Malware Analysis

Role: Malware Analyst 

Tools: Exeinfo PE, PE Studio, Wireshark, Regshot, Strings, Task Manager, Virus Total

Duration: Spring 2024

Description: 

This project focused on conducting both static and dynamic analysis of malicious executable files to identify behavioral patterns, persistence mechanisms, and system-level impact. Twelve distinct malware samples were analyzed within a secure sandbox environment to prevent contamination of production systems. 

Key Contributions: 

• Utilized ExeInfo PE and PE Studio to inspect executable headers, imports, and compiler signatures to determine obfuscation and packing status.

• Applied RegShot to detect Windows Registry changes, revealing persistence and configuration modifications.

• Monitored process and network activity through Task Manager and Wireshark to identify CPU spikes, file writes, and attempted network communication.

• Compared file hashes using VirusTotal, validating family associations and threat intelligence classifications.

• Documented detailed behavioral profiles indicating anti-debugging tactics, file manipulation, and resource abuse consistent with Trojan and ransomware operations.

Outcome: 

The investigation produced a comprehensive Malware Analysis Report summarizing indicators of compromise, behavioral findings, and potential mitigation strategies. This project strengthened my expertise in reverse engineering, digital forensics, and threat analysis, reinforcing the importance of secure testing environments and methodical documentation within malware research.

Incident Response Playbook

Role: Security Stategist 

Tools: NIST 800-61 Framework, Microsoft Visio, Google Workspace

Duration: Spring 2024

Description:

This project centered on designing a comprehensive Incident Response (IR) and Disaster Recovery (DR) framework for a simulated enterprise network environment. The objective was to develop a structured, actionable playbook aligned with NIST 800-61 and ISO/IEC 27035 standards, ensuring organizational preparedness for cybersecurity incidents and continuity after disruptive events.

Key Contributions: 

• Authored a formal Incident Response Plan outlining detection, containment, eradication, and recovery procedures for various attack scenarios (malware, phishing, data breach, insider threat).

• Developed an accompanying Disaster Recovery Strategy addressing backup schedules, recovery point objectives (RPO), and recovery time objectives (RTO).

• Designed response flowcharts and communication matrices using Visio to visualize escalation paths, stakeholder responsibilities, and decision-making timelines.

• Conducted a tabletop simulation in a virtual lab to test plan efficiency, documenting outcomes and recommending procedural improvements.

• Integrated reporting templates for post-incident analysis and continuous improvement cycles.

Outcome:

The completed Incident Response Playbook provides a clear operational guide for responding to cyber incidents and restoring mission-critical services efficiently. The project reinforced my ability to apply risk management principles, contingency planning, and business continuity frameworks in realistic enterprise contexts.

Phishing Detection Using Machine Learning

Role: Machine Learning Security Intern

Tools:  Python, pandas, scikit-learn, TF-IDF, Multinomial Naive Bayes, Logistic Regression, NVIDIA Jetson Orin Nano, Ubuntu Linux

Duration: Spring 2025

Description: 

I independently developed a machine-learning phishing detection lab for a Penn State professor. The project combined legitimate email examples from the Enron dataset with phishing emails from a publicly available honeypot collection. Python scripts prepared and labeled the data, converted email text into numerical features using TF-IDF, and trained classifiers to distinguish phishing from legitimate messages.

The lab ran on an NVIDIA Jetson Orin Nano and became part of Penn State’s SRA 111 curriculum, giving students hands-on experience with email classification and the use of machine learning in cybersecurity.

Key Contributions: 

• Developed scripts to extract text from phishing email files, prepare Enron email data, and combine the sources into labeled datasets.

• Implemented a Multinomial Naive Bayes workflow with balanced training data, a 70/30 train/test split, and TF-IDF features fitted only on the training set.

• Added evaluation code to report accuracy, precision, recall, and F1 scores, and saved the trained model and vectorizer for reuse.

• Explored Logistic Regression in a separate classification experiment.

• Created balanced email samples, individual email exports, and setup documentation for student exercises.

• Configured the Jetson-based lab environment and documented installation and troubleshooting steps.

Outcome: 

The project was incorporated into Penn State’s SRA 111 curriculum as an instructional phishing-classification lab. It connected data preparation, model training, and email analysis in a classroom exercise.

