rajdeep Chakraborty,Anupam Ghosh,Jyotsna Kumar Mandal Pdf

MACHINE LEARNING TECHNIQUES AND ANALYTICS FOR CLOUD SECURITY This book covers new methods, surveys, case studies, and policy with almost all machine learning techniques and analytics for cloud security solutions The aim of Machine Learning Techniques and Analytics for Cloud Security is to integrate machine learning approaches to meet various analytical issues in cloud security. Cloud security with ML has long-standing challenges that require methodological and theoretical handling. The conventional cryptography approach is less applied in resource-constrained devices. To solve these issues, the machine learning approach may be effectively used in providing security to the vast growing cloud environment. Machine learning algorithms can also be used to meet various cloud security issues, such as effective intrusion detection systems, zero-knowledge authentication systems, measures for passive attacks, protocols design, privacy system designs, applications, and many more. The book also contains case studies/projects outlining how to implement various security features using machine learning algorithms and analytics on existing cloud-based products in public, private and hybrid cloud respectively. Audience Research scholars and industry engineers in computer sciences, electrical and electronics engineering, machine learning, computer security, information technology, and cryptography.

Automated Machine Learning with Microsoft Azure

Dennis Michael Sawyers

Author : Dennis Michael Sawyers
Publisher : Packt Publishing Ltd
Page : 340 pages
File Size : 51,6 Mb
Release : 2021-04-23
Category : Computers
ISBN : 9781800561977

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Automated Machine Learning with Microsoft Azure by Dennis Michael Sawyers Pdf

A practical, step-by-step guide to using Microsoft's AutoML technology on the Azure Machine Learning service for developers and data scientists working with the Python programming language Key FeaturesCreate, deploy, productionalize, and scale automated machine learning solutions on Microsoft AzureImprove the accuracy of your ML models through automatic data featurization and model trainingIncrease productivity in your organization by using artificial intelligence to solve common problemsBook Description Automated Machine Learning with Microsoft Azure will teach you how to build high-performing, accurate machine learning models in record time. It will equip you with the knowledge and skills to easily harness the power of artificial intelligence and increase the productivity and profitability of your business. Guided user interfaces (GUIs) enable both novices and seasoned data scientists to easily train and deploy machine learning solutions to production. Using a careful, step-by-step approach, this book will teach you how to use Azure AutoML with a GUI as well as the AzureML Python software development kit (SDK). First, you'll learn how to prepare data, train models, and register them to your Azure Machine Learning workspace. You'll then discover how to take those models and use them to create both automated batch solutions using machine learning pipelines and real-time scoring solutions using Azure Kubernetes Service (AKS). Finally, you will be able to use AutoML on your own data to not only train regression, classification, and forecasting models but also use them to solve a wide variety of business problems. By the end of this Azure book, you'll be able to show your business partners exactly how your ML models are making predictions through automatically generated charts and graphs, earning their trust and respect. What you will learnUnderstand how to train classification, regression, and forecasting ML algorithms with Azure AutoMLPrepare data for Azure AutoML to ensure smooth model training and deploymentAdjust AutoML configuration settings to make your models as accurate as possibleDetermine when to use a batch-scoring solution versus a real-time scoring solutionProductionalize your AutoML and discover how to quickly deliver valueCreate real-time scoring solutions with AutoML and Azure Kubernetes ServiceTrain a large number of AutoML models at once using the AzureML Python SDKWho this book is for Data scientists, aspiring data scientists, machine learning engineers, or anyone interested in applying artificial intelligence or machine learning in their business will find this machine learning book useful. You need to have beginner-level knowledge of artificial intelligence and a technical background in computer science, statistics, or information technology before getting started. Familiarity with Python will help you implement the more advanced features found in the chapters, but even data analysts and SQL experts will be able to train ML models after finishing this book.

