Visit This Web URL https://masterytrail.com/product/accredited-expert-level-ibm-qradar-threat-intelligence-platform-advanced-video-course Lesson 1: Advanced QRadar Architecture and Deployment
1.1. Understanding Distributed Deployment Architectures
1.2. High Availability and Disaster Recovery Configurations
1.3. Scaling QRadar Components (Event Processors, Flow Processors, Data Nodes)
1.4. Multi-Tenant Deployments and Management
1.5. Integrating QRadar in Cloud and Hybrid Environments
1.6. Performance Tuning of QRadar Appliances
1.7. Database Optimization for Large-Scale Deployments
1.8. Network Hierarchy and Asset Profiling Deep Dive
1.9. Leveraging Ariel Database for Advanced Searching
1.10. Backup and Restoration Strategies for Critical Data
Lesson 2: Advanced Log Source Management and Custom DSM Development
2.1. Deep Dive into Log Source Protocols and Collection Methods
2.2. Developing Custom Device Support Modules (DSMs) from Scratch
2.3. Parsing Complex and Multi-Line Events
2.4. Utilizing the DSM Editor for Advanced Configurations
2.5. Regular Expressions for Event Property Extraction
2.6. Handling and Parsing Structured Logs (JSON, XML)
2.7. Troubleshooting Log Source Integration Issues
2.8. Optimizing Event Collection for Performance
2.9. Implementing Custom Properties for Enhanced Analysis
2.10. Best Practices for Log Source Management in Large Environments
Lesson 3: Mastering QRadar Rule and Building Block Logic
3.1. Advanced Concepts of Rule Processing Order and Grouping
3.2. Crafting Complex Correlation Rules with Multiple Conditions
3.3. Utilizing Reference Data Collections for Dynamic Rules
3.4. Building Blocks for Reusable Logic and Conditionals
3.5. Leveraging AQL within Custom Rules
3.6. Anomaly Detection Rules and Configuration
3.7. Behavioral Rules Based on Baselines and Profiling
3.8. Threshold and Accumulation Rules for Pattern Detection
3.9. Optimizing Rule Performance and Reducing False Positives
3.10. Documenting and Managing Custom Rule Sets
Lesson 4: Advanced Use of Reference Data and Lookups
4.1. Creating and Managing Large Reference Data Collections
4.2. Populating Reference Data Programmatically (API, Scripts)
4.3. Utilizing Reference Sets, Maps, Tables, and Maps of Sets
4.4. Dynamic Updates of Reference Data Based on Events
4.5. Performance Impact of Large Reference Data Collections
4.6. Troubleshooting Reference Data Issues
4.7. Use Cases for Advanced Reference Data Applications
4.8. Integrating External Data Sources with Reference Data
4.9. Best Practices for Reference Data Management
4.10. Automating Reference Data Updates and Synchronization
Lesson 5: Integrating and Leveraging IBM X-Force Threat Intelligence
5.1. Understanding X-Force Categories and Feeds
5.2. Configuring and Verifying X-Force Integration
5.3. Utilizing X-Force Data within Rules and Offenses
5.4. Enriching Event and Flow Data with Threat Intelligence
5.5. Investigating Indicators of Compromise (IoCs) with X-Force
5.6. Leveraging the Threat Intelligence App
5.7. Customizing Threat Intelligence Feeds
5.8. Automating Actions Based on Threat Intelligence Matches
5.9. Staying Updated with the Latest Threat Intelligence
5.10. Measuring the Effectiveness of Threat Intelligence
Lesson 6: Advanced Threat Hunting Techniques with QRadar
6.1. Developing Threat Hunting Hypotheses
6.2. Utilizing AQL for Complex Data Exploration
6.3. Hunting for Advanced Persistent Threats (APTs)
6.4. Identifying Insider Threats with Behavioral Analysis
6.5. Leveraging MITRE ATT&CK Framework in Hunting
6.6. Creating Custom Dashboards for Threat Hunting
6.7. Automating Threat Hunting Queries and Alerts
6.8. Integrating External Hunting Tools with QRadar
6.9. Documenting and Operationalizing Threat Hunts
6.10. Measuring the Success of Threat Hunting Activities
Lesson 7: User Behavior Analytics (UBA) and QRadar Advisor with Watson
7.1. Deploying and Configuring UBA
