Curriculum

Externship Program - 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: Leveraging OpenAI GPT for Web Security
Integrating OpenAI GPT into web security workflows
Building AI-powered threat intelligence systems
Using GPT for detecting and mitigating web vulnerabilities
Analyzing and interpreting the results generated by GPT in web security context

Module 15: Google BARD (BERT for Adversarial Robustness and Defense)
Introduction to Google BARD and its role in web security
Understanding BERT (Bidirectional Encoder Representations from Transformers)
Leveraging Google BARD for detecting and defending against web attacks


Module 16: Python and OpenAI Integration
Exploring OpenAI API and Python integration
Accessing and utilizing OpenAI API for web security tasks
Preprocessing data for OpenAI API consumption
Managing API requests and responses in Python


Module 17: Building a Source Code Analysis Tool using OpenAI API and Python
Designing the architecture of the source code analysis tool
Implementing code analysis algorithms and techniques
Integrating OpenAI API for vulnerability detection in source code
Developing a user-friendly interface for the tool


Module 18: Testing and Debugging the Source Code Analysis Tool
Implementing testing methodologies for the analysis tool
Debugging and resolving issues in the tool
Validating the accuracy and effectiveness of vulnerability detection


Module 19: Deployment and Maintenance 
Preparing the source code analysis tool for deployment
Deploying the tool in a web security environment
Monitoring and maintaining the tool for continuous improvement


Module 20: Real-World Use Cases and Best Practices
Exploring real-world applications of Python and OpenAI in web security
Case studies on using AI for web vulnerability detection
Future trends and advancements in Python, OpenAI, and web security


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