Machine Learning, Blockchain, and Cyber Security in Smart Environments

Sarvesh Tanwar,Sumit Badotra,Ajay Rana

Author : Sarvesh Tanwar,Sumit Badotra,Ajay Rana
Publisher : CRC Press
Page : 313 pages
File Size : 47,9 Mb
Release : 2022-08-31
Category : Computers
ISBN : 9781000623918

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Machine Learning, Blockchain, and Cyber Security in Smart Environments by Sarvesh Tanwar,Sumit Badotra,Ajay Rana Pdf

Machine Learning, Cyber Security, and Blockchain in Smart Environment: Application and Challenges provides far-reaching insights into the recent techniques forming the backbone of smart environments, and addresses the vulnerabilities that give rise to the challenges in real-word implementation. The book focuses on the benefits related to the emerging applications such as machine learning, blockchain and cyber security. Key Features: Introduces the latest trends in the fields of machine learning, blockchain and cyber security Discusses the fundamentals, challenges and architectural overviews with concepts Explores recent advancements in machine learning, blockchain, and cyber security Examines recent trends in emerging technologies This book is primarily aimed at graduates, researchers, and professionals working in the areas of machine learning, blockchain, and cyber security.

Machine Learning with Microsoft Technologies

Leila Etaati

Author : Leila Etaati
Publisher : Apress
Page : 363 pages
File Size : 44,9 Mb
Release : 2019-06-12
Category : Computers
ISBN : 9781484236581

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Machine Learning with Microsoft Technologies by Leila Etaati Pdf

Know how to do machine learning with Microsoft technologies. This book teaches you to do predictive, descriptive, and prescriptive analyses with Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, HD Insight, and more. The ability to analyze massive amounts of real-time data and predict future behavior of an organization is critical to its long-term success. Data science, and more specifically machine learning (ML), is today’s game changer and should be a key building block in every company’s strategy. Managing a machine learning process from business understanding, data acquisition and cleaning, modeling, and deployment in each tool is a valuable skill set. Machine Learning with Microsoft Technologies is a demo-driven book that explains how to do machine learning with Microsoft technologies. You will gain valuable insight into designing the best architecture for development, sharing, and deploying a machine learning solution. This book simplifies the process of choosing the right architecture and tools for doing machine learning based on your specific infrastructure needs and requirements. Detailed content is provided on the main algorithms for supervised and unsupervised machine learning and examples show ML practices using both R and Python languages, the main languages inside Microsoft technologies. What You'll Learn Choose the right Microsoft product for your machine learning solutionCreate and manage Microsoft’s tool environments for development, testing, and production of a machine learning projectImplement and deploy supervised and unsupervised learning in Microsoft products Set up Microsoft Power BI, Azure Data Lake, SQL Server, Stream Analytics, Azure Databricks, and HD Insight to perform machine learning Set up a data science virtual machine and test-drive installed tools, such as Azure ML Workbench, Azure ML Server Developer, Anaconda Python, Jupyter Notebook, Power BI Desktop, Cognitive Services, machine learning and data analytics tools, and more Architect a machine learning solution factoring in all aspects of self service, enterprise, deployment, and sharing Who This Book Is For Data scientists, data analysts, developers, architects, and managers who want to leverage machine learning in their products, organization, and services, and make educated, cost-saving decisions about their ML architecture and tool set.

Microsoft Azure Security Center

Yuri Diogenes,Tom Shinder

Author : Yuri Diogenes,Tom Shinder
Publisher : Microsoft Press
Page : 307 pages
File Size : 53,6 Mb
Release : 2018-06-04
Category : Computers
ISBN : 9781509307067

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Microsoft Azure Security Center by Yuri Diogenes,Tom Shinder Pdf

