Sarvesh Gaonkadkar

Designed and implemented an AI-Enhanced SIEM Lab using Wazuh agents, ELK stack, and AI modules for advanced threat detection and predictive analytics. Built a multi-VM architecture on Virtual Box, integrating Kali Linux, Ubuntu, and Metasploitable2 to detect and capture real-time data using Filebeat, forwarding it to ELK for analysis with machine learning algorithms, and visualizing threats via Ki

Built a honeypot lab to simulate a live attack on a Windows VM using firewall security configurations, capturing attacker IP addresses. Compared captured IPs with a pre-made geo-location IP address CSV file, categorizing them by region using KQL commands, and visualizing attack regions globally with Microsoft Sentinel.

Designed a model to recognize fingerprints from stored images, comparing results between CNN, SNN, and traditional methods like ORB and SIFT. The model employs each technique, analyzes results graphically, and provides a reliable fingerprint recognition model with improved accuracy for potential implementation in smartphones and applications.

Developed a real-time key logger malware using Python script, collecting logged keys, clipboard information, system information, and window screenshots. Utilized a Fernet key to encrypt these files, which were then sent to the attacker through SMTP while actual files were saved on the victim’s system.

Grade: 9.80

Pursued a Bachelor of Engineering in Computer Engineering with a minor in Cyber Security. Achieved a CGPA of 9.80.

Grade: 75.08%

Completed Higher Secondary Education under the Maharashtra Board. Achieved a percentage of 75.08.

Grade: 91.60%

Completed Secondary Education under the Maharashtra Board. Achieved a percentage of 91.60.