7.2. Understanding UBA Dashboards and Risk Scoring
7.3. Investigating User Anomalies and Risky Behaviors
7.4. Integrating UBA Findings into Offense Management
7.5. Leveraging QRadar Advisor with Watson for Incident Investigation
7.6. Understanding Watson's Analysis and Insights
7.7. Training and Tuning QRadar Advisor
7.8. Combining UBA and Advisor for Enhanced Detection
7.9. Use Cases for Advanced Behavioral Analytics
7.10. Troubleshooting UBA and Advisor Deployments
Lesson 8: Advanced QRadar Tuning and Optimization
8.1. Identifying and Addressing Performance Bottlenecks
8.2. Tuning Rules and Building Blocks for Efficiency
8.3. Optimizing Ariel Queries for Speed
8.4. Managing and Reducing False Positives Effectively
8.5. Utilizing Tuning Profiles and Exclusion Filters
8.6. Monitoring QRadar System Health and Performance Metrics
8.7. Capacity Planning for Future Growth
8.8. Leveraging QRadar Apps for Performance Analysis
8.9. Advanced QRadar Log Source and Event Tuning
8.10. Continuous Monitoring and Improvement of QRadar Performance
Lesson 9: Custom Action Scripts and External Integrations
9.1. Developing Custom Action Scripts for Automated Responses
9.2. Integrating with External Security Tools via Scripts
9.3. Executing Scripts Based on Rule Matches
9.4. Securely Managing and Deploying Custom Scripts
9.5. Error Handling and Logging in Custom Scripts
9.6. Use Cases for Automated Remediation with Scripts
9.7. Integrating with Ticketing Systems and Workflow Tools
9.8. Utilizing QRadar APIs within External Scripts
9.9. Troubleshooting Custom Action Script Issues
9.10. Maintaining and Updating Custom Script Integrations
Lesson 10: Advanced QRadar App Development
10.1. Utilizing the QRadar App SDK for Development
10.2. Building Custom Dashboards and Visualizations
10.3. Creating Custom Pages and Workflows within QRadar
10.4. Leveraging QRadar APIs within Custom Apps
10.5. Integrating External Data Sources into Apps
10.6. Packaging and Deploying QRadar Apps
10.7. Debugging and Troubleshooting App Development
10.8. Security Best Practices for App Development
10.9. Submitting and Publishing Apps to the App Exchange
10.10. Maintaining and Updating Deployed Apps
Lesson 11: QRadar API for Automation and Orchestration
11.1. Exploring the QRadar REST API Architecture
11.2. Authenticating and Authorizing API Access
11.3. Performing Advanced Searches and Retrieving Data via API
11.4. Managing Offenses and Incidents Programmatically
11.5. Automating Configuration Tasks via API
11.6. Integrating QRadar with SOAR Platforms via API
11.7. Developing Custom API Clients
11.8. Rate Limiting and API Usage Best Practices
11.9. Troubleshooting API Integration Issues
11.10. Monitoring API Usage and Performance
Lesson 12: Integration with IBM Security Orchestration, Automation and Response (SOAR)
12.1. Understanding the Role of SOAR in the Security Operations Center
12.2. Configuring QRadar Integration with IBM SOAR
12.3. Triggering SOAR Playbooks from QRadar Offenses
12.4. Sharing Context and Data Between QRadar and SOAR
12.5. Automating Incident Response Tasks with Playbooks
12.6. Customizing SOAR Playbooks for QRadar Use Cases
12.7. Orchestrating Actions Across Multiple Security Tools
12.8. Measuring the Effectiveness of SOAR Integration
12.9. Troubleshooting QRadar and SOAR Connectivity
12.10. Advanced Use Cases for Security Automation
Lesson 13: QRadar Risk Manager Deep Dive
13.1. Deploying and Configuring QRadar Risk Manager (QRM)
13.2. Importing Network Topology and Device Configurations
13.3. Analyzing Network Risks and Vulnerabilities
13.4. Simulating Attack Paths and Impact
13.5. Prioritizing Risks Based on Asset Criticality
13.6. Integrating Vulnerability Scan Data with QRM
13.7. Utilizing QRM in conjunction with QRadar SIEM
13.8. Reporting on Network Risk Posture
13.9. Troubleshooting QRM Deployment and Data Collection
13.10. Advanced Risk Modeling and Analysis
