Cybersecurity | Application Security | Incident Response
I am a cybersecurity student currently pursuing an M.Tech in Network and Information Security at Pondicherry University. My interests lie in defensive security, threat detection, and digital forensics. Through hands-on labs and simulation platforms such as Blue Team Labs Online and TryHackMe, I have developed practical skills in log analysis, system monitoring, and incident investigation. I enjoy analyzing security events, identifying attack patterns, and understanding how systems can be strengthened against cyber threats. My technical foundation includes networking fundamentals, Python scripting, and Windows/Linux system administration. I am actively building my knowledge in Security Operations Center (SOC) workflows, threat analysis, and security monitoring. I am passionate about continuous learning and aim to contribute to protecting organizations from evolving cyber threats through strong analytical and problem-solving skills.
Currently, I am actively strengthening my cybersecurity expertise through continuous hands-on practice and real-world security labs. I am working on practical blue-team investigations and threat detection challenges on Blue Team Labs Online, where I analyze logs, investigate security incidents, and develop incident response and threat hunting skills. In parallel, I am advancing my penetration testing and defensive security knowledge through structured learning paths and labs on TryHackMe. These platforms allow me to simulate real-world attack scenarios, understand adversary techniques, and practice identifying vulnerabilities and improving system security.
Developed a Convolutional Neural Network (CNN)-based deep learning model for the detection of gastrointestinal cancer from medical imaging data. The model was designed to automatically identify patterns and features associated with cancerous tissues, significantly improving diagnostic accuracy. Implemented data preprocessing techniques including image normalization, resizing, and augmentation to enhance model performance and reduce overfitting. Trained and evaluated the model using appropriate performance metrics such as accuracy and loss functions to ensure reliable predictions. Focused on improving the interpretability of the model by analyzing prediction outputs, enabling better understanding and trust for potential clinical use. This project demonstrates the application of machine learning techniques in healthcare and highlights skills in deep learning, data analysis, and problem-solving.
Built a cybersecurity lab to simulate real-world monitoring and security operations. Performed log analysis to detect anomalies such as failed login attempts and unauthorized access. Configured firewall rules to control network traffic and enhance system security. Correlated logs to identify attack patterns and troubleshoot authentication and connectivity issues. Developed structured workflows for efficient incident detection and resolution.
Email: Santhiya0812@hotmail.com
LinkedIn: Santhiya Mahadevan