Notes - MCS
Machine Learning Applied to Security
Notes - MCS
Machine Learning Applied to Security
  • Machine Learning Applied to Security
  • Machine Learning
    • AI and ML
    • Taxonomy
    • Limitations
    • Terminology
  • SPAM
    • SPAM
    • SPAM Detection
    • Classification Model
    • Naive Bayes (Discrete)
    • SPAM or HAM
    • Blind Optimization
    • Gradient descent
    • Linear Regression
    • Logistic Regression
    • Binary Classification
  • Anomaly Detection
    • Context
    • Anomaly Detection
      • Examples
      • Detection
      • Techniques
    • Detecting anomalies just by seeing
    • Unsupervised Learning
    • Autoencoders
    • Isolation Forest
    • Local Outlier Factor
    • One-Class SVM
    • Tips
  • Malware Detection
    • Context
    • Creeper virus
    • ILOVEYOU worm
    • CryptoLocker ransomware
    • Mirai botnet
    • Clop ransomware
    • How To Recognize Malware
    • Malware Detection
    • Machine Learning Approaches
    • Requirements
    • Multi-Class Classification
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Machine Learning Applied to Security

Notes from the course's theoretical classes

Index

  1. Machine Learning

  2. SPAM

  3. Anomaly Detection

  4. Malware Detection

Last updated 1 year ago