Peeler: Profiling Kernel-Level Events to Detect Ransomware

TL;DR

Peeler is a kernel-level monitoring system that detects ransomware by analyzing behavioral characteristics, achieving over 99% detection accuracy with minimal false positives across diverse ransomware families.

Contribution

This paper introduces Peeler, a novel kernel-level ransomware detection system that relies on behavioral profiling rather than signatures, enabling timely and accurate detection.

Findings

Over 99% detection rate against 43 ransomware families

Detection latency within 115 milliseconds for crypto ransomware

Low resource usage with 4.9% CPU and 9.8 MB memory

Abstract

Ransomware is a growing threat that typically operates by either encrypting a victim's files or locking a victim's computer until the victim pays a ransom. However, it is still challenging to detect such malware timely with existing traditional malware detection techniques. In this paper, we present a novel ransomware detection system, called "Peeler" (Profiling kErnEl -Level Events to detect Ransomware). Peeler deviates from signatures for individual ransomware samples and relies on common and generic characteristics of ransomware depicted at the kernel-level. Analyzing diverse ransomware families, we observed ransomware's inherent behavioral characteristics such as stealth operations performed before the attack, file I/O request patterns, process spawning, and correlations among kernel-level events. Based on those characteristics, we develop Peeler that continuously monitors a target system's kernel events and detects ransomware attacks on the system. Our experimental results show that Peeler achieves more than 99\% detection rate with 0.58\% false-positive rate against 43 distinct ransomware families, containing samples from both crypto and screen-locker types of ransomware. For crypto ransomware, Peeler detects them promptly after only one file is lost (within 115 milliseconds on average). Peeler utilizes around 4.9\% of CPU time with only 9.8 MB memory under the normal workload condition. Our analysis demonstrates that Peeler can efficiently detect diverse malware families by monitoring their kernel-level events.