Built scalable backend infrastructure to provision virtual lab environments for 50+ students using Terraform, Ansible, and vCenter APIs , enabling secure multi-VM setups with internal, external, and DMZ network segmentation.

Programmed modular Infrastructure-as-Code components and automated deployment scripts, accelerating environment setup by 90% and improving cross-course reusability.

Streamlined VM provisioning via backend logic integrated with web-based dashboards, enhancing instructor control and reducing manual intervention.

Automated deployment, configuration, and management of 50 Linux Virtual Machines using Ansible and Python scripts.

Integrated SSH hardening, key-based authentication, and jump hosts for secure remote access.

Developed a Django-based VM management platform with role-based access control (RBAC)

Implemented SSL/TLS encryption and secure session management to prevent unauthorized access.

Used Ansible Vault for secure credential storage and encryption of sensitive data.

Developed a scalable LMS Website using Laravel Model-View-Controller Framework.

Enhanced system security with OAuth2 and multi-factor authentication.

Integrated REST APIs, including payment gateways (PayPal, Razorpay), location, and Zoom

Designed a user-friendly interface using HTML5, CSS3, Bootstrap, and JavaScript.

Built a normalized database with multi-table joins and optimized SQL CRUD queries.

Developed a Python application to automate file transfers from Hadoop HDFS to AWS S3.

Implemented IAM policies to ensure secure access control for AWS resources.

Added data integrity verification to ensure accurate and complete file transfers.

Enabled encryption for files both in transit and at rest using AWS KMS (Key Management Service).

Enhanced security by using AWS Secrets Manager to store and rotate sensitive credentials (e.g., AWS access keys).

Integrated Amazon CloudWatch to monitor file transfers and track errors.

Configured Amazon SNS (Simple Notification Service) to send email notifications for transfer status updates (success/failure).

Developed a Flask-based API for calculating reward points, ensuring scalability and modularity..

Implemented unit testing using Pytest to validate individual components and ensure code reliability.

Used Docker to containerize the application, ensuring consistency across development and testing.

Implemented API testing using Postman, creating automated test cases for different API endpoints.

Implemented a client-server application in Go for the management of tokens.

Clients issue RPC calls to the server to execute create, drop, read-write methods on tokens. The server executes such RPCs and returns an appropriate response to each call.

Solution is developed in Go and utilizes the gRPC and Google Protocol Buffer frameworks.

Implemented replication and extended each token as a (1,N) register with atomic semantics, allowing multiple reader nodes and a single writer node.

Developed and deployed a resilient read-impose-write-all (quorum) protocol, ensuring the accurate maintenance of crucial state information for each token in the system.

Ensured fail-silent behavior for server nodes by allowing them to "crash-stop" operations on a specific token, while still responding to requests on other tokens.

Executed a successful Man in the Middle (MITM) attack using arpspoof to intercept network communication and gain access to sensitive information.

Utilized tcpdump to capture and analyze network traffic during the MITM attack, verifying the success of the attack.

Implemented a self-signed certificate for TLS-based communication to enhance the security of web traffic during the MITM attack.

Decoded base64 values of HTTP authentication during the MITM attack to gain access to sensitive information and improve the security of the targeted system

Demonstrated strong proficiency in cyber attack techniques by launching DOS attacks such as ICMP flood and smurf, UDP flood, TCP SYN, and HTTP Slowloris, and gaining an understanding of how these attacks can be used to compromise a targeted system

Utilized shell scripting and access permissions in Linux to automate the process of launching DOS attacks.

Implemented ufw rules to prevent DOS attacks and secure the targeted system, leveraging expertise in network protocols and security standards to identify and mitigate potential threats.

The program spawns M workers threads and one coordinator thread.

Workers communicate with the coordinator using GoLang channels and routines.

The coordinator partitions the data file into M equal-sized contiguous fragments and assigns each fragment to a worker via a JSON message.

Each worker computes a partial sum and count of integers within its assigned fragment and identifies any prefix or suffix that may have been split between its neighboring fragments.

The coordinator accumulates all workers' partial sums and counts, as well as the sums and counts of the couple of integers in the concatenation of kth suffix and (k+1)th prefix.

Upon receiving responses from all the workers, the coordinator calculates the average of the numbers in the datafile.

Implemented Diffie Hellman Key Exchange

Used Socket programming for communication of Keys.

The Keys are encrypted using RC4 Stream Encryption before communication.

Developed a machine learning model using Python and Scikit-learn that predicts the difficulty of math problems and the time taken to respond.

The model analyzes data from pupil response to achieve an accuracy of 70%.

Utilized Pandas and Numpy for data manipulation and Matplotlib for data visualization.

This cutting-edge technology has significant implications for education and cognitive research.

Client Server based program using RMI.

Implemented Clock Synchronization using Berkeley's Algorithm.

Used Ring Election Algorithm for Electing the coordinator.

Data Centric Model Replicated Write Protocol is used for Data consistency.