{"id":218,"date":"2023-02-15T03:03:49","date_gmt":"2023-02-15T03:03:49","guid":{"rendered":"https:\/\/wp.odu.edu\/cyberimpact-template\/?page_id=218"},"modified":"2025-04-10T08:43:59","modified_gmt":"2025-04-10T08:43:59","slug":"skills-2","status":"publish","type":"page","link":"https:\/\/sites.wp.odu.edu\/jaylinsayles\/skills-2\/","title":{"rendered":"Skills"},"content":{"rendered":"\n
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Technical Proficiency<\/p>\n<\/div><\/div>\n\n\n\n

<Possibly insert an overview of technical skills?><\/p>\n\n\n\n

Machine Learning Model Development<\/strong>:<\/p>\n\n\n\n

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. <\/p>\n\n\n\n

Spam Dataset<\/a>Download<\/a><\/div>\n\n\n\n
Python Code<\/a>Download<\/a><\/div>\n\n\n\n

Linux and Wireless Traffic Analysis:<\/strong><\/p>\n\n\n\n

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<\/strong>, Cain & Abel<\/strong>, and Aircrack-ng<\/strong> to launch dictionary and brute-force attacks.<\/p>\n\n\n\n

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<\/code> 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.<\/p>\n\n\n\n

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.<\/p>\n\n\n\n

<Inside Assignment 5 password cracking from 301><\/p>\n","protected":false},"excerpt":{"rendered":"

<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… <\/p>\n

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