RDE-Dataset.zipRansomware Defense Empowered: Deep Learning for Real-Time Family Identification with a Proprietary Dataset

Ransomware, leveraging sophisticated encryption techniques, poses a significant threat by encrypting crucial data, thereby rendering it inaccessible. The proliferation of diverse ransomware variants has caused considerable harm to governments, corporations, and individual users alike. Despite the increasing prevalence of cyber threats, existing solutions often struggle with real-time detection and early identification of ransomware families. To address this challenge, we introduce FCG-RFD, a novel benchmark dataset featuring extensive Function Call Graphs (FCG) tailored for ransomware family detection. Given the constantly evolving nature of malware, antivirus scanners face ongoing challenges, necessitating access to recent and updated datasets. Our dataset comprises 8,095 samples sourced from reputable repositories including VirusSamples, Virusshare, VirusSign, the Zoo, and MalwareBazaar. Additionally, we include 8,020 normal files obtained from trusted sources such as the Microsoft Store and Softonic. Through FCG-RFD, we aim to facilitate more robust and timely detection of ransomware families, ultimately enhancing cybersecurity measures against this pervasive threat.