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Cybersecurity Research Intern
COSGrid Networks
01.2021 - 04.2021
Led a team that created an IoT Device Classification and AI-powered Anomaly Detection prototype, using tools such as Spark, XGBoost, Sklearn, Zeek, Argus, and TShark, achieving 91% accuracy on fresh data
The prototype was selected as a finalist in the 'Cyber Security Grand Challenge!' organized by DSCI
Successfully deployed Apache Metron as a Big Data Network Intrusion Detection Solution on an AWS IoT Greengrass testbed, achieving an accuracy of 92.4% and a false positive rate of 0.24% in detecting malicious attacks automated by Guardicore's Infection Monkey
Education
Master of Engineering - Cybersecurity
University of Maryland
College Park, MD
05.2024
Skills
Python
C
C
Linux
Windows
Cybersecurity
Machine Learning
GNU Debugger (GDB)
Ghidra
IDA Pro
Binary Ninja
DnSpy
Cheat Engine
JD-GUI
X64/x86dbg
Angr
APKTool
Team Leadership
Problem Solving
Communication
Adaptability
Critical Thinking
Certification
Certified AppSec Practitioner (CAP)
Awards
Awarded the second prize in the hardware category for our project IoT Smart Dustbin.
HackTheBox - Earned the Pro-Hacker Title completing 40+ machines and challenges.
DownUnderCTF 2022 - Achieved 544th place as a sole competitor (nier0x00) in the team 'UMDCSEC-B'.