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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  1. Malware Detection

CryptoLocker ransomware

2013

CryptoLocker became famous for its rapid spread and its powerful asymmetric encryption that was (at the time) uniquely difficult to break.

It also became famous due to something unusual in the malware world: a happy ending. In 2014, the U.S. DoJ and peer agencies overseas managed to take control of the Gameover Zeus botnet and restore the files of CryptoLocker victims free of charge. Unfortunately, CryptoLocker spread via good old-fashioned phishing as well, and variants are still around.

Last updated 1 year ago