Usman Naeem

Academic

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Usman Naeem
+923161676916, usman.naeeml703@gmail.com
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Bahria Town, Rawalpindi.
Linkedln: http_s:[lwww.linkedin.comfinfusman-naeem-74096523SL
Education
Bachelor of Science (Cyber Security)
Major:
l~yber Security, Information Security, Data Security, Blockchain, Information Assurance,
Network Security, Digital Forensics and Malware Analysis & Reverse Engineering.
Dr. A. Q. Khan College of Science & Technology, Phase-8, Bahria Town
ICS (Physics, Computer Science, Mathematics)
Dr. A. Q. Khan College of Science & Technology, Phase-8, Bahria Town
Matriculation (Physics, Chemistry, Computer Science)
Projects
-;nal Proiect: ProbeXpert -
External scanning tool with risk assessment [React, Node.js, Python, MongoDB,
Docker, Kubernetes, Flask]
Leveraged an external scanning tool to discover high-risk vulnerabilities across client networks, including endpoints,
applications, and DNS configurations, enabling remediation strategies that improved overall security posture.
Semester Proiects:
Integrated Cybersecurity Framework (SIEM) [Wazuh, MISP, DVWA, Palo Alto Networks]:
Deployed a fully integrated SIEM framework delivering real-time threat detection and 100% validated security events.
Advanced Network & Cybersecurity Exploration [Nmap, ARP Poisoning, MITM Techniques, Packet Sniffing):
Executed ARP spoofing to orchestrate MITM attacks, extracting credentials while reinforcing defensive tactics.
PharmaChain [MongoDB, Express.js, React.js, Node.js, ethers, Ganache, Truffle) :
Engineered a blockchain-based supply chain to secure pharmacy-distributor transactions and reduce fraud risks.
Information Assurance Project [Physical Security, Information Security] :
Performed threat analyses, EISP, and contingency planning, fortifying enterprise systems through layered defenses.
Al Project [Python):
Developed a constraint-based exam planner using genetic algorithms, optimizing schedules amid resource constraints.
Routing System using Dijkstra [C++, Dijkstra, Heap, Queue, Graph):
Built a CSV-based topology parser leveraging Dijkstra's algorithm to compute the shortest paths for efficient routing.
Work Experience
Network Security Engineer, Ebttikar Technology Company, Riyadh, Saudi arabia.
IJun 2023 - Aug 2023
Interned as a network security engineer, leveraging NGFW (Palo Alto, Cisco ASA) for secure deployments.
Skills & Tools
Professional Skills
Collaboration, Interpersonal Skills, Active Listening, Graphic Designing.
Technical Skills
Python, Flask, React.js, Node.js, Express.js, MongoDB, SQLite, DevSecOps, Docker, Blockchain,
~utopsy, Nmap, OpenVAS, AWS, Azure, Digital Ocean, ASM, C++, DSA, Truffle, Ganache, Ethers,
MS SQL, Cisco, Word press, PHP, Adobe Illustrator & Photoshop.
Achievements
Silver & Bronze medalist, Cyber Security, FAST NUCES.
Silver Medalist, HSSC, Dr. A. Q. Khan College.
Shield and Trophy, Middle School, Saudi Arabia.
Trainings/ Certification
Certified Azure Cloud Practitioner, 2023.
Certified Graphic Designing, Web Development & Word Press Development.
Completed Cisco CCNA & CCNP (Enterprise) training at Corvit, Rawalpindi, enhancing networking acumen.
Participated in a hands-on Capture the Flag workshop at MCS NUST, Rawalpindi, sharpening offensive skills.
Activities
Served as Graphics Head & Web Vice Head at Cyber Space Legion, FAST Islamabad, leading creative initiatives.
Designed a blue teaming phase at Capture the Flag, NaSCOn' 24, FAST, Islamabad.
Interests
Playing Competitive games, Watching web series
FAST NUCES ISLAMABAD CAMPUS
FAST SCHOOL OF
COMPUTING
FINAL YEAR PROJECTS
FAST NUCES ISLAMABAD CAMPUS
Blockchain Based Academic Portal For Instructors
OneBlock is a blockchain-based platform designed for instructors to manage student attendance,
grade assessments (such as quizzes, exams, projects, and finals), and make announcements. All
activity logs are securely stored on the blockchain, ensuring transparency and enabling the
detection of any suspicious activities.
Administrators have full access to view all logs, and automated alerts are generated for any
changes in attendance or grades. The admin panel provides detailed alert infonnation, including
transaction hashes, instructor names, timestamps, IP addresses, and a summary chart. Additionally,
administrators can generate and print reports for further analysis.
All the logs are temper proof and immutable and can be verified on Basechain.
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FAST NUCES ISLAMABAD CAMPUS
emit.
The Emit Threat Detection System is being developed with a focus on cybersecurity professionals
and SOC analysts. There are two views of this app - Analyst View and Admin View - the former is
for analysts to utilize the features of our app to query real-time security alerts, analyze logs from
multiple machines, check for suspicious activities, monitor policy-based security events, and review
insider threat detections in natural language. If an analyst finds that the detection engine has failed
to identify a threat correctly, they can escalate the case to the admin using the feedback button on
the screen. The latter is for the ad min, who will review escalated cases, refine detection rules, and
update the threat database to improve accuracy.
Features include:
Security-related queries, including policy-based detections, insider threat analysis, and log
monitoring.
- Real-time alerts from the detection engine, highlighting potential insider threats and anomalies.
-Log analysis for tracking security events, investigating alerts, and correlating suspicious activities.
-HR Flagging System, which increases the suspicion level of a user involved in suspicious activity.
Emit will track and record suspicion levels whenever insider threat behavior is detected.
-Escalation to the admin panel in case of missed or false alerts.
Emit is an hsider threat detection software, that uses
behavioral analysis for anomaly detection, providing real-
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Technology Used:
React, Express.js, Node.js, MongoDB, Wazuh,
Virtualization, VS Code, Python
Supervisor Name:
Dr. Shahela Saif
Group Members:
Hadia Shahid (i21-1563)
Hassaan Jamil (i21-2774)
Nawfal Waqar (i21-1914)
FAST NUCES ISLAMABAD CAMPUS
Ma lwareXplore
MalwareXplore - Al-Driven Decentralized Malware Analysis and Detection
Our system is designed to detect malicious PDF files using a combination of static and dynamic
analysis techniques along with Al-based verification. The tool is built for cybersecurity professionals
and malware analysts, providing an automated, fast, and accurate way to determine if a PDF file is
safe or harmful.
Key Features:
•
Automated PDF Malware Detection: Predicts whether a file is benign or malicious.
•
Dual Detection Approach: Uses static &

AI enrichment