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

Context

Malware, or malicious software, is any program or file that is intentionally harmful to a computer, network, or server.

Malware can infect networks and devices and is designed to harm those devices, networks, and/or their users in some way.

Depending on the type of malware and its goal, this harm may present itself differently to the user or endpoint. In some cases, the effect malware has is relatively mild and benign, and in others, it can be disastrous.

Last updated 1 year ago