Cybersecurity enthusiast with 4+ years of hands-on learning and project-based experience in SOC operations, SIEM engineering, and offensive security fundamentals.
I specialize in building real-world security labs using tools like Wazuh, Kali Linux, and Windows endpoints to simulate attacks, validate detections, and analyze alerts end-to-end.
My work focuses on detection engineering, log analysis, attack simulation, and automation using Python — with a long-term goal of evolving into a Security Architect with AI-driven security expertise.
Designed and implemented an enterprise-level Security Operations Center (SOC) lab to simulate real-world security monitoring and incident response scenarios. The lab focuses on log collection, threat detection, alert analysis, and investigation workflows, providing hands-on experience with SOC operations and defensive security practices used in enterprise environments.
Performed detailed network traffic analysis on malicious PCAP files using Wireshark to identify suspicious communication patterns, indicators of compromise (IOCs), and malware behavior. The project focuses on protocol analysis, traffic filtering, and threat investigation to strengthen network security monitoring and incident response skills.
Developed a structured offensive security playbook outlining standardized methodologies for ethical hacking and security testing. The playbook documents reconnaissance, vulnerability assessment, exploitation, and post-exploitation techniques in a controlled lab environment, supporting repeatable and responsible security testing practices.
Developed an intelligent song recommendation system that detects user emotions using computer vision and deep learning techniques. The system analyzes facial expressions in real time and recommends personalized music based on detected emotional states, enhancing user experience through context-aware recommendations. The project demonstrates the practical application of AI in personalization and multimedia systems.
Developed an end-to-end sales forecasting solution using historical sales data to analyze trends, seasonality, and demand patterns. The model generates future sales predictions to support business planning, inventory optimization, and data-driven decision-making. The project focuses on accuracy, scalability, and real-world business applicability.
ADeveloped an automated resume screening system to analyze and categorize resumes based on job requirements. The project leverages natural language processing (NLP) techniques to extract key skills, experience, and keywords, helping streamline candidate shortlisting and improve recruitment efficiency