Implementation of Penn State Abington -Rydal Cybersecurity Lab

Role: Lead Information Science Technology Lab Technician 

Tools: Windows 10,  Kali Linux, Network Switches, Routers, VLAN Configuration

Duration: Spring 2023

Description:

I led the design and deployment of a fully operational Cybersecurity and Networking Lab located in Rydal Executive Plaza, serving as a dedicated environment for Penn State Abington students to engage in programming, network configuration, and cybersecurity exercises. The lab supports both in-person tutoring and hands-on instruction, integrating real hardware and virtualized systems to simulate professional enterprise infrastructure.

Key Contributions:

• Configured a secure local network architecture featuring managed switches, routers, and segmented VLANs to isolate student environments.

• Deployed Windows 10, and Kali Linux-based systems for multi-platform learning and administrative control.

• Installed and maintained lab tools including Wireshark, pfSense, Cisco Packet Tracer, and Snort to teach packet analysis, routing, and firewall configuration.

• Implemented centralized authentication, logging, and access control policies to protect lab integrity and ensure compliance with university IT standards.

• Collaborated with faculty and tutoring staff to integrate the lab into the Programming & Network Tutoring program, enabling live demonstrations and guided cybersecurity labs.

Outcome:

The Rydal Cyber Lab became an integral hands-on training space for cybersecurity and IT education at Penn State Abington. It enhanced student learning through applied network simulations, live troubleshooting exercises, and exposure to enterprise-grade security tools. This project strengthened my expertise in systems architecture, network administration, and secure lab design, while demonstrating leadership and collaboration in academic infrastructure development.

Securing the Smart Home: Penetration Testing and Signal Analysis of a Zigbee Mesh Network

Role: 

Tools: Zigbee Mesh Network, Digi XBee3, SDR Pluto, Wireshark, Kali Linux, Python, MQTT, Penetration Testing Tools

Duration: Fall 2024 – Fall 2025

Description:

Developed a home automation and IoT security system utilizing a Zigbee mesh network to demonstrate both secure communication and vulnerability assessment within wireless environments. The project combined IoT device integration, radio frequency analysis, and penetration testing to evaluate network resilience against common attack vectors in smart home ecosystems.

Key Contributions:

• Designed and deployed a functional Zigbee mesh network using Digi XBee3 modules to enable communication between IoT nodes such as sensors and actuators.

• Configured and tested home automation protocols with secure message passing, data encryption, and device authentication.

• Conducted signal interception and vulnerability assessment using SDR Pluto, Rohde & Schwarz Signal Generators and Spectrum Analyzers, and Wireshark to analyze real-time RF communication.

• Employed Kali Linux penetration testing tools to evaluate security posture, exploit weak configurations, and document attack surfaces.

• Programmed custom Python scripts for message decoding, data logging, and traffic visualization across multiple nodes in the mesh network.

Outcome:

The project successfully demonstrated how wireless IoT networks can be both secured and exploited through practical penetration testing and signal analysis. This experience deepened my understanding of embedded network security, radio communication protocols, and ethical hacking methodologies, preparing me to address real-world IoT vulnerabilities in modern cyber-physical systems.

Home Cybersecurity Lab: Virtualized Network for Ethical Hacking and System Hardening

Role: Cybersecurity Student 

Tools: VirtualBox, VMware, Kali Linux, Metasploit, Wireshark, pfSense, Ubuntu Server, Windows 11, Hack The Box, Python

Duration: Fall 2023 - Present 

Description:

Designed and maintain a personal cybersecurity home lab to simulate enterprise network environments for penetration testing, system hardening, and threat analysis. The lab serves as a sandbox for ethical hacking, vulnerability research, and skill development across both offensive and defensive cybersecurity domains. It includes virtualized systems, isolated network segments, and open-source tools that replicate real-world corporate infrastructures and security challenges.

Key Contributions:

• Built a multi-VM environment using VirtualBox and VMware to emulate client-server and attacker-defender scenarios.

• Configured pfSense firewalls, Ubuntu servers, and Windows hosts to test access control policies, intrusion prevention systems, and network segmentation.

• Regularly use Hack The Box and similar platforms to practice privilege escalation, network enumeration, and post-exploitation techniques in a safe and controlled setting.

• Integrated tools such as Wireshark, Metasploit, and Burp Suite to analyze network traffic, identify vulnerabilities, and simulate attacks for defensive learning.

• Developed and tested custom Python scripts for automation, port scanning, and log analysis to strengthen system monitoring capabilities.

Outcome:

The home lab provides a hands-on, continuous learning environment that enhances practical cybersecurity experience beyond the classroom. It supports experimentation with penetration testing tools, vulnerability management, and secure network architecture, solidifying my skills in ethical hacking, system administration, and defensive security engineering.