Machine Learning Techniques for Cyber Threat Detection

Intermediate

🔍 Machine Learning Algorithms for Cyber Threat Detection

ML algorithms are pivotal in identifying and classifying cyber threats:

🎓 Supervised Learning

Use labeled data to classify traffic as malicious or benign.

🧮 Examples:

  • 🌲 Random Forests
  • ➗ Support Vector Machines (SVM)

🕵️ Unsupervised Learning

Detects anomalies without labels — useful for zero-day attacks.

📊 Examples:

  • 📍 K-Means
  • 🧭 DBSCAN

🤖 Deep Learning

Excels in analyzing vast and complex datasets like:

  • 🌐 Raw network packets
  • 🖼️ Images

📌 Example:
Malware classification using Convolutional Neural Networks (CNNs) by transforming binary files into image formats.

🧪 Code Example: Random Forest Classifier

from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score

# Assuming X contains features, y contains labels
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = RandomForestClassifier()
model.fit(X_train, y_train)
predictions = model.predict(X_test)
print('Accuracy:', accuracy_score(y_test, predictions))

🧠 Selecting the right algorithm depends on:

  • 📈 Data type
  • 🎯 Detection goals
  • ⚙️ Real-time vs. offline analysis requirements