Four ML projects applying classic and deep learning techniques to security and text-classification problems, built while working through Hack The Box's AI Red Teamer path (Module 2: Applications of AI in InfoSec).
| Project | Technique | Dataset |
|---|---|---|
| Spam Classification | Naive Bayes + CountVectorizer | SMS Spam Collection |
| Network Anomaly Detection | Random Forest (multi-class) | NSL-KDD |
| Malware Classification | CNN (ResNet-50, transfer learning) | Malimg |
| Sentiment Analysis | Naive Bayes + TfidfVectorizer | IMDB Reviews |
Each folder contains a notebook and a short write-up covering approach and results.
conda create -n ai-infosec python=3.11
conda activate ai-infosec
pip install -r requirements.txtOr with pip directly:
pip install -r requirements.txtThe first three projects each use a meaningfully different ML approach — text classification with Naive Bayes, tabular multi-class classification with Random Forest, and image-based classification with a fine-tuned ResNet-50. The sentiment analysis project is a skills-assessment exercise using the same pipeline as the spam classifier, applied independently to a new dataset.
Based on Module 2 : Applications of AI in InfoSec of the AI Red Teamer Job Role Path by Hack The Box, developed in collaboration with Google.