Discover high-value Azure security insights, tips, and operational optimizations This book presents comprehensive Azure Security Center techniques for safeguarding cloud and hybrid environments. Leading Microsoft security and cloud experts Yuri Diogenes and Dr. Thomas Shinder show how to apply Azure Security Center’s full spectrum of features and capabilities to address protection, detection, and response in key operational scenarios. You’ll learn how to secure any Azure workload, and optimize virtually all facets of modern security, from policies and identity to incident response and risk management. Whatever your role in Azure security, you’ll learn how to save hours, days, or even weeks by solving problems in most efficient, reliable ways possible. Two of Microsoft’s leading cloud security experts show how to: • Assess the impact of cloud and hybrid environments on security, compliance, operations, data protection, and risk management • Master a new security paradigm for a world without traditional perimeters • Gain visibility and control to secure compute, network, storage, and application workloads • Incorporate Azure Security Center into your security operations center • Integrate Azure Security Center with Azure AD Identity Protection Center and third-party solutions • Adapt Azure Security Center’s built-in policies and definitions for your organization • Perform security assessments and implement Azure Security Center recommendations • Use incident response features to detect, investigate, and address threats • Create high-fidelity fusion alerts to focus attention on your most urgent security issues • Implement application whitelisting and just-in-time VM access • Monitor user behavior and access, and investigate compromised or misused credentials • Customize and perform operating system security baseline assessments • Leverage integrated threat intelligence to identify known bad actors

Microsoft Azure Sentinel

Yuri Diogenes,Nicholas DiCola,Jonathan Trull

Author : Yuri Diogenes,Nicholas DiCola,Jonathan Trull
Publisher : Microsoft Press
Page : 347 pages
File Size : 47,7 Mb
Release : 2020-02-25
Category : Computers
ISBN : 9780136485421

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Microsoft Azure Sentinel by Yuri Diogenes,Nicholas DiCola,Jonathan Trull Pdf

Microsoft Azure Sentinel Plan, deploy, and operate Azure Sentinel, Microsoft’s advanced cloud-based SIEM Microsoft’s cloud-based Azure Sentinel helps you fully leverage advanced AI to automate threat identification and response – without the complexity and scalability challenges of traditional Security Information and Event Management (SIEM) solutions. Now, three of Microsoft’s leading experts review all it can do, and guide you step by step through planning, deployment, and daily operations. Leveraging in-the-trenches experience supporting early customers, they cover everything from configuration to data ingestion, rule development to incident management… even proactive threat hunting to disrupt attacks before you’re exploited. Three of Microsoft’s leading security operations experts show how to: • Use Azure Sentinel to respond to today’s fast-evolving cybersecurity environment, and leverage the benefits of its cloud-native architecture • Review threat intelligence essentials: attacker motivations, potential targets, and tactics, techniques, and procedures • Explore Azure Sentinel components, architecture, design considerations, and initial configuration • Ingest alert log data from services and endpoints you need to monitor • Build and validate rules to analyze ingested data and create cases for investigation • Prevent alert fatigue by projecting how many incidents each rule will generate • Help Security Operation Centers (SOCs) seamlessly manage each incident’s lifecycle • Move towards proactive threat hunting: identify sophisticated threat behaviors and disrupt cyber kill chains before you’re exploited • Do more with data: use programmable Jupyter notebooks and their libraries for machine learning, visualization, and data analysis • Use Playbooks to perform Security Orchestration, Automation and Response (SOAR) • Save resources by automating responses to low-level events • Create visualizations to spot trends, identify or clarify relationships, and speed decisions • Integrate with partners and other third-parties, including Fortinet, AWS, and Palo Alto

Predictive Analytics with Microsoft Azure Machine Learning

Valentine Fontama,Roger Barga,Wee Hyong Tok

Author : Valentine Fontama,Roger Barga,Wee Hyong Tok
Publisher : Apress
Page : 178 pages
File Size : 53,9 Mb
Release : 2014-11-25
Category : Computers
ISBN : 9781484204450

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Predictive Analytics with Microsoft Azure Machine Learning by Valentine Fontama,Roger Barga,Wee Hyong Tok Pdf

