zkcplk/Federe_Ogrenme_ile_Malware_Tespiti
Marmara Üniversitesi, Fen Bilimleri Enstitüsü, Bilgisayar Mühendisliği bölümünde sunulan “DERİN SİNİR AĞLARI İLE FEDERE ÖĞRENME TABANLI BİR KÖTÜ AMAÇLI YAZILIM TESPİT UYGULAMASI” başlıklı tez çalışmasına ait kaynak kodlarıdır.
This project provides a system for detecting and classifying malware using deep neural networks within a federated learning framework. It helps organizations enhance their cybersecurity defenses by collaboratively learning from various data sources without centralizing sensitive information. Security analysts and IT professionals can use this to process network traffic or file samples and identify malicious software.
No commits in the last 6 months.
Use this if you need to build or enhance a malware detection system that leverages machine learning while prioritizing data privacy and decentralized data processing.
Not ideal if you are looking for an off-the-shelf, ready-to-deploy commercial antivirus solution for end-users.
Stars
10
Forks
—
Language
Python
License
—
Category
Last pushed
Jul 16, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/zkcplk/Federe_Ogrenme_ile_Malware_Tespiti"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
rednaga/APKiD
Android Application Identifier for Packers, Protectors, Obfuscators and Oddities - PEiD for Android
0xfke/Malware-Detection-and-Analysis-using-Machine-Learning
Malware🦠 Detection and Analysis using Machine Learning (MDAML) is designed to provide users with...
rieck/malheur
A Tool for Automatic Analysis of Malware Behavior
AFAgarap/malware-classification
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support...
CalebFenton/apkfile
Android app analysis and feature extraction library