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AI+ Security Level 3™ Spanish
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Introduction
Course Introduction
Module 1: Foundations of AI and Machine Learning for Security Engineering
1.1 Mathematical Foundations for AI in Security
1.2 Machine Learning Techniques in Cybersecurity
1.3 Core AI Algorithms for Security
1.4 NLP for Cybersecurity Applications
1.5 Deep Learning for Security Log Analysis
1.6 AI in Intrusion detection and Prevention System
1.7 AI for Malware and Ransomware Detection
1.8 Network Traffic Analysis with AI
1.9 Behavioral Analytics for Insider Threat Detection
1.10 Automating Threat Intelligence with Machine Learning
1.11 Data Ingestion and Preprocessing for Cybersecurity
1.12 Feature Engineering for Anomaly Detection
1.13 Engineering AI Pipelines for Cybersecurity
1.14 Secure Storage and Encryption of Datasets
1.15 Continuous Model Retraining for Evolving Threats
1.16 Complexity of Feature Extraction in Cybersecurity
1.17 Managing Imbalanced Security Datasets
1.18 Limitations of Traditional AI models in Security
1.19 Adversarial Attacks on AI Systems
1.20 Mitigating False Positives and Negatives in Threat Detection
Module 2: Machine Learning for Threat Detection and Response
2.1 Extracting Network-Level Features in Cybersecurity
2.2 Behavioral Analysis and NLP for Threat Detection
2.3 Dynamic Feature Engineering in Real-Time Data Streams
2.4 Training Supervised Models for Malware Classification
2.5 Enhancing Detection Accuracy with Ensemble Learning for Malicious Traffic
2.6 Enhancing Cybersecurity Through Supervised Learning Challenges
2.7 Clustering Algorithms for Detecting Abnormal Network Behavior
2.8 Engineering Pipelines for Real-Time Anomaly Detection
2.9 Streaming Data Pipelines for Real-Time Threat Detection
2.10 Real-Time Intrusion Detection with AI and Spark
Module 3: Deep Learning for Security Applications
3.1 Convolutional Neural Networks for Security Applications
3.2 Optimizing CNNs for Malware Detection and Network Traffic Classification
3.3 CNNs for Encrypted Traffic Analysis with Case Study
3.4 Decoding RNNs and LSTMs for Time-Series Anomaly Detection
3.5 RNNs for Brute Force and DDoS Attack Detection
3.6 Sequence for Modeling for Phishing Campaign Detection
3.7 Hybrid Model Combining CNNs and RNNs
3.8 Detecting Zero-Day Exploits with Autoencoders
3.9 Variational Autoencoders for Enhanced Anomaly Detection with Case Study
3.10 Fortifying Deep Learning Models Against Adversarial Attacks
3.11 Detect and Defend: Navigating Gradient-Based Attacks
3.12 Engineering Secure Deep Learning Architecture
Module 4: Adversarial AI in Security
4.1 Understanding Gradient-Based Adversarial Attacks
4.2 Decoding Data Poisoning: Evasion Tactics in Malware Detection
4.3 Tools for AI Attacks Simulation
4.4 Adversarial Training for Model Robustness
4.5 Gradient Masking and Input Transformations
4.6 Defensive Distillation for Hardening AI Models
4.7 Ensemble Methods for Enhanced Resilience with Case Study
4.8 Red Teaming Tools for Adversarial Testing
4.9 Simulating AI Vulnerabilities with GAN-Driven Adversarial Scenarios with Case Study
4.10 Designing Robust AI Systems with Redundancy and Diversity
4.11 Enhancing AI Trust with Secure Enclave and Explainability Techniques
4.12 Ensuring Model Security Through Cryptographic Methods
Module 5: AI in Network Security
5.1 AI-Powered Architectures for Intelligent Intrusion Detection Systems
5.2 Integrating AI into Network Traffic Monitoring Tools with Case Study
5.3 Adaptive Techniques for DDoS Detection and Mitigation with a Case Study
5.4 Performance Optimization of AI Models in High-Speed Networks
5.5 Feature Engineering for Anomaly Detection in Network Security
5.6 Implementing Unsupervised Learning for Anomaly Detection
5.7 Unmasking Threats: Detecting Lateral Movements in Networks
5.8 Integrating AI into Secure Software-Defined Networking (SDN)
5.9 AI-Driven Virtualized Network Function Design
5.10 Case Study: AI-Based Zero-Trust Network Access Control
5.11 Performance Tuning of AI Models for High Throughput Networks
Module 6: AI in Endpoint Security
6.1 AI-Driven Malware Detection Through Dynamic and Static Analysis
6.2 Engineering AI Systems for Fileless Malware Detection
6.3 AI-Powered Defense: Tackling Polymorphic and Metamorphic Malware
6.4 Building AI-Driven EDR Systems
6.5 AI-Powered Threat Detection and Anomaly Analysis
6.6 Case-Study: Implementing an AI Driven Endpoint Security Platform
6.7 AI Integration with Existing Security Solutions
6.8 AI-Powered Automation in Threat Hunting
6.9 Leveraging AI to Detect Dormant Malware and APTs
6.10 Reinforcement Learning for Active Threat Hunting
6.11 Efficient AI Models for Resource-Constrained Devices
6.12 Engineering AI for IoT Security in Smart Cities
