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

ILOVEYOU worm

2000

Onel de Guzman crafted his creation with straightforward criminal intent: he couldn’t afford dial-up service, so he built a worm that would steal other people’s passwords so he could piggyback off of their accounts.

But the malware so cleverly took advantage of several flaws in Windows 95 that it spread like wildfire, and soon millions of infected computers were sending out copies of the worm and beaming passwords back to a Filipino email address.

Onel de Guzman was never charged with a crime, because nothing he did was illegal in the Philippines at the time, but he expressed regret in an interview 20 years later, saying he never intended the malware to spread as far as it did.

Last updated 1 year ago