Lesson 14: Advanced QRadar Compliance and Reporting
14.1. Mapping Compliance Requirements to QRadar Content
14.2. Utilizing and Customizing Compliance Use Cases
14.3. Building Custom Reports for Regulatory Audits
14.4. Scheduling and Automating Compliance Reports
14.5. Integrating with External Reporting Platforms
14.6. Analyzing Compliance Posture with QRadar Data
14.7. Addressing Compliance Gaps Identified by QRadar
14.8. Utilizing Historical Data for Compliance Trends
14.9. Best Practices for Maintaining Compliance in QRadar
14.10. Demonstrating Compliance with QRadar Reports
Lesson 15: Integrating Machine Learning Models with QRadar
15.1. Understanding Machine Learning Concepts in Cybersecurity
15.2. QRadar's Native Machine Learning Capabilities
15.3. Integrating External Machine Learning Platforms
15.4. Utilizing Machine Learning for Anomaly Detection
15.5. Training and Validating Machine Learning Models
15.6. Operationalizing Machine Learning Findings in QRadar
15.7. Addressing Bias and Explainability in ML Models
15.8. Monitoring and Maintaining ML Model Performance
15.9. Future Trends in Machine Learning for SIEM
15.10. Ethical Considerations of AI and ML in Security
Lesson 16: Expert-Level QRadar Searching with AQL
16.1. Advanced AQL Syntax and Functions
16.2. Optimizing AQL Queries for Large Datasets
16.3. Utilizing Joins and Subqueries in AQL
16.4. Time Series Analysis with AQL
16.5. Leveraging Parsed Properties in AQL Queries
16.6. Creating Complex Filters and Groupings
16.7. Utilizing AQL in Rules, Searches, and Reports
16.8. Troubleshooting AQL Query Performance
16.9. Best Practices for Writing Efficient AQL
16.10. Advanced Use Cases for AQL in Threat Hunting
Lesson 17: Advanced Incident Response Workflows in QRadar
17.1. Customizing Offense Properties and Classification
17.2. Developing Advanced Offense Routing Rules
17.3. Integrating with Incident Response Platforms (beyond SOAR)
17.4. Automating Data Enrichment for Investigations
17.5. Utilizing QRadar Incident Forensics (if applicable)
17.6. Creating Custom Dashboards for Incident Responders
17.7. Documenting Incident Investigations within QRadar
17.8. Post-Incident Analysis and Lessons Learned
17.9. Integrating with Threat Intelligence during Response
17.10. Measuring Incident Response Metrics in QRadar
Lesson 18: QRadar Data Monitoring and Health Management
18.1. Proactive Monitoring of QRadar Component Health
18.2. Setting Up Alerts for System Issues
18.3. Analyzing QRadar System Logs and Metrics
18.4. Utilizing QRadar's ariel_query_database for Monitoring
18.5. Performance Monitoring of Event and Flow Processing
18.6. Disk Space Management and Optimization
18.7. Network Monitoring of QRadar Appliances
18.8. Utilizing External Monitoring Tools for QRadar
18.9. Troubleshooting Common QRadar Health Issues
18.10. Capacity Planning and Resource Allocation
Lesson 19: Advanced QRadar Administration and Maintenance
19.1. User Role and Access Control Deep Dive
19.2. Managing Authentication and Authorization Methods
19.3. Patching and Upgrading QRadar in Complex Deployments
19.4. Managing Licenses and Entitlements
19.5. Command-Line Interface (CLI) for Advanced Tasks
19.6. Scripting QRadar Administration Tasks
19.7. Troubleshooting QRadar System Errors
19.8. Disaster Recovery Planning and Testing
19.9. Security Hardening of QRadar Appliances
19.10. Best Practices for QRadar Administration
Lesson 20: Integrating QRadar with Cloud Security Platforms
20.1. Collecting Logs and Flows from Cloud Environments (AWS, Azure, GCP)
20.2. Utilizing Cloud-Specific Log Sources and DSMs
20.3. Monitoring Cloud Security Posture with QRadar
20.4. Integrating with Cloud Security APIs
20.5. Addressing Challenges of Cloud Data Collection
20.6. Leveraging Cloud-Native Security Controls in QRadar Rules
20.7. Building Cloud-Specific Threat Hunting Use Cases
20.8. Reporting on Cloud Security Events
