Solving business problems with statistics and machine learning.

Dipti Rani  • Peerlist

This project is not just a standard malware classifier; it's an end-to-end study on adversarial robustness in AI security.

While many models can achieve high accuracy (>99%) on clean data, this project demonstrates that they are often dangerously insecure and vulnerable to simple attacks. It then implements a professional-grade defense (Adversarial Training) to build a truly robust and secure classifier.

Final Result: The model's security was systematically improved from 0% robustness to 100% robustness against a black-box adversarial attack.

AISEC- AI Powered Malware Detection System an

I engineered "MyGit," a lightweight version control system from scratch in Java, to master the internal mechanics of Git.
My implementation replicates Git's core architecture, featuring a content-addressable object store powered by SHA-1 hashing and a commit history structured as a Directed Acyclic Graph (DAG).

This project was a focused exercise in system design and applied data structures, providing a foundational understanding of the tools that underpin modern software engineering.