Threat Hunting with Elastic Stack

Threat Hunting with Elastic Stack
Author: Andrew Pease
Publisher: Packt Publishing Ltd
Total Pages: 392
Release: 2021-07-23
Genre: Computers
ISBN: 1801079803

Learn advanced threat analysis techniques in practice by implementing Elastic Stack security features Key FeaturesGet started with Elastic Security configuration and featuresLeverage Elastic Stack features to provide optimal protection against threatsDiscover tips, tricks, and best practices to enhance the security of your environmentBook Description Threat Hunting with Elastic Stack will show you how to make the best use of Elastic Security to provide optimal protection against cyber threats. With this book, security practitioners working with Kibana will be able to put their knowledge to work and detect malicious adversary activity within their contested network. You'll take a hands-on approach to learning the implementation and methodologies that will have you up and running in no time. Starting with the foundational parts of the Elastic Stack, you'll explore analytical models and how they support security response and finally leverage Elastic technology to perform defensive cyber operations. You'll then cover threat intelligence analytical models, threat hunting concepts and methodologies, and how to leverage them in cyber operations. After you've mastered the basics, you'll apply the knowledge you've gained to build and configure your own Elastic Stack, upload data, and explore that data directly as well as by using the built-in tools in the Kibana app to hunt for nefarious activities. By the end of this book, you'll be able to build an Elastic Stack for self-training or to monitor your own network and/or assets and use Kibana to monitor and hunt for adversaries within your network. What you will learnExplore cyber threat intelligence analytical models and hunting methodologiesBuild and configure Elastic Stack for cyber threat huntingLeverage the Elastic endpoint and Beats for data collectionPerform security data analysis using the Kibana Discover, Visualize, and Dashboard appsExecute hunting and response operations using the Kibana Security appUse Elastic Common Schema to ensure data uniformity across organizationsWho this book is for Security analysts, cybersecurity enthusiasts, information systems security staff, or anyone who works with the Elastic Stack for security monitoring, incident response, intelligence analysis, or threat hunting will find this book useful. Basic working knowledge of IT security operations and network and endpoint systems is necessary to get started.

Applied Incident Response

Applied Incident Response
Author: Steve Anson
Publisher: John Wiley & Sons
Total Pages: 471
Release: 2020-01-29
Genre: Computers
ISBN: 1119560268

Incident response is critical for the active defense of any network, and incident responders need up-to-date, immediately applicable techniques with which to engage the adversary. Applied Incident Response details effective ways to respond to advanced attacks against local and remote network resources, providing proven response techniques and a framework through which to apply them. As a starting point for new incident handlers, or as a technical reference for hardened IR veterans, this book details the latest techniques for responding to threats against your network, including: Preparing your environment for effective incident response Leveraging MITRE ATT&CK and threat intelligence for active network defense Local and remote triage of systems using PowerShell, WMIC, and open-source tools Acquiring RAM and disk images locally and remotely Analyzing RAM with Volatility and Rekall Deep-dive forensic analysis of system drives using open-source or commercial tools Leveraging Security Onion and Elastic Stack for network security monitoring Techniques for log analysis and aggregating high-value logs Static and dynamic analysis of malware with YARA rules, FLARE VM, and Cuckoo Sandbox Detecting and responding to lateral movement techniques, including pass-the-hash, pass-the-ticket, Kerberoasting, malicious use of PowerShell, and many more Effective threat hunting techniques Adversary emulation with Atomic Red Team Improving preventive and detective controls

Machine Learning with the Elastic Stack

Machine Learning with the Elastic Stack
Author: Rich Collier
Publisher: Packt Publishing Ltd
Total Pages: 299
Release: 2019-01-31
Genre: Computers
ISBN: 1788471776

Leverage Elastic Stack’s machine learning features to gain valuable insight from your data Key FeaturesCombine machine learning with the analytic capabilities of Elastic StackAnalyze large volumes of search data and gain actionable insight from themUse external analytical tools with your Elastic Stack to improve its performanceBook Description Machine Learning with the Elastic Stack is a comprehensive overview of the embedded commercial features of anomaly detection and forecasting. The book starts with installing and setting up Elastic Stack. You will perform time series analysis on varied kinds of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you will deploy machine learning within the Elastic Stack for logging, security, and metrics. In the concluding chapters, you will see how machine learning jobs can be automatically distributed and managed across the Elasticsearch cluster and made resilient to failure. By the end of this book, you will understand the performance aspects of incorporating machine learning within the Elastic ecosystem and create anomaly detection jobs and view results from Kibana directly. What you will learnInstall the Elastic Stack to use machine learning featuresUnderstand how Elastic machine learning is used to detect a variety of anomaly typesApply effective anomaly detection to IT operations and security analyticsLeverage the output of Elastic machine learning in custom views, dashboards, and proactive alertingCombine your created jobs to correlate anomalies of different layers of infrastructureLearn various tips and tricks to get the most out of Elastic machine learningWho this book is for If you are a data professional eager to gain insight on Elasticsearch data without having to rely on a machine learning specialist or custom development, Machine Learning with the Elastic Stack is for you. Those looking to integrate machine learning within their search and analytics applications will also find this book very useful. Prior experience with the Elastic Stack is needed to get the most out of this book.

