AI+ Security Level 3
The AI+ Security: Level 3 course provides a comprehensive exploration of the intersection between AI and cybersecurity, focusing on advanced topics critical to modern security engineering. It covers foundational concepts in AI and machine learning for security, delving into areas like threat detection, response mechanisms, and the use of deep learning for security applications. The course addresses the challenges of adversarial AI, network and endpoint security, and secure AI system engineering, along with emerging topics such as AI for cloud, container security, and blockchain integration. Key subjects also include AI in identity and access management (IAM), IoT security, and physical security systems, culminating in a hands-on capstone project that tasks learners with designing and engineering AI-driven security solutions.
All students receive:
- One-Year Subscription (with all updates)
- High-Quality E-Book
- Al Mentor for Personalized Guidance
- Quizzes, Assessments, and Course Resources
- Exam Study Guide
- Proctored Exam with one Free Retake
Check out our full list of training locations and learning formats. Please note that the location you choose may be an Established HD-ILT location with a virtual live instructor.
Train face-to-face with the live instructor.
Access to on-demand training content anytime, anywhere.
Attend the live class from the comfort of your home or office.
Interact with a live, remote instructor from a specialized, HD-equipped classroom near you. An SLI sales rep will confirm location availability prior to registration confirmation.
All Sunset Learning dates are guaranteed to run!
Register
Prerequisites:
Required:
- AI+ Security: Level 1
- AI+ Security: Level 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
Target Audience:
- Security Analyst
- Cybersecurity Specialist
- Security Consultant
Course Objectives:
- Gain proficiency in applying deep learning algorithms for advanced cyber defense applications, such as malware analysis, phishing detection, and predictive threat modeling.
- Develop expertise in integrating AI with cloud and container security, emphasizing scalable and automated threat mitigation for cloud-based platforms and containerized applications.
- Master the application of AI techniques to enhance identity and access management by streamlining identity verification, managing access control systems, and securing authentication processes.
- Explore the use of AI to secure IoT devices by addressing unique challenges, including detecting compromised devices and safeguarding communication protocols.
Course Outline:
Foundations of AI and Machine Learning for Security Engineering
- Core AI and ML Concepts for Security
- AI Use Cases in Cybersecurity
- Engineering AI Pipelines for Security
- Challenges in Applying AI to Security
Machine Learning for Threat Detection and Response
- Engineering Feature Extraction for Cybersecurity Datasets
- Supervised Learning for Threat Classification
- Unsupervised Learning for Anomaly Detection
- Engineering Real-Time Threat Detection Systems
Deep Learning for Security Applications
- Convolutional Neural Networks (CNNs) for Threat Detection
- Recurrent Neural Networks (RNNs) and LSTMs for Security
- Autoencoders for Anomaly Detection
- Adversarial Deep Learning in Security
Adversarial AI in Security
- Introduction to Adversarial AI Attacks
- Defense Mechanisms Against Adversarial Attacks
- Adversarial Testing and Red Teaming for AI Systems
- Engineering Robust AI Systems Against Adversarial AI
AI in Network Security
- AI-Powered Intrusion Detection Systems
- AI for Distributed Denial of Service (DDoS) Detection
- AI-Based Network Anomaly Detection
- Engineering Secure Network Architectures with AI