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Hi, I am Rahul

Rahul Raj

Technology Enthusiast at Rand0mwalk.

I am a passionate technologist with decade long experience handling IT and IT enabled Services for Government and in Corporate Sector. I built several tools in the domains of Cybersecurity and Data Science. I like to learn everyday, read everyday, grow everyday

Team Playing
Technical Skills
Result Oriented


Government of India

Oct 2013 - Present, India


Mar 2023 - Present

  • Leads innovation and quality with a team of 34 specialists.
  • Maintenance and upkeep of communication equipment.
  • Ensure efficient Power Generation and Distribution.
  • IT and Cybersecurity.
Trainer/ Instructor (IT/ Security/ Embedded Systems)

Aug 2020 - Feb 2023

  • Trained over 2000 people in domain of IT, Embedded System and Cybersecurity.
  • Organised three national level workshops in Artificial Intelligence and Data Science.
  • Guided several software and IT projects in AI and Datascience.
Deputy Director (IT & InfoSec)

May 2015 - Jul 2018

  • Provided IT and ITeS for over 500 users geographically spread across 11000 acres.
  • Provided Information Security cover to safeguard highly confidential digital assets.
  • Concluded several temporary contractural agreements to mitigate manpower shortfalls.
  • Undertook several projects worth ~180 Crores for development of Digital services.
Asst Engineer (Electrical & IT)

Oct 2013 - Apr 2015

  • Maintenance of various Electrical Equipment.
  • IT Asset Life Cycle Management

Programmer Analyst
Congnizant Technology Solutions (CTS)

Feb 2011 - Jun 2011, Chennai

  • Administration/ Debugging/ Troubleshooting of all internal applications of Company, mostly backend SQL related.

System Administrator
Bobcares Inc.

Oct 2010 - Jan 2011, Kochi, Kerala

  • Remote technical support to webhosting companies.
  • Troubleshooting hosting errors.
  • Handling customer queries wrt hosting services.


Developer March 2018 - Present

A passive OSINT tool for username recon. Tool is written in python with nil dependency of APIs of any website, whatsoever.

Jekyll Static Blog
Jekyll Static Blog
Developer Jun 2018 - Dec 2021

My Personal static blog site created using Jekyll. Retired this when I moved the contents to Hugo.

Network Intrusion Detector
Network Intrusion Detector
Developer Jul - Sep 2019

Network Intrusion Detector using Machine Learning on the KDD Cup99 dataset. Program classifies incoming connections as good or bad

Developer Oct 2019

This is a python implementation of the original work in the paper ‘Truth will out’, Departure-Based Process-Level Detection of Stealthy Attacks on Control Systems.

Developer Mar 2020

An interactive data analytics on Covid-19 Pandemic in India using GapMinder.

Developer Jun 2020

WExDA is a web based data exploration tool primarily useful for data preperation/ data analysis stage. This automates the EDA via web ui and is built using streamlit.

Developer Mar 2019

An attempt to get feet wet with Android Development. One unique feature added is the location service. Demo of the app can be found here.

Partisan Bias in News
Partisan Bias in News
Developer Sep 2018

The project was aimed at using various data mining techniques to analyse the partisan bias on Indian News media. Data was generated through webscrapping and insights were drawn from analytics.

EmployeeFeedback DApp
EmployeeFeedback DApp
Contributor Apr 2019

Keeping in mind privacy, transparency, fairness and absolute immunity to bias and manipulation, this DApp is in Etherium blockchain using solidarity smart contracts. Demo vide can be found here


Process Aware Stealthy Attack Detection in Industrial Control Systems
2022 OpenAIRE 22 March 2022

This is a python implementation of the original work in the paper Wissam Aoudi, Mikel Iturbe, and Magnus Almgren. 2018. Truth Will Out. Departure-Based Process-Level Detection of Stealthy Attacks on Control Systems. In 2018 ACM SIGSAC Conference on Computer and Communications Security (CCS ’18), October 15–19, 2018, Toronto, ON, Canada. ACM, New York, NY, USA, 15 pages.

Diffusion Map for Manifold Learning, Theory and Implementation
KDNuggets 14 March 2020

‘Curse of dimensionality’ is a well-known problem in Data Science, which often causes poor performance, inaccurate results, and, most importantly, a similarity measure break-down. The primary cause of this is because high dimensional datasets are typically sparse, and often a lower-dimensional structure or ‘Manifold’ would embed this data. So there is a non-linear relationship among the variables (or features or dimensions), which we need to learn to compute better similarity.