This tool performs static malware analysis by first detecting the file type and then applying file-type–specific analysis techniques. It inspects PE files to identify packing methods, architecture, and indicators of compromise such as URLs and domains, and analyzes Office documents for suspicious macros and other malicious elements.

As you can see, this website has many features. You can learn more about me, read blog posts, or even play a game. This project is continuously being developed, and I hope you enjoy exploring it!

In this project, we tried to developed a scanner that can identify security vulnerabilities across given IP addresses and websites. By integrating LLM support using GPT-4, the scanner is able to assist in detecting potential vulnerabilities more intelligently. It is also designed to recognize different types of systems such as databases, game servers, and IoT and OT applications, helping to uncover security weaknesses across a wide range of environments.

Phishing Killer V7 is a machine learning model with GUI, designed for phishing detection. It is based on Customized LinearSVC and trained with approximately 85K data as combination of six different dataset. It has a 98% accuracy score on test dataset with 0.98 Recall and Precision.

We developed a medium-interaction honeypot specifically designed to capture XSS and SQL injection attacks. The honeypot uses realistic data and content to increase credibility and improve attacker engagement. A dedicated logging system was implemented to categorize and record attacks based on their type.