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AI+ Security Level 2™ Self-Paced Learning
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Lab Instructions
Module 1: Introduction to Artificial Intelligence (AI) and Cyber Security
1.0 Introduction to Artificial Intelligence and Cyber Security
1.1 Understanding CSAI
1.2 AI in Cybersecurity: Tools, Ethics, Future Collaboration
1.3 Introduction to Cybersecurity and Trends
1.4 AI’s Role in Modern Cybersecurity Solutions
1.5 The Need, Key Components, and Future of Adaptive Security
1.6 Roadmap and Applications of Cyber Security AI (CSAI)
1.7 Recent Trends and Strategies in Enhancing Digital Defense with CSAI
1.8 Summary: Artificial Intelligence and Cyber Security
Module 1: Ebook
Quiz
Module 2: Python Programming for AI and Cybersecurity Professionals
2.0 Introduction Python Programming for AI and Cybersecurity Professionals
2.1 Introduction to Python and Key Learning Outcomes
2.2 Python Fundamentals and Its Critical Role in Cybersecurity with Key Libraries
2.3 Use of Python for Cybersecurity
2.4 Introduction to AI Scripting and Techniques
2.5 Sample Practical: Access Management using Python Scripting
2.6 Introduction to Data Analytics and Visualization in Cybersecurity
2.7 Sample Practical: Pandas Dataset for Visualization of Cyber Attacks
2.8 The Role of Python in Enhancing Cybersecurity Frameworks and Tools
2.9 Summary Python Programming for AI and Cybersecurity Professionals
Module 2: Ebook
Quiz
Lab Practice 2.1
Lab Practice 2.2
Lab Practice 2.3
Python File Download
Module 3: Application of Machine Learning in Cybersecurity
3.0 Introduction Applications of Machine Learning in Cybersecurity
3.1 Introduction
3.2 Learning Outcomes
3.3 Overview of Applications of ML
3.4 Importance of Anomaly & Behavior Analysis
3.5 Software Approach for AI in Cybersecurity
3.6 Introduction to Dynamic and Proactive Defence
3.7 Dynamic Cybersecurity Defense with Machine Learning Insights
3.8 Securing Sensitive Data and Systems from Cyberattacks
3.9 The Key Solutions for Enhancement of Cybersecurity Posture
3.10 Futuristic Technology for Dynamic Cybersecurity ML Model
3.11 Cybersecurity Threats and Trends: A Proactive Approach
3.12 Summary Applications of Machine Learning in Cybersecurity
Module 3: Ebook
Quiz
Lab Practice 3.1
Lab Practice 3.2
Lab Practice 3.3
Python File Download
Module 4: Detection of Email Threats with Artificial Intelligence (AI)
4.0 Introduction Detection of Email Threats with AI
4.1 AI and Machine Learning in Cybersecurity
4.2 Pattern Recognition , Threat Detection and Challenges in Email Analysis
4.3 Role of AI in identifying, mitigating and evolving phishing attacks
4.4 AI prevention, approaches of phishing detection, challenges and future
4.5 Automating Threat Detection and Response
4.6 Integrating Machine Learning with Email Security Protocols
4.7 Future Directions in Autonomous Email Threat Management
4.8 AI Tools and Platforms for Email Security
4.9 Summary Detection of Email Threats with AI
Module 4: Ebook
Quiz
Lab Practice 4.1
Lab Practice 4.2
Python File Download
Module 5: AI Algorithm for Malware Threat Detection
5.0 Introduction AI Algorithm for Malware Threat Detection
5.1 Advances and Challenges in Malware Threat Detection
5.2 AI-Powered Malware Detection: Types, Advantages, and Model Development
5.3 Machine Learning and Deep Learning Techniques for Malware Detection
5.4 Feature Engineering and Representation Learning for Malware Analysis
5.5 Techniques for Malware Identification and Analysis
5.6 AI-Driven Behavioral Analysis for Real-Time Malware Mitigation
5.7 AI-Powered Malware Detection and Real-Time Threat Monitoring
5.8 Adaptable and Scalable Malware Defence Strategies
5.9 AI-Powered Threat Intelligence and Malware Defense
5.10 Python and Tools for Malware Analysis
5.11 Summary AI Algorithm for Malware Threat Detection
Module 5: Ebook
Quiz
Quiz
Lab Practice 5.1
Python File Download
Module 6: Network Anomaly Detection using AI
6.0 Module intro: Network Anomaly Detection using AI
6.1 learning outcomes
6.2 Overview of Network Anomalies
