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

Creeper virus

1971

Computer pioneer John von Neumann’s posthumous work Theory of Self-Reproducing Automata, was published in 1966. Five years later, the first known computer virus, called Creeper, was written by Bob Thomas. Written in PDP-10 assembly language, Creeper could reproduce itself and move from computer to computer across the nascent ARPANET.

Creeper did no harm to the systems it infected - Thomas developed it as a proof of concept, and its only effect was that it caused connected teletype machines to print a message that said “I’M THE CREEPER: CATCH ME IF YOU CAN.”

Last updated 1 year ago