Visão Geral

Curso Machine Learning Security, ensina aos participantes os fundamentos da segurança cibernética, incluindo ameaças, riscos e vulnerabilidades. Os alunos aprendem sobre os diferentes tipos de modelos de aprendizado de máquina e como eles podem ser usados ​​para proteger seus sistemas.

Objetivo

Após realizar este Curso Machine Learning Security você será capa de:

• Entenda os fundamentos da segurança cibernética
• Identifique diferentes tipos de modelos de aprendizado de máquina
• Entenda as diferentes maneiras como o aprendizado de máquina pode ser usado na segurança cibernética
• Aplique os conceitos a problemas do mundo real

Pre-Requisitos

• Os alunos devem ser desenvolvedores Python trabalhando em sistemas de aprendizado de máquina.

Materiais

Conteúdo Programatico

• Cyber Security Basics
  • What is security?
  • Threat and risk
  • Cyber security threat types
  • Consequences of insecure software
    • Constraints and the market
    • The dark side
  • Categorization of bugs
    • The Seven Pernicious Kingdoms
    • Common Weakness Enumeration (CWE)
    • CWE Top 25 Most Dangerous Software Errors
    • Vulnerabilities in the environment and dependencies
• Cyber Security in Machine Learning
  • ML-specific cyber security considerations
  • What makes machine learning a valuable target?
  • Possible consequences
  • Inadvertent AI failures
  • Some real-world abuse examples
  • ML threat model
    • Creating a threat model for machine learning
    • Machine learning assets
    • Security requirements
    • Attack surface
    • Attacker model – resources, capabilities, goals
    • Confidentiality threats
    • Integrity threats (model)
    • Integrity threats (data, software)
    • Availability threats
    • Dealing with AI/ML threats in software security
• Using ML in Cyber Security
  • Static code analysis and ML
  • ML in fuzz testing
  • ML in anomaly detection and network security
  • Limitations of ML in security
• Malicious Use of AI and ML
  • Social engineering attacks and media manipulation
  • Vulnerability exploitation
  • Malware automation
  • Endpoint security evasion
• Adversarial Machine Learning
  • Threats against machine learning
  • Attacks against machine learning integrity
    • Poisoning attacks
    • Poisoning attacks against supervised learning
    • Poisoning attacks against unsupervised and reinforcement learning
    • Evasion attacks
    • Common white-box evasion attack algorithms
    • Common black-box evasion attack algorithm
    • Transferability of poisoning and evasion attacks
  • Some defense techniques against adversarial samples
    • Adversarial training
    • Defensive distillation
    • Gradient masking
    • Feature squeezing
    • Using reformers on adversarial data
    • Caveats about the efficacy of current adversarial defenses
    • Simple practical defenses
  • Attacks against machine learning confidentiality
    • Model extraction attacks
    • Defending against model extraction attacks
    • Model inversion attacks
    • Defending against model inversion attacks
• Denial of Service
  • Denial of Service
  • Resource exhaustion
  • Cash overflow
  • Flooding
  • Algorithm complexity issues
  • Denial of service in ML
    • Accuracy reduction attacks
    • Denial-of-information attacks
    • Catastrophic forgetting in neural networks
    • Resource exhaustion attacks against ML
    • Best practices for protecting availability in ML systems
• Input Validation Principles
  • Blacklists and whitelists
  • Data validation techniques
  • What to validate – the attack surface
  • Where to validate – defense in depth
  • How to validate – validation vs transformations
  • Output sanitization
  • Encoding challenges
  • Validation with regex
  • Regular expression denial of service (ReDoS)
  • Dealing with ReDoS
• Injection
  • Injection principles
  • Injection attacks
  • SQL injection
  • SQL injection basics
  • Attack techniques
  • Content-based blind SQL injection
  • Time-based blind SQL injection
  • SQL injection best practices
  • Input validation
  • Parameterized queries
  • Additional considerations
  • SQL injection and ORM
  • Code injection
    • Code injection via input()
    • OS command injection
  • General protection best practices
• Integer Handling Problems
  • Representing signed numbers
  • Integer visualization
  • Integers in Python
  • Integer overflow
  • Integer overflow with ctypes and NumPy
  • Other numeric problems
• Files and Streams
  • Path traversal
  • Path traversal-related examples
  • Additional challenges in Windows
  • Virtual resources
  • Path traversal best practices
  • Format string issues
• Unsafe Native Code
  • Native code dependence
  • Best practices for dealing with native code
• Input Validation in Machine Learning
  • Misleading the machine learning mechanism
  • Sanitizing data against poisoning and RONI
  • Code vulnerabilities causing evasion, misprediction, or misclustering
  • Typical ML input formats and their security
• Security Features
  • Authentication
    • Authentication basics
    • Multi-factor authentication
    • Authentication weaknesses - spoofing
    • Password management
  • Information exposure
    • Exposure through extracted data and aggregation
    • Privacy violation
    • System information leakage
    • Information exposure best practices
• Time and State
  • Race conditions
    • File race condition
    • Avoiding race conditions in Python
  • Mutual exclusion and locking
    • Deadlocks
  • Synchronization and thread safety
• Errors
  • Error handling
    • Returning a misleading status code
    • Information exposure through error reporting
  • Exception handling
    • In the except, catch block. And now what?
    • Empty catch block
    • The danger of assert statements
• Using Vulnerable Components
  • Assessing the environment
  • Hardening
  • Malicious packages in Python
  • Vulnerability management
    • Patch management
    • Vulnerability management
    • Bug bounty programs
    • Vulnerability databases
    • Vulnerability rating – CVSS
    • DevOps, the build process and CI / CD
    • Dependency checking in Python
  • ML Supply Chain Risks
    • Common ML system architectures
    • ML system architecture and the attack surface
    • Protecting data in transit – transport layer security
    • Protecting data in use – homomorphic encryption
    • Protecting data in use – differential privacy
    • Protecting data in use – multi-party computation
  • ML frameworks and security
    • General security concerns about ML platforms
    • TensorFlow security issues and vulnerabilities
• Cryptography for Developers
  • Cryptography basics
  • Cryptography in Python
  • Elementary algorithms
    • Random number generation
    • Hashing
  • Confidentiality protection
    • Symmetric encryption
  • Homomorphic encryption
    • Basics of homomorphic encryption
    • Types of homomorphic encryption
    • FHE in machine learning
  • Integrity protection
    • Message Authentication Code (MAC)
    • Digital signature
  • Public Key Infrastructure (PKI)
    • Some further key management challenges
    • Certificates
• Security Testing
  • Security testing methodology
    • Security testing – goals and methodologies
    • Overview of security testing processes
    • Threat modeling
  • Security testing techniques and tools
    • Code analysis
    • Dynamic analysis
• Wrap Up
  • Secure coding principles
    • Principles of robust programming by Matt Bishop
    • Secure design principles of Saltzer and Schröder
  • And now what?
    • Software security sources and further reading
    • Python resources
    • Machine learning security resources