Hi, I'm Deepan Chandru

Cybersecurity & Machine Learning Engineer

Passionate about securing digital systems and leveraging machine learning to solve complex problems. Specializing in cybersecurity, data analysis, and AI-driven solutions with expertise in penetration testing and network security.

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About Me

I'm a dedicated cybersecurity and machine learning enthusiast with a strong foundation in computer science and engineering. Having completed my Bachelor's degree at SRM Institute of Science and Technology, Chennai, I specialize in cybersecurity, data analysis, and machine learning applications.

With expertise in both defensive and offensive cybersecurity techniques, I work on developing secure systems, analyzing threats, and implementing machine learning solutions for security challenges. My projects demonstrate my skills in penetration testing, network security, and AI-driven security analytics.

I'm passionate about staying updated with the latest cybersecurity trends and machine learning algorithms, always eager to tackle new challenges in the ever-evolving digital security landscape.

Cybersecurity Expert

ML Engineer

Education

SRM Institute of Science and Technology, Chennai

Bachelor of Technology in Computer Science and Engineering

Specialization in Cybersecurity

September 2021 - June 2025

Skills & Technologies

Cybersecurity

Network Security

Penetration Testing

Ethical Hacking

Security Auditing

Incident Response

Cryptography

Machine Learning

TensorFlow

Scikit-learn

Deep Learning

Data Analysis

NLP

RAG

Programming

Python

C++

SQL

Tools & Platforms

Wireshark

Metasploit

Kali Linux

AWS

Git

Featured Projects

Real-Time Phishing URL Detection Using Ensemble Learning

Built a Python system that can analyze 10,000 URLs per minute to detect phishing attempts in real-time email streams, reducing successful phishing attacks by 8.6%.
Created an advanced machine learning system using multiple algorithms (Random Forest, XGBoost, CatBoost) that improved detection accuracy by 19% compared to traditional blacklist methods.
Analyzed over 50,000 suspicious URLs to identify hidden patterns and improve detection capabilities by 22%.
Worked with security experts to fine-tune the system, reducing false alarms by 15% while maintaining high detection rates.

Python Scikit-learn XGBoost CatBoost Ensemble Learning

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Supervised Polarity Classification of Tweets Using NLP

Cleaned and prepared 1.6 million tweets for analysis using NLTK and SpaCy to ensure high-quality data.
Built sentiment analysis models using Logistic Regression and Random Forest, achieving 90% accuracy - 30% better than simple keyword-based methods.
Made the system fast enough to analyze sentiment in real-time, responding in under 200 milliseconds for large datasets.
Built an interactive dashboard with Streamlit to show sentiment trends, helping teams understand data 40% faster.
Tested the model thoroughly to ensure it works well with different types of Twitter content, making predictions 22% more stable.

Python NLTK SpaCy Scikit-learn Streamlit NLP

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Professional Experience

Data Science Intern

Gradtwin

February 2025 - April 2025

Worked on data analysis and machine learning projects. Developed predictive models and implemented data-driven solutions. Collaborated with the team on various data science initiatives and contributed to the development of AI-powered applications.