Data Science and Machine Learning are in high demand, as customers are increasingly looking for ways to glean insights from all their data. More customers now realize that Business Intelligence is not enough as the volume, speed and complexity of data now defy traditional analytics tools. While Business Intelligence addresses descriptive and diagnostic analysis, Data Science unlocks new opportunities through predictive and prescriptive analysis. The purpose of this book is to provide a gentle and instructionally organized introduction to the field of data science and machine learning, with a focus on building and deploying predictive models. The book also provides a thorough overview of the Microsoft Azure Machine Learning service using task oriented descriptions and concrete end-to-end examples, sufficient to ensure the reader can immediately begin using this important new service. It describes all aspects of the service from data ingress to applying machine learning and evaluating the resulting model, to deploying the resulting model as a machine learning web service. Finally, this book attempts to have minimal dependencies, so that you can fairly easily pick and choose chapters to read. When dependencies do exist, they are listed at the start and end of the chapter. The simplicity of this new service from Microsoft will help to take Data Science and Machine Learning to a much broader audience than existing products in this space. Learn how you can quickly build and deploy sophisticated predictive models as machine learning web services with the new Azure Machine Learning service from Microsoft.

MATLAB for Machine Learning

Giuseppe Ciaburro

Author : Giuseppe Ciaburro
Publisher : Packt Publishing Ltd
Page : 374 pages
File Size : 50,5 Mb
Release : 2024-01-30
Category : Computers
ISBN : 9781835089538

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MATLAB for Machine Learning by Giuseppe Ciaburro Pdf

Master MATLAB tools for creating machine learning applications through effective code writing, guided by practical examples showcasing the versatility of machine learning in real-world applications Key Features Work with the MATLAB Machine Learning Toolbox to implement a variety of machine learning algorithms Evaluate, deploy, and operationalize your custom models, incorporating bias detection and pipeline monitoring Uncover effective approaches to deep learning for computer vision, time series analysis, and forecasting Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionDiscover why the MATLAB programming environment is highly favored by researchers and math experts for machine learning with this guide which is designed to enhance your proficiency in both machine learning and deep learning using MATLAB, paving the way for advanced applications. By navigating the versatile machine learning tools in the MATLAB environment, you’ll learn how to seamlessly interact with the workspace. You’ll then move on to data cleansing, data mining, and analyzing various types of data in machine learning, and visualize data values on a graph. As you progress, you’ll explore various classification and regression techniques, skillfully applying them with MATLAB functions. This book teaches you the essentials of neural networks, guiding you through data fitting, pattern recognition, and cluster analysis. You’ll also explore feature selection and extraction techniques for performance improvement through dimensionality reduction. Finally, you’ll leverage MATLAB tools for deep learning and managing convolutional neural networks. By the end of the book, you’ll be able to put it all together by applying major machine learning algorithms in real-world scenarios.What you will learn Discover different ways to transform data into valuable insights Explore the different types of regression techniques Grasp the basics of classification through Naive Bayes and decision trees Use clustering to group data based on similarity measures Perform data fitting, pattern recognition, and cluster analysis Implement feature selection and extraction for dimensionality reduction Harness MATLAB tools for deep learning exploration Who this book is for This book is for ML engineers, data scientists, DL engineers, and CV/NLP engineers who want to use MATLAB for machine learning and deep learning. A fundamental understanding of programming concepts is necessary to get started.

Active Machine Learning with Python

Margaux Masson-Forsythe

Author : Margaux Masson-Forsythe
Publisher : Packt Publishing Ltd
Page : 176 pages
File Size : 49,5 Mb
Release : 2024-03-29
Category : Computers
ISBN : 9781835462683

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Active Machine Learning with Python by Margaux Masson-Forsythe Pdf