6.13 Case Study: Engineering AI for Smart City IoT Protection
Module 7: Secure AI System Engineering
7.1 Architectural Best Practices for Secure AI Systems
7.2 Integrating encryption mechanism in AI pipelines
7.3 Building Resilient AI Systems for Financial Security
7.4 Leveraging Cryptographic Techniques in AI Models
7.5 Securing ML Models Against Inference Attacks
7.6 Federated Learning for Privacy: A Secure Cryptographic Healthcare Case Study
7.7 Exploring Post-Quantum Cryptography in AI Security
7.8 Auditing AI Decision-Making in Cybersecurity with a Case Study
7.9 Engineering Transparent Models for Trust in AI Security
7.10 Engineering Efficient AI Models for Real Time Security
7.11 Optimizing AI for Scalability and Latency
7.12 Hardware acceleration of AI Security Models
7.13 Optimizing Real-Time Fraud Detection: Model Pruning and Quantization Technique
Module 8: AI for Cloud and Container Security
8.1 AI-Driven Solutions for Cloud Security: Detecting Misconfigurations and Anomalies with a Case Study
8.2 Hardening AI Systems Against Cloud-Based Threats
8.3 Detecting Container Runtime Anomalies Using AI Models
8.4 AI-Based Vulnerability Scanning for Container Images
8.5 AI-Driven Threat Detection in Kubernetes Microservices with a Case Study
8.6 Leveraging AI to Detect Unauthorized Container Access
8.7 AI-Driven Threat Detection in Serverless Applications with a Case Study
8.8 Challenges and Best Practices in Securing Serverless Function
8.9 AI Models for Detecting Misconfigurations in Serverless Applications
8.10 Enhancing DevSecOps Pipelines with AI-driven Security Testing
8.11 AI-Driven DevSecOps: Enhancing Continuous Delivery
8.12 AI for Identifying Security Flaws in Code
Module 9: AI and Blockchain for Security
9.1 Introduction to Blockchain Technology and Security Properties
9.2 Engineering Blockchain-Based Solutions for Decentralized Security
9.3 Integrating AI with Blockchain for Secure Data Management
9.4 Case Study: AI and Blockchain: Transforming Secure Identity Management
9.5 Cryptographic Techniques for Ensuring Blockchain Data Integrity
9.6 Engineering AI Models for Real-Time Fraud Detection in Blockchain Transactions with Case Study
9.7 Case Study: AI-Based Fraud Detection in Cryptocurrency Exchanges
9.8 AI-Based Fraud Detection in Cryptocurrency Exchanges
9.9 Securing Smart Contracts with AI-Based Vulnerability Detection
9.10 Engineering AI Systems for Malicious Smart Contract Detection
9.11 AI-Powered Smart Contract Auditing in DeFi Applications with a Case Study
9.12 Ensuring Robustness of AI Models in Blockchain Systems
9.13 Using AI to Optimize Consensus Algorithm
9.14 AI-Enhanced Proof of Stake: Revolutionizing Consensus Mechanism
9.15 AI-Enhanced Proof of Stake: Engineering Secure Systems
Module 10: AI in Identity and Access Management (IAM)
10.1 AI-Based Monitoring for Threat Detection with a Case Study
10.2 Leveraging AI for Detecting Privilege Escalations and Adaptive Identity Management
10.3 Engineering AI Models for Biometric Authentications
10.4 AI Enhanced MFA for Risk-Based Network Security
10.5 Enhancing Passwordless Authentication with AI Detection of MFA Circumvention
10.6 AI-Driven Zero Trust Implementation in Identity and Access Management
10.7 AI-Powered Continuous Monitoring in Zero-Trust Architecture for Enterprise Networks
10.8 Challenges and Best Practices in Deploying AI in Zero-Trust Environment
10.9 Using AI to Automatically Assign and Manage Roles
10.10 AI Based Optimization of Access Policies and Privileges
10.11 AI-Driven Role-Based Access Management in Healthcare
10.12 Engineering Adaptive RBAC Systems with AI
Module 11: AI for Physical and IoT Security
11.1 Engineering AI System for Cyber Threat Detection in Smart Cities
11.2 Using AI to Monitor an Protect Critical Infrastructure
11.3 AI-Driven Security for IoT Device Tampering in Smart Cities
11.4 Leveraging AI to Ensure Privacy and Security in public IoT Deployments
11.5 Using AI to Detect Anomalies in ICS and SCADA Networks
11.6 AI-Driven Security for IIoT in Manufacturing
11.7 AI-Based Solutions for Securing Operational Technology (OT) Networks
11.8 Federated Learning for Securing Decentralized Industrial IoT Systems
11.9 Engineering AI Systems to Detect Cyber Threats in Autonomous Vehicles
11.10 AI-Driven Security in Autonomous Vehicular Communication
11.11 AI-Driven Security: Protecting Vehicle Systems and User Privacy
11.12 AI-Powered Security and Monitoring for Smart Homes
11.13 AI-Powered Threat Detection for Smart Home IoT Security
11.14 Implementing Federated Learning for Decentralized Smart Home Security Solutions
Module 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
Course Summary
Course Summary
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AI+ Security Level 3™ Spanish
