2024 journal article
RD-FAXID: Ransomware Detection with FPGA-Accelerated XGBoost
ACM TRANSACTIONS ON RECONFIGURABLE TECHNOLOGY AND SYSTEMS, 17(4).
author keywords: FPGAs; XGBoost; Binary Classification; High-Level Synthesis; Ransomware; Hardware Performance Counters; Accelerators; Machine Learning
open access via dl.acm.org (publisher PDF)
Source: Web Of Science
Added: December 16, 2024
Over the last decade, there has been a rise in cyberattacks, particularly ransomware, causing significant disruption and financial repercussions across public and private sectors. Tremendous efforts have been spent on developing techniques to detect ransomware to, ideally, protect data or have as minimum data loss as possible. Ransomware attacks are becoming more frequent and sophisticated as there is a constant tussle between attackers and cybersecurity defenders. Machine Learning (ML) approaches have proven more effective in detecting ransomware than classical signature-based detection. In particular, tree-based algorithms such as Decision Trees (DT), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) spike up interest among cybersecurity researchers. However, due to the nature of the problem, traditional CPUs and GPUs fail to keep up with the desired performance, especially for large data workloads. Thus, the problem demands a customized solution to detect the ransomware. Here, we propose an FPGA accelerated tree-based ML model for multi-dataset ransomware detection. We show the capability of the proposed prototype to address the problem from more than one set of features, reducing false positive and negative rates to have robust predictions by looking at Hardware Performance Counters (HPCs), Operating System (OS) calls, and network traffic information simultaneously. With 1,000 samples per batch, the FPGA prototype has 65.8 \({\times}\) and 4.1 \({\times}\) lower latency over the CPU and GPU, respectively. Moreover, the FPGA design is up to 11.3 \({\times}\) cost-effective and 643 \({\times}\) energy-efficient compared to the CPU and 3 \({\times}\) cost-effective and 16.8 \({\times}\) energy-efficient over the GPU.