Malware-Detection-and-Analysis-using-Machine-Learning and Android-Malware-Detection
About Malware-Detection-and-Analysis-using-Machine-Learning
0xfke/Malware-Detection-and-Analysis-using-Machine-Learning
Malware🦠 Detection and Analysis using Machine Learning (MDAML) is designed to provide users with an intuitive interface for analyzing and detecting malware in various file formats.
This tool helps cybersecurity analysts and IT security professionals quickly assess files, URLs, and executables for malware. You provide a suspicious file (like an EXE or DLL), a URL, or a file hash, and it uses external threat intelligence and machine learning to determine if it's malicious. The output is a clear report indicating whether a threat is detected and why.
About Android-Malware-Detection
vannu07/Android-Malware-Detection
Android Malware Detection is a machine learning-based security tool designed to identify and classify malicious Android applications. The project leverages advanced ML algorithms to analyze Android APK files and detect potential malware threats, helping to protect users from malicious software.
This project helps cybersecurity analysts and mobile security researchers automatically identify malicious Android applications. You feed it Android Package Kit (APK) files, and it tells you whether each app is safe or contains malware, along with explanations for its decision. It's designed for professionals who need to quickly assess the security risk of unknown Android apps without manually inspecting their code.
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work