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Module 1: Introduction to Artificial Intelligence (AI) and Cyber Security
Module Introduction to Artificial Intelligence and Cyber Security
Understanding CSAI
AI in Cybersecurity: Tools, Ethics, Future Collaboration
Introduction to Cybersecurity and Trends
AI’s Role in Modern Cybersecurity Solutions
The Need, Key Components, and Future of Adaptive Security
Roadmap and Applications of Cyber Security AI (CSAI)
Recent Trends and Strategies in Enhancing Digital Defense with CSAI
Module 1: E-Book
Summary: Artificial Intelligence and Cyber Security
Quiz
Module 2: Python Programming for AI and Cybersecurity Professionals
Introduction: Python Programming for AI and Cybersecurity Professionals
Introduction to Python and Key Learning Outcomes
Python Fundamentals and Its Critical Role in Cybersecurity with Key Libraries
Use of Python for Cybersecurity
Introduction to AI Scripting and Techniques
Sample Practical: Access Management using Python Scripting
Introduction to Data Analytics and Visualization in Cybersecurity
Sample Practical: Pandas Dataset for Visualization of Cyber Attacks
The Role of Python in Enhancing Cybersecurity Frameworks and Tools
Summary: Python Programming for AI and Cybersecurity Professionals
Module 2: E-Book
Quiz
Lab Practice 2.1
Lab Practice 2.2
Python File Download
Module 3: Application of Machine Learning in Cybersecurity
Module 3 Introduction Applications of Machine Learning in Cybersecurity
Introduction
Learning Outcomes
Overview of Applications of ML
Importance of Anomaly & Behavior Analysis
Software Approach for AI in Cybersecurity
Introduction to Dynamic and Proactive Defence
Dynamic Cybersecurity Defense with Machine Learning Insights
Securing Sensitive Data and Systems from Cyberattacks
The Key Solutions for Enhancement of Cybersecurity Posture
Futuristic Technology for Dynamic Cybersecurity ML Model
Cybersecurity Threats and Trends: A Proactive Approach
Summary Applications of Machine Learning in Cybersecurity
Module 3: E-Book
Quiz
Lab Practice 3.1
Python File Download
Module 4: Detection of Email Threats with Artificial Intelligence (AI)
Module 4: Introduction Detection of Email Threats with AI
AI and Machine Learning in Cybersecurity
Pattern Recognition , Threat Detection and Challenges in Email Analysis
Role of AI in identifying, mitigating and evolving phishing attacks
AI prevention, approaches of phishing detection, challenges and future
Automating Threat Detection and Response
Integrating Machine Learning with Email Security Protocols
Future Directions in Autonomous Email Threat Management
AI Tools and Platforms for Email Security
Summary Detection of Email Threats with AI
Module 4: E-Book
Quiz
Module 5: AI Algorithm for Malware Threat Detection
Introduction: AI Algorithm for Malware Threat Detection
Advances and Challenges in Malware Threat Detection
AI-Powered Malware Detection: Types, Advantages, and Model Development
Machine Learning and Deep Learning Techniques for Malware Detection
Feature Engineering and Representation Learning for Malware Analysis
Techniques for Malware Identification and Analysis
AI-Driven Behavioral Analysis for Real-Time Malware Mitigation
AI-Powered Malware Detection and Real-Time Threat Monitoring
Adaptable and Scalable Malware Defence Strategies
AI-Powered Threat Intelligence and Malware Defense
Python and Tools for Malware Analysis
Summary: AI Algorithm for Malware Threat Detection
Module 5: E-Book
Quiz
Module 6: Network Anomaly Detection using AI
Module intro: Network Anomaly Detection using AI
Learning Outcomes
Overview of Network Anomalies
Significance of Anomaly Detection for Network Security
AI-Powered Network Anomaly Detection Systems
Use Cases of Anomaly Detection System
AI-Enabled Firewall for Enhancing Network Security
Standard Operational Procedure of Implementing AI Model for Network Anomaly Detection
Practical Approach for AI in Network Anomaly Detection
Evaluate the Effectiveness of AI-based Network Security
Disadvantage of AI Powered Network Security: Zero Day Threat
Summary: Network Anomaly Detection using AI
Module 6: E-Book
Quiz
Lab Practice 6.1
Python File Download
Module 7: User Authentication Security with AI
Introduction: User Authentication Security with AI
Overview of User Authentication
Application of AI Technologies in Authentication
Advantages of Machine Learning and Neural Networks
AI-Enhanced Biometric Recognition Systems
Anomaly Detection through AI
Behavioural Analysis with AI
The Integration of Contextual and Risk-Based Authentication
Implementing Real-Time Threat Detection
Balancing User Convenience with Security
Adaptive Authentication Technologies
Overview of Current AI-Based Platform
Future Trends in AI Authentication Technologies
Summary of Key Points
Summary: User Authentication Security with AI
Module 7: E-Book
Quiz
Module 8: Generative Adversarial Network (GAN) for Cyber Security
Introduction: Generative Adversarial Network (GAN) for Cyber Security
Overview of GANs and their Basic Principles
Importance of GANs in the Cybersecurity Landscape
Advantages of using GANs in Cybersecurity
How GANs Generate Simulated Cyber Threats for Training Purposes
Benefits of Using Simulated Attacks to Enhance Security Protocols
Generating Synthetic Malware Samples
Simulating Network Attacks and Intrusions
Mimicking Adversarial Behaviour and Tactics
Techniques for Vulnerability Detection using GANs
Case Studies of GANs Identifying and Mitigating Security Flaws
Identifying Software Vulnerabilities
Enhancing Intrusion Detection Systems
Adversarial Training for Robust Security Models
Overview of Technical Tools Used to Implement GANs in Cybersecurity
Discussion of Popular Python Libraries and Frameworks for GAN Development
Popular GAN Frameworks
Implementing GANs in Python
Summary: Generative Adversarial Network (GAN) for Cyber Security
Module 8: E-Book
Quiz
Lab Practice 8.1
Python File Download
Module 9: Penetration Testing with Artificial Intelligence
Module intro: Penetration Testing with Artificial Intelligence
Machine Learning Techniques for Vulnerability Discovery
AI-Assisted Static and Dynamic Code Analysis for Vulnerability Mapping
Intelligent Fuzzing Techniques Using AI
Anomaly Detection and Vulnerability Identification
Prioritization of Vulnerabilities
AI in Real-Time Threat Monitoring
Self-Learning Systems for Adaptive Security
AI Integration with SIEM
AI-Powered Penetration Testing Methodologies
Simulation of Sophisticated Cyber Attacks
Automated Exploitation and Post-exploitation Analysis
Continuous Security Validation and Hardening
Overview of AI-based Penetration Testing Tools
AI-based Vulnerability Scanners
Summary: Penetration Testing with Artificial Intelligence
Module 9: E-Book
Quiz
Lab Practice 9.1
Python File Download
Module 10: Capstone Project
Module Introduction to Capstone Project
Anomaly Detection in Credit Card Transactions
Summary: Introduction to Capstone Project
Module 10: E-Book
Resources
AI+ Security Tools
AI+ Security Blueprint
AI+ Security Detailed Curriculum
AI+ Security Resources and References
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