6.3 Significance of Anomaly Detection for Network Security
6.4 AI-Powered Network Anomaly Detection Systems
6.5 Use Cases of Anomaly Detection System
6.6 AI-Enabled Firewall for Enhancing Network Security
6.7 Standard Operational Procedure of Implementing AI Model for Network Anomaly Detection
6.8 Practical Approach for AI in Network Anomaly Detection
6.9 Evaluate the Effectiveness of AI-based Network Security
6.10 Disadvantage of AI Powered Network Security: Zero Day Threat
6.11 Summary: Network Anomaly Detection using AI
Module 6: Ebook
Quiz
Quiz
Lab Practice 6.1
Lab Practice 6.2
Python File Download
Module 7: User Authentication Security with AI
7.0 Introduction: User Authentication Security with AI
7.1 Overview of User Authentication
7.2 Application of AI Technologies in Authentication
7.3 Advantages of Machine Learning and Neural Networks
7.4 AI-Enhanced Biometric Recognition Systems
7.5 Anomaly Detection through AI
7.6 Behavioural Analysis with AI
7.7 The Integration of Contextual and Risk-Based Authentication
7.8 Implementing Real-Time Threat Detection
7.9 Balancing User Convenience with Security
7.10 Adaptive Authentication Technologies
7.11 Overview of Current AI-Based Platform
7.12 Future Trends in AI Authentication Technologies
7.13 Summary of Key Points
7.14 Summary User Authentication Security with AI
Module 7: Ebook
Quiz
Quiz
Lab Practice 7.1
Lab Practice 7.2
Python File Download
Module 8: Generative Adversarial Network (GAN) for Cyber Security
8.0 Introduction Generative Adversarial Network (GAN) for Cyber Security
8.1 Overview of GANs and their Basic Principles
8.2 Importance of GANs in the Cybersecurity Landscape
8.3 Advantages of using GANs in Cybersecurity
8.4 How GANs Generate Simulated Cyber Threats for Training Purposes
8.5 Benefits of Using Simulated Attacks to Enhance Security Protocols
8.6 Generating Synthetic Malware Samples
8.7 Simulating Network Attacks and Intrusions
8.8 Mimicking Adversarial Behaviour and Tactics
8.9 Techniques for Vulnerability Detection using GANs
8.10 Case Studies of GANs Identifying and Mitigating Security Flaws
8.11 Identifying Software Vulnerabilities
8.12 Enhancing Intrusion Detection Systems
8.13 Adversarial Training for Robust Security Models
8.14 Overview of Technical Tools Used to Implement GANs in Cybersecurity
8.15 Discussion of Popular Python Libraries and Frameworks for GAN Development
8.16 Popular GAN Frameworks
8.17 Implementing GANs in Python
8.18 Summary Generative Adversarial Network (GAN) for Cyber Security
Module 8: Ebook
Quiz
Quiz
Lab Practice 8.1
Lab Practice 8.2
Python File Download
Module 9: Penetration Testing with Artificial Intelligence
9.0 Module intro Penetration Testing with Artificial Intelligence
9.1 Machine Learning Techniques for Vulnerability Discovery
9.2 AI-Assisted Static and Dynamic Code Analysis for Vulnerability Mapping
9.3 Intelligent Fuzzing Techniques Using AI
9.4 Anomaly Detection and Vulnerability Identification
9.5 Prioritization of Vulnerabilities
9.6 AI in Real-Time Threat Monitoring
9.7 Self-Learning Systems for Adaptive Security
9.8 AI Integration with SIEM
9.9 AI-Powered Penetration Testing Methodologies
9.10 Simulation of Sophisticated Cyber Attacks
9.11 Automated Exploitation and Post-exploitation Analysis
9.12 Continuous Security Validation and Hardening
9.13 Overview of AI-based Penetration Testing Tools
9.14 AI-based Vulnerability Scanners
9.15 Summary Penetration Testing with Artificial Intelligence
Module 9: Ebook
Quiz
Quiz
Lab Practice 9.1
Lab Practice 9.2
Python File Download
Module 10: Capstone Project
10.0 Module Introduction to Capstone Project
10.1 Anomaly Detection in Credit Card Transactions
10.2 Summary: Introduction to Capstone Project
Module 10: Ebook
Lab Practice 10.1
Lab Practice 10.2
Python File Download
Resources
AI+ Security Tools
AI+ Security Blueprint
AI+ Security Detailed Curriculum
AI+ Security Resources and References
AI CERTs Exam Guidelines
AI CERTs Exam Guidelines
AI+ Security Level 2 Examination
AI+ Security Level 2 Examination
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AI+ Security Level 2™ Self-Paced Learning
