Ransom Access Memories: Achieving Practical Ransomware Protection in Cloud with DeftPunk
Zhongyu Wang, Yaheng Song, Erci Xu, Haonan Wu, Guangxun Tong, Shizhuo Sun, Haoran Li, Jincheng Liu, Lijun Ding, Rong Liu, Jiaji Zhu, Jiesheng Wu
摘要
In this paper, we focus on building a ransomware detection and recovery system for cloud block stores. We start by discussing the possibility of directly using existing methods or porting one to our scenario with modifications. These attempts, though failed, led us to identify the unique IO characteristics of ransomware, and further drove us to build DeftPunk, a block-level ransomware detection and recovery system. DeftPunk uses a two-layer classifier for fast and accurate detection, creates pre-/post-attack snapshots to avoid data loss, and leverages log-structured support for low overhead recovery. Our large-scale benchmark shows that DeftPunk can achieve nearly 100% recall across 13 types of ransomware and low runtime overhead.
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引用它的顶会 Paper3
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它引用的顶会 Paper4
- FlashGuard: Leveraging Intrinsic Flash Properties to Defend Against Encryption RansomwareJian Huang, Jun Xu, Xinyu Xing, Peng Liu 等CCS 2017 · 被引用 94 次
- RSSD: defend against ransomware with hardware-isolated network-storage codesign and post-attack analysisBenjamin Reidys, Peng Liu, Jian HuangASPLOS 2022 · 被引用 28 次
- More Than Capacity: Performance-oriented Evolution of Pangu in AlibabaQiang Li, Qiao Xiang, Yuxin Wang, Haohao Song 等FAST 2023
- UNVEIL: A Large-Scale, Automated Approach to Detecting RansomwareAmin Kharraz, Sajjad Arshad, Collin Mulliner, William K. Robertson 等USENIX Security 2016
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