Practical Threat Intelligence and Data-Driven Threat Hunting

Practical Threat Intelligence and Data-Driven Threat Hunting
Author: Valentina Costa-Gazcón
Publisher: Packt Publishing Ltd
Total Pages: 398
Release: 2021-02-12
Genre: Computers
ISBN: 1838551638

Get to grips with cyber threat intelligence and data-driven threat hunting while exploring expert tips and techniques Key Features Set up an environment to centralize all data in an Elasticsearch, Logstash, and Kibana (ELK) server that enables threat hunting Carry out atomic hunts to start the threat hunting process and understand the environment Perform advanced hunting using MITRE ATT&CK Evals emulations and Mordor datasets Book DescriptionThreat hunting (TH) provides cybersecurity analysts and enterprises with the opportunity to proactively defend themselves by getting ahead of threats before they can cause major damage to their business. This book is not only an introduction for those who don’t know much about the cyber threat intelligence (CTI) and TH world, but also a guide for those with more advanced knowledge of other cybersecurity fields who are looking to implement a TH program from scratch. You will start by exploring what threat intelligence is and how it can be used to detect and prevent cyber threats. As you progress, you’ll learn how to collect data, along with understanding it by developing data models. The book will also show you how to set up an environment for TH using open source tools. Later, you will focus on how to plan a hunt with practical examples, before going on to explore the MITRE ATT&CK framework. By the end of this book, you’ll have the skills you need to be able to carry out effective hunts in your own environment.What you will learn Understand what CTI is, its key concepts, and how it is useful for preventing threats and protecting your organization Explore the different stages of the TH process Model the data collected and understand how to document the findings Simulate threat actor activity in a lab environment Use the information collected to detect breaches and validate the results of your queries Use documentation and strategies to communicate processes to senior management and the wider business Who this book is for If you are looking to start out in the cyber intelligence and threat hunting domains and want to know more about how to implement a threat hunting division with open-source tools, then this cyber threat intelligence book is for you.

Network Security Through Data Analysis

Network Security Through Data Analysis
Author: Michael S Collins
Publisher: "O'Reilly Media, Inc."
Total Pages: 416
Release: 2014-02-10
Genre: Computers
ISBN: 1449357865

Traditional intrusion detection and logfile analysis are no longer enough to protect today’s complex networks. In this practical guide, security researcher Michael Collins shows you several techniques and tools for collecting and analyzing network traffic datasets. You’ll understand how your network is used, and what actions are necessary to protect and improve it. Divided into three sections, this book examines the process of collecting and organizing data, various tools for analysis, and several different analytic scenarios and techniques. It’s ideal for network administrators and operational security analysts familiar with scripting. Explore network, host, and service sensors for capturing security data Store data traffic with relational databases, graph databases, Redis, and Hadoop Use SiLK, the R language, and other tools for analysis and visualization Detect unusual phenomena through Exploratory Data Analysis (EDA) Identify significant structures in networks with graph analysis Determine the traffic that’s crossing service ports in a network Examine traffic volume and behavior to spot DDoS and database raids Get a step-by-step process for network mapping and inventory

Designing a HIPAA-Compliant Security Operations Center

Designing a HIPAA-Compliant Security Operations Center
Author: Eric C. Thompson
Publisher: Apress
Total Pages: 241
Release: 2020-02-25
Genre: Computers
ISBN: 1484256085

