Malware classification
Train a CNN to classify malware families from grayscale binary images using the Malimg dataset, and learn why byte-level texture is both signal and weakness.
Training and evaluation
The latest entry in the AI red teaming series trains a random forest on NSL-KDD and shows how evaluation metrics map the exact weaknesses an attacker exploits.
Preprocessing and splitting the dataset
Preparing the NSL-KDD dataset for random forest anomaly detection, from binary and multi-class targets to encoding, feature selection, and honest splitting.
Network anomaly detection
Train a random forest on the NSL-KDD dataset for network anomaly detection, with every data loading step examined through an adversarial red teaming lens.
Training and evaluating your first spam classifier
Build, tune, and evaluate a Naive Bayes spam classifier with scikit-learn, then examine what the model reveals to an adversary in this AI red teaming entry.
Feature extraction
How extraction builds the feature space a spam classifier learns from, and why every vocabulary decision creates an evasion path for a red teamer to find.
Preprocessing the spam dataset
Every text cleaning step in a spam classifier either blocks an evasion path or opens one. See how preprocessing shapes what the model can and cannot see.
Bayesian spam classification: the dataset
Preparing the SMS Spam Collection dataset for Bayesian classification, covering download, extraction, loading, and cleaning through an adversarial lens.
Spam classification: Naive Bayes filters
How Naive Bayes spam filters work, why the independence assumption makes them exploitable, and how GoodWords attacks broke email filtering wide open.
Metrics for evaluating a model
Learn how accuracy, precision, recall, and F1-score work in practice, where each metrics deceive, and how adversaries exploit the gaps they leave behind.