AI+ Security Level 3

Hours: 40 / Access Length: 12 Months / Delivery: Online, Self-Paced
Online Hours: 40
Retail Price: $495.00

Course Overview:

This course validates advanced-level expertise in AI-driven cybersecurity strategy, governance, and risk management. The exam assesses deep knowledge of advanced security architectures, AI-enabled threat intelligence, and strategic security decision-making within complex enterprise environments.

Recommended Prerequisites:

• Completion of AI+ Security Level 1 and 2
• Intermediate / Advanced Python Programming: Proficiency or expert in Python, including deep learning frameworks (TensorFlow, PyTorch).
• Intermediate Machine Learning Knowledge: Proficiency in understanding of deep learning, adversarial AI, and model training.
• Advanced Cybersecurity Knowledge: Proficiency in threat detection, incident response, and network/endpoint security.
• AI in Security Engineering: Knowledge of AI’s role in identity and access management (IAM), IoT security, and physical security.
• Cloud and Container Expertise: Understanding of cloud security, containerization, and blockchain technologies.
• Linux/CLI Mastery: Advanced command-line skills and experience with security tools in Linux environments.

Course Outline:

Lesson 1: Foundations of AI and Machine Learning for Security Engineering

• 1.1        Core AI and ML Concepts for Security
• 1.2        AI Use Cases in Cybersecurity
• 1.3        Engineering AI Pipelines for Security
• 1.4        Challenges in Applying AI to Security

Lesson 2: Machine Learning for Threat Detection and Response

• 2.1        Engineering Feature Extraction for Cybersecurity Datasets
• 2.2        Supervised Learning for Threat Classification
• 2.3        Unsupervised Learning for Anomaly Detection
• 2.4        Engineering Real-Time Threat Detection Systems

Lesson 3: Deep Learning for Security Applications

• 3.1        Convolutional Neural Networks (CNNs) for Threat Detection
• 3.2        Recurrent Neural Networks (RNNs) and LSTMs for Security
• 3.3        Autoencoders for Anomaly Detection
• 3.4        Adversarial Deep Learning in Security

Lesson 4: Adversarial AI in Security

• 4.1        Introduction to Adversarial AI Attacks
• 4.2        Defense Mechanisms Against Adversarial Attacks
• 4.3        Adversarial Testing and Red Teaming for AI Systems
• 4.4        Engineering Robust AI Systems Against Adversarial AI

Lesson 5: AI in Network Security

• 5.1        AI-Powered Intrusion Detection Systems (IDS)
• 5.2        AI for Distributed Denial of Service (DDoS) Detection
• 5.3        AI-Based Network Anomaly Detection
• 5.4        Engineering Secure Network Architectures with AI

Lesson 6: AI in Endpoint Security

• 6.1        AI for Malware Detection and Classification
• 6.2        AI for Endpoint Detection and Response (EDR)
• 6.3        AI-Driven Threat Hunting
• 6.4        AI for Securing Mobile and IoT Devices

Lesson 7: Secure AI System Engineering

• 7.1        Designing Secure AI Architectures
• 7.2        Cryptography in AI for Security
• 7.3        Ensuring Model Explainability and Transparency in Security
• 7.4        Performance Optimization of AI Security Systems

Lesson 8: AI for Cloud and Container Security

• 8.1        AI for Securing Cloud Environments
• 8.2        AI-Driven Container Security
• 8.3        AI for Securing Serverless Architectures
• 8.4        AI and DevSecOps

Lesson 9: AI and Blockchain for Security

• 9.1        Fundamentals of Blockchain and AI Integration
• 9.2        AI for Fraud Detection in Blockchain
• 9.3        Smart Contracts and AI Security
• 9.4        AI-Enhanced Consensus Algorithms

Lesson 10: AI in Identity and Access Management (IAM)

• 10.1     AI for User Behavior Analytics in IAM
• 10.2     AI for Multi-Factor Authentication (MFA)
• 10.3     AI for Zero-Trust Architecture
• 10.4     AI for Role-Based Access Control (RBAC)

Lesson 11: AI for Physical and IoT Security

• 11.1     AI for Securing Smart Cities
• 11.2     AI for Industrial IoT Security
• 11.3     AI for Autonomous Vehicle Security
• 11.4     AI for Securing Smart Homes and Consumer IoT

Lesson 12: Capstone Project - Engineering AI Security Systems

• 12.1     Defining the Capstone Project Problem
• 12.2     Engineering the AI Solution
• 12.3     Deploying and Monitoring the AI System
• 12.4     Final Capstone Presentation and Evaluation

All necessary course materials are included.

System Requirements:

Internet Connectivity Requirements:

• Cable, Fiber, DSL, or LEO Satellite (i.e. Starlink) internet with speeds of at least 10mb/sec download and 5mb/sec upload are recommended for the best experience.

NOTE: While cellular hotspots may allow access to our courses, users may experience connectivity issues by trying to access our learning management system.  This is due to the potential high download and upload latency of cellular connections.   Therefore, it is not recommended that students use a cellular hotspot as their primary way of accessing their courses.

Hardware Requirements:

• CPU: 1 GHz or higher
• RAM: 4 GB or higher
• Resolution: 1280 x 720 or higher.  1920x1080 resolution is recommended for the best experience.
• Speakers / Headphones
• Microphone for Webinar or Live Online sessions.

Operating System Requirements:

• Windows 7 or higher.
• Mac OSX 10 or higher.
• Latest Chrome OS
• Latest Linux Distributions
  

NOTE: While we understand that our courses can be viewed on Android and iPhone devices, we do not recommend the use of these devices for our courses. The size of these devices do not provide a good learning environment for students taking online or live online based courses.

Web Browser Requirements:

• Latest Google Chrome is recommended for the best experience.
• Latest Mozilla FireFox
• Latest Microsoft Edge
• Latest Apple Safari

Basic Software Requirements (These are recommendations of software to use):

• Office suite software (Microsoft Office, OpenOffice, or LibreOffice)
• PDF reader program (Adobe Reader, FoxIt)
• Courses may require other software that is described in the above course outline.