Develop a comprehensive plan for building a HIPAA-compliant security operations center, designed to detect and respond to an increasing number of healthcare data breaches and events. Using risk analysis, assessment, and management data combined with knowledge of cybersecurity program maturity, this book gives you the tools you need to operationalize threat intelligence, vulnerability management, security monitoring, and incident response processes to effectively meet the challenges presented by healthcare’s current threats. Healthcare entities are bombarded with data. Threat intelligence feeds, news updates, and messages come rapidly and in many forms such as email, podcasts, and more. New vulnerabilities are found every day in applications, operating systems, and databases while older vulnerabilities remain exploitable. Add in the number of dashboards, alerts, and data points each information security tool provides and security teams find themselves swimming in oceans of data and unsure where to focus their energy. There is an urgent need to have a cohesive plan in place to cut through the noise and face these threats. Cybersecurity operations do not require expensive tools or large capital investments. There are ways to capture the necessary data. Teams protecting data and supporting HIPAA compliance can do this. All that’s required is a plan—which author Eric Thompson provides in this book. What You Will Learn Know what threat intelligence is and how you can make it useful Understand how effective vulnerability management extends beyond the risk scores provided by vendors Develop continuous monitoring on a budget Ensure that incident response is appropriate Help healthcare organizations comply with HIPAA Who This Book Is For Cybersecurity, privacy, and compliance professionals working for organizations responsible for creating, maintaining, storing, and protecting patient information.

Threat Hunting in the Cloud

Threat Hunting in the Cloud
Author: Chris Peiris
Publisher: John Wiley & Sons
Total Pages: 636
Release: 2021-08-31
Genre: Computers
ISBN: 1119804108

Implement a vendor-neutral and multi-cloud cybersecurity and risk mitigation framework with advice from seasoned threat hunting pros In Threat Hunting in the Cloud: Defending AWS, Azure and Other Cloud Platforms Against Cyberattacks, celebrated cybersecurity professionals and authors Chris Peiris, Binil Pillai, and Abbas Kudrati leverage their decades of experience building large scale cyber fusion centers to deliver the ideal threat hunting resource for both business and technical audiences. You'll find insightful analyses of cloud platform security tools and, using the industry leading MITRE ATT&CK framework, discussions of the most common threat vectors. You'll discover how to build a side-by-side cybersecurity fusion center on both Microsoft Azure and Amazon Web Services and deliver a multi-cloud strategy for enterprise customers. And you will find out how to create a vendor-neutral environment with rapid disaster recovery capability for maximum risk mitigation. With this book you'll learn: Key business and technical drivers of cybersecurity threat hunting frameworks in today's technological environment Metrics available to assess threat hunting effectiveness regardless of an organization's size How threat hunting works with vendor-specific single cloud security offerings and on multi-cloud implementations A detailed analysis of key threat vectors such as email phishing, ransomware and nation state attacks Comprehensive AWS and Azure "how to" solutions through the lens of MITRE Threat Hunting Framework Tactics, Techniques and Procedures (TTPs) Azure and AWS risk mitigation strategies to combat key TTPs such as privilege escalation, credential theft, lateral movement, defend against command & control systems, and prevent data exfiltration Tools available on both the Azure and AWS cloud platforms which provide automated responses to attacks, and orchestrate preventative measures and recovery strategies Many critical components for successful adoption of multi-cloud threat hunting framework such as Threat Hunting Maturity Model, Zero Trust Computing, Human Elements of Threat Hunting, Integration of Threat Hunting with Security Operation Centers (SOCs) and Cyber Fusion Centers The Future of Threat Hunting with the advances in Artificial Intelligence, Machine Learning, Quantum Computing and the proliferation of IoT devices. Perfect for technical executives (i.e., CTO, CISO), technical managers, architects, system admins and consultants with hands-on responsibility for cloud platforms, Threat Hunting in the Cloud is also an indispensable guide for business executives (i.e., CFO, COO CEO, board members) and managers who need to understand their organization's cybersecurity risk framework and mitigation strategy.

Machine Learning with the Elastic Stack - Second Edition

Machine Learning with the Elastic Stack - Second Edition
Author: Rich Collier
Publisher:
Total Pages: 450
Release: 2021-05-28
Genre:
ISBN: 9781801070034

Discover expert techniques for combining machine learning with the analytic capabilities of Elastic Stack and uncover actionable insights from your data Key Features: Integrate machine learning with distributed search and analytics Preprocess and analyze large volumes of search data effortlessly Operationalize machine learning in a scalable, production-worthy way Book Description: Elastic Stack, previously known as the ELK stack, is a log analysis solution that helps users ingest, process, and analyze search data effectively. With the addition of machine learning, a key commercial feature, the Elastic Stack makes this process even more efficient. This updated second edition of Machine Learning with the Elastic Stack provides a comprehensive overview of Elastic Stack's machine learning features for both time series data analysis as well as for classification, regression, and outlier detection. The book starts by explaining machine learning concepts in an intuitive way. You'll then perform time series analysis on different types of data, such as log files, network flows, application metrics, and financial data. As you progress through the chapters, you'll deploy machine learning within Elastic Stack for logging, security, and metrics. Finally, you'll discover how data frame analysis opens up a whole new set of use cases that machine learning can help you with. By the end of this Elastic Stack book, you'll have hands-on machine learning and Elastic Stack experience, along with the knowledge you need to incorporate machine learning in your distributed search and data analysis platform. What You Will Learn: Find out how to enable the ML commercial feature in the Elastic Stack Understand how Elastic machine learning is used to detect different types of anomalies and make predictions Apply effective anomaly detection to IT operations, security analytics, and other use cases Utilize the results of Elastic ML in custom views, dashboards, and proactive alerting Train and deploy supervised machine learning models for real-time inference Discover various tips and tricks to get the most out of Elastic machine learning Who this book is for: If you're a data professional looking to gain insights into Elasticsearch data without having to rely on a machine learning specialist or custom development, then this Elastic Stack machine learning book is for you. You'll also find this book useful if you want to integrate machine learning with your observability, security, and analytics applications. Working knowledge of the Elastic Stack is needed to get the most out of this book.

