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+--- Thread: Data To Defense: A Guide To Cybersecurity Analytics (/Thread-Data-To-Defense-A-Guide-To-Cybersecurity-Analytics--733378)
Data To Defense: A Guide To Cybersecurity Analytics - AD-TEAM - 12-21-2024
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Data To Defense: A Guide To Cybersecurity Analytics
Published 11/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 600.35 MB | Duration: 2h 24m
Mastering Cybersecurity Analytics: From Fundamentals to Advanced Techniques
What you'll learn
Understand the fundamental concepts of cybersecurity analytics and its role in protecting digital assets.
Acquire knowledge of various data sources used in cybersecurity analytics, including network traffic, log files, and sensor data.
Learn data preprocessing techniques to prepare data for analysis, such as cleaning, normalization, and feature engineering.
Explore machine learning algorithms relevant to cybersecurity analytics, including anomaly detection, classification, and regression.
Develop skills in data visualization to effectively communicate cybersecurity insights.
Understand the ethical implications of cybersecurity analytics and the importance of privacy and compliance.
Gain practical experience through hands-on projects and case studies.
Requirements
Basic understanding of computer science
Basic understanding of programming (e.g., Python)
Basic understanding of statistics
Description
This comprehensive course is designed to equip you with the essential skills and knowledge to excel in the field of cybersecurity analytics. Whether you're a cybersecurity professional, data analyst, or aspiring security analyst, this course will provide you with a solid foundation and advanced techniques to effectively analyze security data and protect your organization's assets.What You'll Learn:You will learn the fundamental concepts of cybersecurity analytics, including data-driven security and its importance. You will explore various data sources, such as network traffic, logs, and threat intelligence feeds, and master techniques for data cleaning, transformation, and enrichment.You will also delve into data analysis and visualization, applying statistical analysis techniques and utilizing powerful visualization tools like Matplotlib and Seaborn to uncover insights from data.The course covers a wide range of machine learning techniques, including supervised and unsupervised learning algorithms. You will learn how to build and evaluate machine learning models for tasks like anomaly detection, intrusion detection, and threat classification. Additionally, you will explore advanced techniques like deep learning for complex security challenges.You will gain a deep understanding of threat intelligence and hunting, including identifying indicators of compromise (IOCs) and conducting threat hunting. You will also learn how to effectively use Security Information and Event Management (SIEM) systems to analyze security events and detect threats.Finally, you will explore the power of automation and orchestration in cybersecurity. You will learn how to automate routine tasks, streamline incident response, and improve overall security efficiency.What You'll Learn:Fundamental Concepts:Understand the core concepts of cybersecurity analytics, including data-driven security and its importance.Learn about the role of cybersecurity analysts and the key skills required.Data Acquisition and Preparation:Explore various sources of cybersecurity data, such as network traffic, logs, and threat intelligence feeds.Master techniques for data cleaning, transformation, and enrichment.Learn how to handle missing data, outliers, and inconsistencies.Data Analysis and Visualization:Apply statistical analysis techniques to uncover insights from data.Utilize powerful visualization tools to present data effectively.Gain hands-on experience with data visualization libraries like Matplotlib and Seaborn.Machine Learning for Cybersecurity
ive into machine learning concepts and algorithms relevant to cybersecurity.Learn how to build and evaluate machine learning models for tasks like anomaly detection, intrusion detection, and threat classification.Explore advanced techniques like deep learning for complex security challenges.Threat Intelligence and Hunting:Understand the role of threat intelligence in proactive security.Learn how to identify indicators of compromise (IOCs) and conduct threat hunting.Explore techniques for analyzing threat actor tactics, techniques, and procedures (TTPs).SIEM and Security Automation:Master the concepts of Security Information and Event Management (SIEM).Learn how to integrate SIEM with other security tools to enhance threat detection and response.Explore automation tools and frameworks for streamlining security operations.Understand the benefits of orchestration for incident response. Overview
Section 1: Understanding Cybersecurity Analytics
Lecture 1 Introduction to cybersecurity analytics
Lecture 2 Importance of data-driven security
Lecture 3 Role of cybersecurity analysts
Section 2: Data Sources and Collection
Lecture 4 Types of data in cybersecurity
Lecture 5 Data collection methods
Lecture 6 Wireshark data collection demonstration
Lecture 7 Windows event viewer demo
Section 3: Data Preparation and Cleaning
Lecture 8 Data normalization and enrichment
Lecture 9 Data cleaning techniques
Lecture 10 Common Code for ETL processing
Section 4: Exploratory Data Analysis (EDA)
Lecture 11 Statistical analysis
Lecture 12 Data visualization
Section 5: Machine Learning for Cybersecurity Analytics
Lecture 13 Introduction to machine learning
Lecture 14 Learning Algorithms
Lecture 15 Model evaluation and tuning
Lecture 16 Building ML Models for Cybersecurity
Lecture 17 Applying Machine Learning to Cybersecurity
Section 6: Threat Intelligence and Hunting
Lecture 18 Threat intelligence sources
Lecture 19 Threat hunting techniques
Section 7: Security Information and Event Management (SIEM)
Lecture 20 SIEM architecture and components
Lecture 21 SIEM use cases
Lecture 22 demo of SIEM tool
Section 8: Automation and Orchestration
Lecture 23 Automation and orchestration Tools and Frameworks
Lecture 24 Uses cases with automation and orchestration
Section 9: Conclusion and wrap up
Lecture 25 Wrap up
Cybersecurity Professionals: Security analysts, incident responders, threat intelligence analysts, and security operations center (SOC) analysts.,Data Scientists and Analysts: Data scientists and analysts interested in applying their skills to cybersecurity.,IT Professionals: Network engineers, system administrators, and IT operations professionals who want to enhance their security skills.,Students and Academics: Computer science, information technology, and cybersecurity students.,Cybersecurity Enthusiasts: Individuals with a passion for cybersecurity and a desire to learn more.
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