Overview
Explore how LSTM neural networks and Python can generate AI-driven test inputs for software testing and network security, improving upon traditional fuzzing techniques.
Syllabus
-
- Introduction to Fuzzing Techniques
-- Overview of Traditional Fuzzing Methods
-- Limitations and Challenges of Traditional Fuzzing
- Introduction to LSTM Neural Networks
-- Understanding Recurrent Neural Networks (RNNs)
-- Detailed Study of LSTMs: Architecture and Functionality
- Python for AI-Driven Fuzzing
-- Setting Up Python Environment for AI Development
-- Key Python Libraries: TensorFlow, Keras
- Implementing LSTMs for Test Input Generation
-- Preparing Datasets for LSTM Training
-- Training LSTMs on Existing Software Inputs
-- Generating AI-Driven Test Inputs with LSTMs
- Application of AI-Driven Fuzzing in Software Testing
-- Case Studies and Practical Examples
-- Evaluating the Effectiveness of AI-Generated Inputs
-- Comparing AI-Driven Fuzzing to Traditional Methods
- Enhancing Network Security with AI-Driven Fuzzing
-- Identifying Network Vulnerabilities with LSTM Models
-- Simulating Network Attacks Using Generated Inputs
- Advanced Topics and Future Directions
-- Improvements in AI Algorithms for Fuzzing
-- Combining AI-Driven Techniques with Other Testing Methods
- Practical Workshop
-- Step-by-Step Implementation of LSTM for Fuzzing
-- Hands-On Projects: Building and Testing AI-Driven Cases
- Course Wrap-Up and Assessments
-- Summary of Key Learnings
-- Final Project and Assessment Guidelines
Taught by
Tags
On-Demand
