Curriculum

AI for Cyber Security with IBM QRadar

Module 1: Introduction To CEH
  • Definition and scope of ethical hacking
  • Legal and ethical considerations in ethical hacking
  • Role of an ethical hacker in an organization

Module 2: Introduction to Networking
  • OSI Model,
  • TCP/IP Model,
  • Ports, Protocols,
  • Design a Subnet,
  • Networking commands (Linux/Windows)
  • Network Configuring using Cisco
  • Packet Tracer

Module 3: Introduction to Linux
  • Basic Linux commands and navigation
  • File system hierarchy and permissions
  • Package management and software installation

Module 4: Vulnerability Analysis
  • Vulnerability assessment concepts and tools
    • Identifying vulnerabilities in systems and networks
    • Risk assessment and prioritization of vulnerabilities

Module 5: Social Engineering
  • Types of social engineering attacks
    • Human behavior and psychology in social engineering attacks
    • Countermeasures for social engineering attacks

Module 6: Hacking Web Applications
  • Web application vulnerabilities and attacks
  • Web application protection measures
  • Testing and securing web applications

Module 7: Web Application Security Testing and Vulnerability Assessment
  • Exploring different types of security testing (e.g., penetration testing, vulnerability scanning)
  • Understanding the importance of vulnerability assessment in web security
  • Using Python tools for automated vulnerability scanning and assessment
  • Analyzing and interpreting

Module 8: OSNIT
  • OSNIT framework
  • How to use OSNIT framework

Module 9: Introduction to Web Security and Python Fundamentals
  • Introduction to Natural Language Processing and its applications
  • Understanding the basics of text preprocessing and tokenization
  • Leveraging Python libraries such as NLTK and spaCy for NLP tasks
  • Exploring Open Ai’s GPT models for text generation and completion
  • Building NLP models for sentiment analysis, named entity recognition, and text classification using Python and OpenAI

Module 10: Natural Language Processing (NLP) with Python and Open AI
  • Introduction to Natural Language Processing and its applications
  • Understanding the basics of text preprocessing and tokenization
  • Leveraging Python libraries such as NLTK and spaCy for NLP tasks
  • Exploring Open Ai’s GPT models for text generation and completion
  • Building NLP models for sentiment analysis, named entity recognition, and text classification using Python and OpenAI

Module 11: Machine Learning for Cybersecurity with Python and OpenAI
  • Introduction to machine learning algorithms and their role in cybersecurity
  • Preparing data for machine learning tasks in the cybersecurity domain
  • Building machine learning models for anomaly detection and intrusion detection using Python and scikit-learn
  • Exploring the use of reinforcement learning for cybersecurity tasks
  • Integrating OpenAI's reinforcement learning models for optimizing cybersecurity defenses

Module 12: Python for Web Security
  • Leveraging Python for web scraping and data collection
  • Automating security-related tasks with Python
  • Using Python libraries for web scanning and reconnaissance

Module 13: Introduction to OpenAI and GPT 
  • Introduction to OpenAI and its applications
  • Understanding the GPT (Generative Pre-trained Transformer) model
  • Exploring the capabilities and limitations of OpenAI's GPT-3

Module 14: Introduction to Cyber Threat Intelligence (CTI)
  • Overview of CTI and its importance in cybersecurity
  • Types of CTI and their impact on organizations
  • CTI sources and analysis techniques
  • Common tools and platforms used in CTI

Module 15: Python Hacking Analytics
  • Python for Cybersecurity:
    • Overview of using Python in the realm of cybersecurity.
    • Application of Python scripting for penetration testing and vulnerability analysis.
    • Hacking Techniques with Python:
    • Exploration of Python-based hacking tools and frameworks.
    • Demonstrations of common hacking techniques implemented using Python scripts.
  • Analytics in Cybersecurity:
    • Utilizing Python for data analysis in cybersecurity.
    • Applying statistical and machine learning techniques for threat detection and anomaly identification.
Module 16: Python Seaborn Visualization
  • Introduction to Seaborn:
    • Overview of Seaborn as a Python data visualization library.
    • Basic functionalities and features offered by Seaborn for creating visually appealing plots.
  • Statistical Data Visualization:
    • Exploration of Seaborn's capabilities in creating statistical plots.
    • Application of Seaborn for visualizing relationships, distributions, and correlations in datasets.
  • Customization and Styling:
    • Techniques for customizing and styling Seaborn visualizations.
    • Adjustment of color palettes, themes, and other aesthetic aspects to enhance the visual appeal of plots.

Module 17: QRadar Administration
  • Configuring data sources and log sources
  • Setting up reference data, rules, and policies

Module 18: QRadar Log Management
  • Overview of log management and storage concepts
  • Managing log sources, events, and flows in QRadar
  • Configuring custom log sources
  • Setting up log source extensions and event collectors in QRadar

Module 19: QRadar Reporting and Dashboarding
  • Creating custom reports and dashboards in QRadar
  • Understanding QRadar report types and formats
  • Using QRadar visualizations and dashboards for threat analysis
  • Best practices for QRadar reporting and dashboarding

Module 20: QRadar Automation and Orchestration
  • Overview of automation and orchestration in QRadar
  • Creating custom scripts and workflows in QRadar
  • Integrating QRadar with automation and orchestration tools
  • Best practices for automation and orchestration in QRadar.