20.9. Troubleshooting Cloud Integration Issues
20.10. Best Practices for Securing Cloud Deployments with QRadar
Lesson 21: Advanced Network Flow Analysis in QRadar
21.1. Understanding Flow Protocols and Collection Methods
21.2. Analyzing Network Conversations and Sessions
21.3. Identifying Anomalous Network Traffic Patterns
21.4. Utilizing Flow Data in Rules and Offenses
21.5. Enriching Flow Data with Context (Users, Assets)
21.6. Troubleshooting Flow Source Integration
21.7. Performance Tuning of Flow Processors
21.8. Advanced Flow Filtering and Aggregation
21.9. Use Cases for Network-Based Threat Detection
21.10. Integrating External Flow Analysis Tools
Lesson 22: QRadar Vulnerability Manager Integration and Analysis (if applicable)
22.1. Integrating Vulnerability Scanners with QRadar
22.2. Importing and Managing Vulnerability Data
22.3. Correlating Vulnerabilities with Event and Flow Data
22.4. Prioritizing Vulnerabilities Based on Exploitation Activity
25.5. Utilizing Vulnerability Information in Rules
22.6. Reporting on Vulnerability Posture
22.7. Leveraging QRadar for Vulnerability Prioritization
22.8. Troubleshooting Vulnerability Integration Issues
22.9. Automating Vulnerability Remediation Workflows
22.10. Best Practices for Integrated Vulnerability Management
Lesson 23: Advanced QRadar Reporting and Dashboarding
23.1. Designing Complex Reports with Multiple Data Sources
23.2. Utilizing AQL for Advanced Report Queries
23.3. Customizing Report Templates and Formatting
23.4. Creating Executive-Level Dashboards
23.5. Sharing and Scheduling Dashboards and Reports
23.6. Integrating External Business Intelligence Tools
23.7. Performance Considerations for Reports and Dashboards
23.8. Utilizing Pulse Dashboards for Real-time Visualization
23.9. Best Practices for Data Visualization in QRadar
23.10. Automating Report Generation and Distribution
Lesson 24: QRadar and Threat Intelligence Platforms (TIPs) Integration
24.1. Understanding Different Types of TIPs
24.2. Integrating QRadar with External TIPs
24.3. Exchanging Threat Intelligence Data Bidirectionally
24.4. Utilizing TIP Data for Enhanced Correlation
24.5. Automating Threat Intelligence Sharing
24.6. Customizing TIP Integrations
24.7. Troubleshooting TIP Connectivity Issues
24.8. Leveraging TIPs for Proactive Hunting
24.9. Measuring the Value of TIP Integration
24.10. Future of Threat Intelligence Integration
Lesson 25: Advanced Use Cases for QRadar in Different Verticals
25.1. Tailoring QRadar for Financial Services Security
25.2. QRadar in Healthcare: Protecting Patient Data
25.3. Industrial Control Systems (ICS) Security with QRadar
25.4. E-commerce Security and Fraud Detection
25.5. QRadar in Government and Critical Infrastructure
25.6. Developing Vertical-Specific Content and Rules
25.7. Addressing Unique Compliance Requirements by Vertical
25.8. Threat Landscapes and Attack Patterns in Different Industries
25.9. Customizing Dashboards for Vertical-Specific Monitoring
25.10. Case Studies of QRadar Deployments in Various Verticals
Lesson 26: Extending QRadar Functionality with Apps from the App Exchange
26.1. Exploring Advanced Apps on the IBM Security App Exchange
26.2. Evaluating and Selecting Relevant Apps
26.3. Installing and Configuring Complex Apps
26.4. Troubleshooting App Installation and Runtime Issues
26.5. Leveraging App Functionality in Incident Response
26.6. Integrating App Data into Rules and Reports
26.7. Customizing App Configurations
26.8. Contributing to the QRadar App Ecosystem
26.9. Security Considerations for Installing Third-Party Apps
26.10. Staying Updated with New App Releases
Lesson 27: QRadar and Security Operations Center (SOC) Optimization
27.1. Aligning QRadar Capabilities with SOC Processes
27.2. Optimizing Analyst Workflows with QRadar Features
27.3. Utilizing QRadar for Tiered Incident Response
27.4. Measuring SOC Efficiency with QRadar Metrics
