I am currently pursuing a Master's degree at the Manipal School of Information Sciences, specializing in cybersecurity forensics and counterterrorism. With a strong foundation in electronics and communication from Sri Siddhartha Institute of Technology, my academic journey is complemented by hands-on experience as the former Chairperson of the IEEE Student Branch. My current focus lies in vulnerability assessment and advanced driver-assistance systems, bolstered by certifications in network security, ethical hacking, and advanced cybersecurity. Driven by a passion for safeguarding digital spaces, I blend academic rigor with a proactive approach to addressing the dynamic landscape of cyber threats.
VIEW MY Projects →Manipal School of Information Sciences, Manipal, Karnataka, India.
Sri Siddhartha Institute of Technology, Tumakuru, Karnataka, India.
Sarvodaya PU College, Tumakuru, Karnataka, India.
Gained hands-on experience in optical fiber networks, telecom equipment troubleshooting, and teamwork in real-world applications.
Gained proficiency in using Kali Linux for ethical hacking and penetration testing.
Acquired foundational skills in Security Information and Event Management (SIEM) using Splunk.
Enhanced practical knowledge and exam readiness for Certified Ethical Hacker (CEH) certification.
Strengthened expertise in cybersecurity fundamentals and threat analysis through CompTIA Security+ & CYSA+.
Mastered exploratory data analysis techniques essential for machine learning applications.
Gained essential skills in digital forensics for investigating cyber incidents and evidence handling.
Designed a safety system with features like drowsiness detection and theft alerts, achieving 95% success rates.
Integrated GSM-based alert mechanisms for emergency notifications.
Tech Stack: Raspberry Pi, Arduino, OpenCV.
Developed a chatbot to detect vulnerabilities in C programs, achieving 80% accuracy.
Designed a Tkinter-based GUI for real-time error reporting.
Tech Stack: Python, Tkinter, Cppcheck.
Built a machine learning model to classify malicious traffic, deployed on AWS for scalability.
Utilized Scikit-learn for model training and testing.
Tech Stack: Python, AWS, Scikit-learn.
C, Python, SQL, Pandas, NumPy, Arduino.
Power BI, Tableau, Matplotlib, Seaborn.
Classification, Clustering, Model Training.
VPN (IPSec, SSL/TLS), IDS/IPS (Snort, Wireshark), Firewall Configuration.
TensorFlow, PyTorch, Scikit-learn, Nmap, Logic Analyzers.