Sai Sritha Dulam

Accomplished IT professional with expertise in C, Python, and MySQL, specializing in IAM technologies such as OAuth, SAML, and LDAP. Proficient in utilizing IAM tools like Okta to enhance network security and conduct thorough penetration testing and risk assessments. Demonstrates effective communication skills and a strong foundation in machine learning, HTML, and CSS. Committed to advancing cybersecurity measures while pursuing opportunities to innovate within the field.

• Cyber Security Foundation – IBM
• Penetration Testing with Metasploit – LinkedIn
• Pre-Security learning path – TryHackMe

Cyber-Attack Classification Using Machine Learning(Post Graduation):

This project focuses on utilizing Machine learning (ML) and Deep Learning (DL) algorithms, specifically Support Vector Classification (SVC) and Neural Networks-based sequential models, for real-time Cyber-Attack classification and Intrusion Detection.

ACHIEVEMENT:

The SVC model achieved over 97% accuracy, and the Neural Network model surpassed 97% for Cyber-Attack classification within the NSL-KDD dataset. Predicting Compressive Strength of Ashbricks using Machine Learning, This project developed a machine learning-based system to predict the compressive strength of ash bricks, aiming to save time and costs compared to traditional concrete strength testing methods., Developed a user-friendly webpage using HTML, CSS, and JavaScript to predict ash brick strength based on input parameters, and integrated Flask and Jupyter APIs for result generation, streamlining the prediction process.

• Reading books
• Listening music
• Pencil Sketches