Use active machine learning with Python to improve the accuracy of predictive models, streamline the data analysis process, and adapt to evolving data trends, fostering innovation and progress across diverse fields Key Features Learn how to implement a pipeline for optimal model creation from large datasets and at lower costs Gain profound insights within your data while achieving greater efficiency and speed Apply your knowledge to real-world use cases and solve complex ML problems Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionBuilding accurate machine learning models requires quality data—lots of it. However, for most teams, assembling massive datasets is time-consuming, expensive, or downright impossible. Led by Margaux Masson-Forsythe, a seasoned ML engineer and advocate for surgical data science and climate AI advancements, this hands-on guide to active machine learning demonstrates how to train robust models with just a fraction of the data using Python's powerful active learning tools. You’ll master the fundamental techniques of active learning, such as membership query synthesis, stream-based sampling, and pool-based sampling and gain insights for designing and implementing active learning algorithms with query strategy and Human-in-the-Loop frameworks. Exploring various active machine learning techniques, you’ll learn how to enhance the performance of computer vision models like image classification, object detection, and semantic segmentation and delve into a machine AL method for selecting the most informative frames for labeling large videos, addressing duplicated data. You’ll also assess the effectiveness and efficiency of active machine learning systems through performance evaluation. By the end of the book, you’ll be able to enhance your active learning projects by leveraging Python libraries, frameworks, and commonly used tools.What you will learn Master the fundamentals of active machine learning Understand query strategies for optimal model training with minimal data Tackle class imbalance, concept drift, and other data challenges Evaluate and analyze active learning model performance Integrate active learning libraries into workflows effectively Optimize workflows for human labelers Explore the finest active learning tools available today Who this book is for Ideal for data scientists and ML engineers aiming to maximize model performance while minimizing costly data labeling, this book is your guide to optimizing ML workflows and prioritizing quality over quantity. Whether you’re a technical practitioner or team lead, you’ll benefit from the proven methods presented in this book to slash data requirements and iterate faster. Basic Python proficiency and familiarity with machine learning concepts such as datasets and convolutional neural networks is all you need to get started.

Learn Azure Sentinel

Richard Diver,Gary Bushey

Author : Richard Diver,Gary Bushey
Publisher : Packt Publishing Ltd
Page : 423 pages
File Size : 52,9 Mb
Release : 2020-04-07
Category : Computers
ISBN : 9781839216633

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Learn Azure Sentinel by Richard Diver,Gary Bushey Pdf

Understand how to set up, configure, and use Azure Sentinel to provide security incident and event management services for your environment Key FeaturesSecure your network, infrastructure, data, and applications on Microsoft Azure effectivelyIntegrate artificial intelligence, threat analysis, and automation for optimal security solutionsInvestigate possible security breaches and gather forensic evidence to prevent modern cyber threatsBook Description Azure Sentinel is a Security Information and Event Management (SIEM) tool developed by Microsoft to integrate cloud security and artificial intelligence (AI). Azure Sentinel not only helps clients identify security issues in their environment, but also uses automation to help resolve these issues. With this book, you’ll implement Azure Sentinel and understand how it can help find security incidents in your environment with integrated artificial intelligence, threat analysis, and built-in and community-driven logic. This book starts with an introduction to Azure Sentinel and Log Analytics. You’ll get to grips with data collection and management, before learning how to create effective Azure Sentinel queries to detect anomalous behaviors and patterns of activity. As you make progress, you’ll understand how to develop solutions that automate the responses required to handle security incidents. Finally, you’ll grasp the latest developments in security, discover techniques to enhance your cloud security architecture, and explore how you can contribute to the security community. By the end of this book, you’ll have learned how to implement Azure Sentinel to fit your needs and be able to protect your environment from cyber threats and other security issues. What you will learnUnderstand how to design and build a security operations centerDiscover the key components of a cloud security architectureManage and investigate Azure Sentinel incidentsUse playbooks to automate incident responsesUnderstand how to set up Azure Monitor Log Analytics and Azure SentinelIngest data into Azure Sentinel from the cloud and on-premises devicesPerform threat hunting in Azure SentinelWho this book is for This book is for solution architects and system administrators who are responsible for implementing new solutions in their infrastructure. Security analysts who need to monitor and provide immediate security solutions or threat hunters looking to learn how to use Azure Sentinel to investigate possible security breaches and gather forensic evidence will also benefit from this book. Prior experience with cloud security, particularly Azure, is necessary.