27.5. Integrating QRadar with SOC Management Tools
27.6. Training and Mentoring SOC Analysts on QRadar
27.7. Developing Playbooks Based on QRadar Offenses
27.8. Communication and Collaboration within the SOC using QRadar
27.9. Continuous Improvement of SOC Operations with QRadar
27.10. Building a Mature SOC Leveraging QRadar
Lesson 28: Advanced Log Event Extended Format (LEEF) and Custom Properties
28.1. Deep Dive into LEEF Structure and Specification
28.2. Creating Custom LEEF Formats for Unique Log Sources
28.3. Utilizing the LEEF Editor
28.4. Extracting Custom Properties from LEEF Logs
28.5. Leveraging Custom Properties in Rules and Searches
28.6. Performance Impact of Excessive Custom Properties
28.7. Troubleshooting LEEF Parsing Issues
28.8. Best Practices for Defining Custom Properties
28.9. Managing and Documenting Custom Properties
28.10. Automating Custom Property Creation
Lesson 29: QRadar EDR and Network Insights Integration (if applicable)
29.1. Integrating QRadar with Endpoint Detection and Response (EDR) Solutions
29.2. Correlating Endpoint Data with Network and Log Activity
29.3. Utilizing EDR Insights in QRadar Rules and Offenses
29.4. Deploying and Configuring QRadar Network Insights
29.5. Analyzing Network Flow Metadata Provided by Network Insights
29.6. Leveraging Network Insights Data for Threat Hunting
29.7. Troubleshooting EDR and Network Insights Integration
29.8. Use Cases for Enhanced Visibility with EDR and Network Insights
29.9. Combining EDR, Network Insights, and Log Data for Investigations
29.10. Future of Integrated Endpoint and Network Visibility
Lesson 30: QRadar for Insider Threat Detection
30.1. Understanding Insider Threat Kill Chains
30.2. Utilizing UBA for Identifying Malicious Insiders
30.3. Monitoring User Activity and Data Access
30.4. Developing Rules for Detecting Insider Threat Indicators
30.5. Integrating HR and Identity Data with QRadar
30.6. Analyzing User Behavior Anomalies Over Time
30.7. Investigating Potential Insider Threat Incidents
30.8. Reporting on Insider Threat Activity
30.9. Best Practices for Mitigating Insider Threats with QRadar
30.10. Legal and Privacy Considerations in Insider Threat Monitoring
Lesson 31: QRadar and Threat Hunting Automation
31.1. Identifying Repetitive Threat Hunting Tasks
31.2. Automating Data Collection and Enrichment for Hunts
31.3. Scripting AQL Queries for Scheduled Execution
31.4. Utilizing QRadar's API for Automated Hunting Workflows
31.5. Integrating with Automation Playbooks (e.g., Ansible, Python)
31.6. Orchestrating Hunting Activities Across Multiple Tools
31.7. Reporting on Automated Hunting Results
31.8. Maintaining and Updating Hunting Automation Scripts
31.9. Measuring the Efficiency of Automated Hunting
31.10. Future of AI and Automation in Threat Hunting
Lesson 32: Advanced QRadar Troubleshooting and Debugging
32.1. Utilizing QRadar ariel_query_database for Diagnostics
32.2. Analyzing QRadar Log Files for Errors
32.3. Troubleshooting Component Communication Issues
32.4. Diagnosing Database Performance Problems
32.5. Utilizing QRadar Support Tools
32.6. Network Troubleshooting for Event and Flow Sources
32.7. Debugging Custom Rules and Scripts
32.8. Identifying and Resolving Licensing Issues
32.9. Working with IBM Support for Complex Problems
32.10. Developing a Systematic Troubleshooting Methodology
Lesson 33: QRadar Performance Monitoring and Capacity Planning
33.1. Key Performance Indicators (KPIs) for QRadar
33.2. Monitoring Event and Flow Rates
33.3. Analyzing Component CPU, Memory, and Disk Usage
33.4. Utilizing QRadar's System Monitoring Tools
33.5. Capacity Planning Based on Data Growth
33.6. Predicting Future Resource Requirements
33.7. Optimizing Data Retention Policies
33.8. Performance Testing and Benchmarking
33.9. Scaling Strategies for Growing Environments
33.10. Cost Optimization of QRadar Deployment
Lesson 34: QRadar and Security Architecture Integration
