M.Tech Cybersecurity student building ML-driven security systems and researching AI-augmented threat detection. I bridge real-world network operations with cutting-edge research — from hospital networks to malware analysis using LLMs.
I'm a second-semester M.Tech Computer Science Engineering student specializing in Cybersecurity at Sanskaram University, where I hold a GPA of 9.25/10. My work sits at the intersection of machine learning, network security, and applied AI research.
I'm currently building a Machine Learning-based Intrusion Detection System (IDS) using the CICIDS 2017 dataset — classifying network traffic into attack and benign categories with a security-first evaluation philosophy. I'm also applying to KAUST's research internship on Dynamic Malware Analysis using LLMs.
Prior to my M.Tech, I served as a Network Administrator at Nizamiye Hospital, where I configured firewalls, managed network switches, and led fiber optic infrastructure deployments in a critical medical environment. I also completed research internships at the National Centre for AI and Robotics (NCAR) and a data science internship at Cerebro Systems Hub.
I'm multilingual — fluent in English and Hausa, with basic proficiency in Turkish — and passionate about research that has real-world security impact.
A focused set of capabilities built across academic coursework, research internships, and real-world network operations.
From security operations to research, I work across the full arc of cybersecurity — practical and theoretical.
I design and implement machine learning pipelines for network security — from raw packet data to trained classifiers that prioritize real-world threat detection metrics like ATTACK-class recall.
I explore how Large Language Models and advanced ML architectures can augment intrusion detection, malware analysis, and behavioral fingerprinting — moving security from reactive to intelligent.
My background in live network administration — including hospital-grade infrastructure — gives my research a grounded perspective. I've seen how attacks look in practice, and I build accordingly.
Active research directions spanning ML security, privacy-preserving systems, and LLM-augmented analysis.
Hands-on work at the intersection of security and machine learning.
ML-based Network Intrusion Detection System built on the CICIDS 2017 dataset. Binary classification of BENIGN vs. ATTACK traffic using Random Forest and Logistic Regression, with a Streamlit dashboard deployed to Streamlit Cloud.
Led a team to design, prototype, and build a functional robotic arm at the National Centre for AI and Robotics. Managed planning, electronics integration, and testing phases end-to-end.
End-to-end fiber optic infrastructure deployment for Nizamiye Hospital staff quarters — site survey, cable laying, terminal splicing, router configuration, and full network documentation.
A journey through network operations, AI research, and cybersecurity academia.
Open to research internships, collaborations, and cybersecurity opportunities worldwide.