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

Mirai botnet

2016

Internet of Things (IoT) devices are omnipresent, ignored, and often go unpatched for years. The Mirai botnet was similar to some of the early malware we discussed because it exploited a previously unknown vulnerability and wreaked far more havoc than its creator intended.

In this case, the malware found and took over IoT gadgets (mostly CCTV cameras) that hadn’t had their default passwords changed. Paras Jha, the college student who created the Mirai malware, intended to use the botnets he created for DoS attacks that would help settle scores in the obscure world of Minecraft server hosting, but instead, he unleashed an attack that focused on a major DNS provider and cut off much of the U.S. east coast from the internet for the better part of a day.

Last updated 1 year ago