Trellix Intelligent Sandbox (Formerly McAfee Advanced Threat Defence) enables organizations to detect advanced, evasive malware and convert threat information into immediate action and protection. Unlike traditional sandboxes, it includes additional inspection capabilities that broaden detection and expose evasive threats. Tight integration between security solutions - from network and endpoint to investigation - enables instant sharing of threat information across the environment, enhancing protection and investigation.● Led the debugging, development, and stabilization efforts to automate the calculation of CPU & Memory consumption for SOAK testing, resulting in improved automation outcomes. ● Enhanced automation coverage by incorporating new features such as Email connector status and Swap memory utilization in Python. ● Took ownership of automating Detection Package tests, developed Python-based automation in a Linux environment. Achieved consistent delivery of flawless, error-free package releases to customers.● Implementation of the Snort Mitigation Technique for enhancing the Network Intrusion Detection System. ● Developed a resource consumption utility enabling different teams to monitor CPU, Memory, and Swap memory utilization for any system in Python. Demonstrated proficiency in Python scripting and automation within a Linux environment. ● Engineered and implemented a Logstash pipeline for publishing Telemetry data leveraging ELK stack. ● Contributed to documentation review, Test Plan creation, Test case development, Test execution, Bug reporting, and Validation. ● Utilized Jenkins CI/CD pipeline tool, and the GitHub source code repository to ensure seamless integration and deployment processes. ● Developed SDKs for APIs using OpenAPI, enhancing the developer experience for customers, enabling them to seamlessly integrate and adapt the SDK to their specific environment and requirements.Worked on initial phase of Next-Gen Sandboxing solution.

McAfee’s Advanced Threat Defence(MATD) Sandbox.

Integrated Home Continuous Development Test System facilitates continuous testing throughout the development process to identify blockers or critical issues. This testing procedure simulates Wi-Fi clients and generates TCP traffic at 2Mbps speed for each client using the Candela traffic generator. It monitors various aspects of the Device Under Test (DUT), including uptime, memory usage, temperature, and the number of connected clients. This project focuses on evaluating the performance and stability of the device.● Implemented and expanded Python scripts to monitor uptime and memory usage, integrating them seamlessly into the testing process. ● Enhanced automation scripts for auto-download and upgrade testing of new devices, significantly reducing time spent and eliminating manual effort. Additionally, implemented a mechanism to concurrently add multiple devices from different regions for simultaneous testing and expanded modules in the DOCSIS Automation framework using TCL scripting. ● Enhanced web testing automation with string validation using Watir WebDriver in Ruby, ensuring strings match with design documentation. Failed matches trigger test case failures, prompting immediate issue resolution. ● Enhanced Python scripts to meet project requirements and prepare reports to send across the team. ● Conducted functional testing to ensure the robustness and reliability of product features.