Fardeen Abdul Aziz

Dedicated and technically proficient professional trained in the Linux operating system, with strong foundational knowledge in networking concepts and protocols. Skilled in network management and configuration through comprehensive courses in Microsoft Certified Solutions Expert (MCSE) and Cisco Certified Network Associate (CCNA). Seeking to leverage this expertise in a networking or system administration role to contribute to efficient and secure IT infrastructure management. Focused Administrator committed to maintaining productive and quality-driven environment. Collaborative and versatile professional with expertise in employee relations, strategic planning and report preparation. Skilled at easily transcending cultural differences.

• Practicing network design and configuration using Cisco Packet Tracer
• Reading articles and staying updated on the latest trends in IT and networking
• Building and configuring a home lab for hands-on networking experience
• Listening to music to relax and unwind
• Staying active by playing sports regularly
• Socializing with friends and exploring new places
• Assisting students with their academic studies and tutoring
• Participating in networking user groups and attending industry meetups for professional growth

• Awarded Best Project Explanation during undergraduate studies.
• Recognized as the Best Class Representative during graduation.
• Honored for organizing a cultural fest and serving as Vice Team Leader.
• Secured multiple awards in sports competitions.
• Won 1st prize in Storytelling at a college-level competition.
• Achieved 1st prize in Interschool Carrom Competition in Class X.
• Won 2nd prize in a Teacher’s Day competition for playing the role of a Math teacher in Class X.

• Certified in Cisco Packet Tracer
• Certified in Red Hat Linux Administration
• Certified in Networking
• Certified in Microsoft Certified Solutions Expert (MCSE)
• Completed Cloud Integrity Assurance course at Governors State University
• Practiced Social Engineering skills
• Completed an internship in Network Security at RAM Infotech
• Published a research paper on network traffic and attack analysis using big data in a cloud environment
• Participated in an internship training program on Data Science and Machine Learning
• Attended a pre-conference tutorial on DevOps

PROJECT-1

Completed a project on “Large Data And Nework Attack Analysis” as a team leader. 

• Focus: The project explores the application of Big Data Analytics in identifying and mitigating network security threats such as DDOS, SQL injection, and brute force attacks.
• Objective: It aims to analyze and secure vast amounts of data by implementing a centralized log analysis system to detect and block attacks in real-time using cloud-based solutions.
• Methodology: The system logs network traffic, analyzes potential attacks, and automatically transmits data to a centralized server for analysis and threat identification.
• Big Data in Cybersecurity: The project demonstrates how Big Data can not only be secured but also serve as a tool to detect cyber threats by leveraging patterns in vast data sets.
• Proposed Features: It includes detection of attackers through IP analysis, protection mechanisms for Big Data, and the ability to identify SQL and brute force attacks.
• Future Enhancements: Plans include integrating encryption techniques for better data security and enhancing network protection.

PROJECT-2

Completed a project on “Plant Disease Detection And Classification Using CNN Model” asa team leader.

• Objective: The project aims to develop an AI-based system that detects and classifies plant diseases using the InceptionV3 architecture, focusing on improving agricultural productivity and reducing crop losses.
• Problem: Plant diseases pose a significant threat to crop yield and food security, with manual detection methods being slow and inefficient. Automated detection using AI addresses these challenges.
• Methodology: The system employs Convolutional Neural Networks (CNN) with InceptionV3 to analyze plant images and classify diseases from a dataset of 70,295 images across various crops like Apple, Corn, Tomato, and others.
• Dataset: The dataset used includes plant images from different crops and covers a range of diseases. It is divided into training (80%) and testing (20%) sets to train the model.
• Accuracy: The model achieved an accuracy of 89% on the test set, with potential for improvement in future iterations.
• Proposed Advantages: The system offers faster and more efficient disease detection compared to traditional methods, providing early intervention to prevent crop losses.
• Future Enhancements: The project can be extended by improving the model's accuracy and enabling it to detect a wider range of diseases as they evolve over time.

PROJECT-3

Windows Server Project:

• Server Configuration and Domain Setup: The project involves configuring two domain controllers (DC1 and DC2), setting up IP addresses, domains, and machine names for each. Workstations are added to the domain (IT_FAA.com), with corresponding user accounts and security groups created.
• Advanced Storage Management: RAID-5 is set up for data redundancy, and data deduplication is configured on the Seattle drive to optimize storage space. Sales storage pools are created for better data management.
• Security Implementation: Security groups for different organizational units (OUs) such as Miami, New York, and Seattle are established, with specific users assigned to these groups. Password policies enforcing complexity, history, and age limits are applied to ensure strong security practices.
• Network Services: The project includes setting up DFS (Distributed File System) namespaces, replication groups, and configuring DNS with zone transfers between domain controllers. DHCP configuration for load balancing between servers ensures efficient IP address allocation.
• Authentication and Backup: RADIUS authentication is enabled for secure network access. Backup processes are established, including creating backups for critical data such as the Miami sales home drive.

PROJECT-4

Social Engineering Project 

This project provided insights into how social engineering principles can be applied to both customer engagement and business security strategies.

Seminar on Network Security

Topic: Importance and Future of Network Security
Key Areas Covered:

• Discussed the importance of securing networks, data, and systems from unauthorized access and breaches.
• Analyzed common network threats, including malware, ransomware, DDoS attacks, phishing, and SQL injection.
• Presented essential security technologies such as firewalls, encryption, IDS/IPS, and VPNs.
• Shared best practices for network security, emphasizing regular updates, strong passwords, multi-factor authentication, and continuous monitoring.
• Explored emerging trends, including AI-driven threat detection, Zero Trust Architecture (ZTA), and IoT security.

Outcome: Enhanced understanding of cybersecurity challenges and solutions in modern digital infrastructure.