Deep Learning with Azure

Mathew Salvaris,Danielle Dean,Wee Hyong Tok

Author : Mathew Salvaris,Danielle Dean,Wee Hyong Tok
Publisher : Apress
Page : 298 pages
File Size : 51,7 Mb
Release : 2018-08-24
Category : Computers
ISBN : 9781484236796

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Deep Learning with Azure by Mathew Salvaris,Danielle Dean,Wee Hyong Tok Pdf

Get up-to-speed with Microsoft's AI Platform. Learn to innovate and accelerate with open and powerful tools and services that bring artificial intelligence to every data scientist and developer. Artificial Intelligence (AI) is the new normal. Innovations in deep learning algorithms and hardware are happening at a rapid pace. It is no longer a question of should I build AI into my business, but more about where do I begin and how do I get started with AI? Written by expert data scientists at Microsoft, Deep Learning with the Microsoft AI Platform helps you with the how-to of doing deep learning on Azure and leveraging deep learning to create innovative and intelligent solutions. Benefit from guidance on where to begin your AI adventure, and learn how the cloud provides you with all the tools, infrastructure, and services you need to do AI. What You'll Learn Become familiar with the tools, infrastructure, and services available for deep learning on Microsoft Azure such as Azure Machine Learning services and Batch AI Use pre-built AI capabilities (Computer Vision, OCR, gender, emotion, landmark detection, and more) Understand the common deep learning models, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs) with sample code and understand how the field is evolving Discover the options for training and operationalizing deep learning models on Azure Who This Book Is For Professional data scientists who are interested in learning more about deep learning and how to use the Microsoft AI platform. Some experience with Python is helpful.

Machine Learning Techniques and Analytics for Cloud Security

Rajdeep Chakraborty,Anupam Ghosh,Jyotsna Kumar Mandal

Author : Rajdeep Chakraborty,Anupam Ghosh,Jyotsna Kumar Mandal
Publisher : John Wiley & Sons
Page : 484 pages
File Size : 53,5 Mb
Release : 2021-11-30
Category : Computers
ISBN : 9781119764090

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Machine Learning Security With Azure

Microsoft Azure Essentials Azure Machine Learning by Jeff Barnes Pdf

Machine Learning Security with Azure by Georgia Kalyva Pdf

Hands-On Machine Learning with Azure by Thomas K Abraham,Parashar Shah,Jen Stirrup,Lauri Lehman,Anindita Basak Pdf

Machine Learning Techniques and Analytics for Cloud Security by Rajdeep Chakraborty,Anupam Ghosh,Jyotsna Kumar Mandal Pdf

Automated Machine Learning with Microsoft Azure by Dennis Michael Sawyers Pdf

Machine Learning, Blockchain, and Cyber Security in Smart Environments by Sarvesh Tanwar,Sumit Badotra,Ajay Rana Pdf

Machine Learning with Microsoft Technologies by Leila Etaati Pdf

Microsoft Azure Security Center by Yuri Diogenes,Tom Shinder Pdf

Microsoft Azure Sentinel by Yuri Diogenes,Nicholas DiCola,Jonathan Trull Pdf

Predictive Analytics with Microsoft Azure Machine Learning by Valentine Fontama,Roger Barga,Wee Hyong Tok Pdf

MATLAB for Machine Learning by Giuseppe Ciaburro Pdf

Active Machine Learning with Python by Margaux Masson-Forsythe Pdf

Learn Azure Sentinel by Richard Diver,Gary Bushey Pdf

Deep Learning with Azure by Mathew Salvaris,Danielle Dean,Wee Hyong Tok Pdf

Machine Learning Techniques and Analytics for Cloud Security by Rajdeep Chakraborty,Anupam Ghosh,Jyotsna Kumar Mandal Pdf

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