一句话:提出勒索软件感知 SSD RSSD,用硬件辅助日志、远端安全卸载和攻击后证据链防御新型勒索攻击,性能开销极低。
加密勒索软件会加密 SSD 上的用户数据并索要赎金,且不断演化出垃圾回收(GC)攻击、计时攻击和 trim 攻击等新模型来绕过现有防御,分别用于释放保留数据、隐藏 I/O 模式和物理擦除数据。RSSD 是一种勒索软件感知 SSD,重新设计 flash 管理以低开销实现硬件辅助日志,按时间顺序保守保留用户数据旧版本与存储操作,并利用硬件隔离的 NVMe over Ethernet 将日志安全卸载到远端云/服务器。日志构成可信证据链,支持攻击后分析。作者在真实 SSD FPGA 板上实现并评估,显示 RSSD 可防御新型与未来勒索攻击,性能开销可忽略。
Encryption ransomware has become a notorious malware. It encrypts user data on storage devices like solid-state drives (SSDs) and demands a ransom to restore data for users. To bypass existing defenses, ransomware would keep evolving and performing new attack models. For instance, we identify and validate three new attacks, including (1) garbage-collection (GC) attack that exploits storage capacity and keeps writing data to trigger GC and force SSDs to release the retained data; (2) timing attack that intentionally slows down the pace of encrypting data and hides its I/O patterns to escape existing defense; (3) trimming attack that utilizes the trim command available in SSDs to physically erase data. To enhance the robustness of SSDs against these attacks, we propose RSSD, a ransomware-aware SSD. It redesigns the flash management of SSDs for enabling the hardware-assisted logging, which can conservatively retain older versions of user data and received storage operations in time order with low overhead. It also employs hardware-isolated NVMe over Ethernet to expand local storage capacity by transparently offloading the logs to remote cloud/servers in a secure manner. RSSD enables post-attack analysis by building a trusted evidence chain of storage operations to assist the investigation of ransomware attacks. We develop RSSD with a real-world SSD FPGA board. Our evaluation shows that RSSD can defend against new and future ransomware attacks, while introducing negligible performance overhead.
DOI 原文 · arXiv · PDF(开放获取) · DBLP
@inproceedings{ReidysL022,
title = {RSSD: defend against ransomware with hardware-isolated network-storage codesign and post-attack analysis},
author = {Benjamin Reidys and Peng Liu and Jian Huang},
booktitle = {ASPLOS},
year = {2022},
doi = {10.1145/3503222.3507773}
}