34.1. Positioning QRadar within the Overall Security Stack
34.2. Integrating QRadar with Firewalls, IPS, and Proxies
34.3. Leveraging Security Device Logs for Enhanced Context
34.4. Integrating with Identity and Access Management (IAM) Systems
34.5. Utilizing QRadar for Security Control Validation
34.6. Sharing QRadar Insights with Other Security Tools
34.7. Designing a Security Architecture Around QRadar
34.8. Addressing Architectural Challenges in Large Enterprises
34.9. Future Trends in Security Architecture and SIEM
34.10. Case Studies of Integrated Security Architectures
Lesson 35: Expert-Level QRadar Command Line Interface (CLI) Usage
35.1. Advanced Navigation and Command Execution
35.2. Utilizing CLI for System Configuration
35.3. Troubleshooting Components via CLI
35.4. Managing Services and Processes
35.5. Scripting CLI Commands for Automation
35.6. Working with Configuration Files
35.7. Performing Database Operations via CLI
35.8. Monitoring System Health from the Command Line
35.9. Security Considerations for CLI Access
35.10. Best Practices for Expert CLI Usage
Lesson 36: QRadar Database (Ariel) Optimization
36.1. Understanding Ariel Database Architecture
36.2. Indexing Strategies for Faster Searches
36.3. Partitioning and Data Management
36.4. Monitoring Database Performance
36.5. Troubleshooting Database Issues
36.6. Optimizing Data Retention and Archiving
36.7. Utilizing Ariel Query Performance Tools
36.8. Impact of Data Volume on Database Performance
36.9. Best Practices for Ariel Database Maintenance
36.10. Advanced Data Modeling in Ariel
Lesson 37: Developing Advanced QRadar Use Cases
37.1. Identifying Complex Threat Scenarios
37.2. Translating Threat Scenarios into QRadar Rules
37.3. Developing Use Cases for Specific Attack Techniques
37.4. Utilizing Threat Intelligence in Use Case Development
37.5. Testing and Validating Custom Use Cases
37.6. Documenting and Sharing Developed Use Cases
37.7. Measuring the Effectiveness of Custom Use Cases
37.8. Maintaining and Updating Developed Use Cases
37.9. Collaborating on Use Case Development
37.10. Building a Use Case Development Framework
Lesson 38: QRadar and Enterprise Security Architecture
38.1. Integrating QRadar into Large-Scale Enterprise Environments
38.2. Addressing Challenges of Data Volume and Diversity
38.3. Distributed Deployments in Global Organizations
38.4. Centralized vs. Decentralized QRadar Management
38.5. Integrating with Global Security Operations Centers
38.6. Multi-Tenancy for Managed Security Service Providers (MSSPs)
38.7. Security Considerations for Enterprise Deployments
38.8. Performance Tuning for High-Throughput Environments
38.9. Disaster Recovery and Business Continuity Planning
38.10. Governance and Compliance in Enterprise QRadar Deployments
Lesson 39: Future Trends in SIEM and QRadar
39.1. The Evolution of SIEM and Security Analytics
39.2. Impact of Cloud and Hybrid Environments on SIEM
39.3. Role of AI and Machine Learning in Future SIEM
39.4. Automation and Orchestration Trends
39.5. Integration with Extended Detection and Response (XDR)
39.6. Threat Intelligence Sharing and Collaboration
39.7. Privacy and Regulatory Changes Affecting SIEM
39.8. The Future of Threat Hunting
39.9. Emerging Security Technologies and Their Impact on QRadar
39.10. Staying Ahead in the Cybersecurity Landscape
Lesson 40: QRadar Expert Certification Preparation and Capstone
40.1. Review of Key Advanced Concepts
40.2. Practice Questions and Exam Strategies
40.3. Hands-on Lab Scenarios Review
40.4. Deep Dive into Challenging Topics
45.5. Case Study Analysis and Application of Knowledge
40.6. Expert-Level Troubleshooting Scenarios
40.7. Designing and Presenting Advanced QRadar Solutions
40.8. Practical Application of Threat Hunting Methodologies
40.9. Q&A with Instructor and Peer Discussion
40.10. Course Wrap-up and Next Steps for Continued Learning