Mastering Machine Learning for Penetration Testing

Mastering Machine Learning for Penetration Testing
Author: Chiheb Chebbi
Publisher: Packt Publishing Ltd
Total Pages: 264
Release: 2018-06-27
Genre: Language Arts & Disciplines
ISBN: 178899311X

Become a master at penetration testing using machine learning with Python Key Features Identify ambiguities and breach intelligent security systems Perform unique cyber attacks to breach robust systems Learn to leverage machine learning algorithms Book Description Cyber security is crucial for both businesses and individuals. As systems are getting smarter, we now see machine learning interrupting computer security. With the adoption of machine learning in upcoming security products, it’s important for pentesters and security researchers to understand how these systems work, and to breach them for testing purposes. This book begins with the basics of machine learning and the algorithms used to build robust systems. Once you’ve gained a fair understanding of how security products leverage machine learning, you'll dive into the core concepts of breaching such systems. Through practical use cases, you’ll see how to find loopholes and surpass a self-learning security system. As you make your way through the chapters, you’ll focus on topics such as network intrusion detection and AV and IDS evasion. We’ll also cover the best practices when identifying ambiguities, and extensive techniques to breach an intelligent system. By the end of this book, you will be well-versed with identifying loopholes in a self-learning security system and will be able to efficiently breach a machine learning system. What you will learn Take an in-depth look at machine learning Get to know natural language processing (NLP) Understand malware feature engineering Build generative adversarial networks using Python libraries Work on threat hunting with machine learning and the ELK stack Explore the best practices for machine learning Who this book is for This book is for pen testers and security professionals who are interested in learning techniques to break an intelligent security system. Basic knowledge of Python is needed, but no prior knowledge of machine learning is necessary.

Wireshark for Security Professionals

Wireshark for Security Professionals
Author: Jessey Bullock
Publisher: John Wiley & Sons
Total Pages: 288
Release: 2017-03-20
Genre: Computers
ISBN: 1118918215

Master Wireshark to solve real-world security problems If you don’t already use Wireshark for a wide range of information security tasks, you will after this book. Mature and powerful, Wireshark is commonly used to find root cause of challenging network issues. This book extends that power to information security professionals, complete with a downloadable, virtual lab environment. Wireshark for Security Professionals covers both offensive and defensive concepts that can be applied to essentially any InfoSec role. Whether into network security, malware analysis, intrusion detection, or penetration testing, this book demonstrates Wireshark through relevant and useful examples. Master Wireshark through both lab scenarios and exercises. Early in the book, a virtual lab environment is provided for the purpose of getting hands-on experience with Wireshark. Wireshark is combined with two popular platforms: Kali, the security-focused Linux distribution, and the Metasploit Framework, the open-source framework for security testing. Lab-based virtual systems generate network traffic for analysis, investigation and demonstration. In addition to following along with the labs you will be challenged with end-of-chapter exercises to expand on covered material. Lastly, this book explores Wireshark with Lua, the light-weight programming language. Lua allows you to extend and customize Wireshark’s features for your needs as a security professional. Lua source code is available both in the book and online. Lua code and lab source code are available online through GitHub, which the book also introduces. The book’s final two chapters greatly draw on Lua and TShark, the command-line interface of Wireshark. By the end of the book you will gain the following: Master the basics of Wireshark Explore the virtual w4sp-lab environment that mimics a real-world network Gain experience using the Debian-based Kali OS among other systems Understand the technical details behind network attacks Execute exploitation and grasp offensive and defensive activities, exploring them through Wireshark Employ Lua to extend Wireshark features and create useful scripts To sum up, the book content, labs and online material, coupled with many referenced sources of PCAP traces, together present a dynamic and robust manual for information security professionals seeking to leverage Wireshark.