Hands-On Artificial Intelligence for Cybersecurity

Unsupervised learning

In the case of unsupervised learning, the algorithms must try to classify the data independently, without the aid of a previous classification provided by the analyst. In the context of cybersecurity, unsupervised learning algorithms are important for identifying new (not previously detected) forms of malware attacks, frauds, and email spamming campaigns.

Here are some examples of unsupervised algorithms:

• Dimensionality reduction:
  • Principal component analysis (PCA)
  • PCA Kernel
• Clustering:
  • k-means
  • Hierarchical cluster analysis (HCA)