<Possibly insert an overview of technical skills?>

Machine Learning Model Development:

I developed and tested a spam detection model using Support Vector Machines (SVM) as part of a machine learning project. The model was trained on real-world text message data, which I cleaned and converted into numerical features using Term Frequency-Inverse Document Frequency (TF-IDF). I ran two experiments using different data splits (70% training / 30% testing and 90% training / 10% testing) to compare how the amount of training data affects the model’s accuracy. This project gave me hands-on experience with machine learning in a cybersecurity context and helped me understand how to evaluate model performance in real-world scenarios.

Linux and Wireless Traffic Analysis:

In this project, I explored different techniques used for password cracking on Linux, Windows, and Wi-Fi networks. I created user accounts, assigned passwords of varying strengths, and used tools like John the Ripper, Cain & Abel, and Aircrack-ng to launch dictionary and brute-force attacks.

For the Linux and Windows sections, I extracted password hashes and successfully cracked them using both command-line tools and graphical interfaces. For the wireless portion, I analyzed encrypted .cap files of Wi-Fi traffic, identified vulnerabilities in WEP and WPA2 encryption, and decrypted the traffic after recovering the Wi-Fi passwords through dictionary attacks.

This hands-on experience helped me understand the importance of strong passwords, common attack vectors in cybersecurity, and how network traffic can reveal critical information if not properly secured.

<Inside Assignment 5 password cracking from 301>