CanCal: Towards Real-time and Lightweight Ransomware Detection and Response in Industrial Environments
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arXiv
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914928878682112 |
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| author | Wang, Shenao Dong, Feng Yang, Hangfeng Xu, Jingheng Wang, Haoyu |
| author_facet | Wang, Shenao Dong, Feng Yang, Hangfeng Xu, Jingheng Wang, Haoyu |
| contents | Ransomware attacks have emerged as one of the most significant cybersecurity threats. Despite numerous proposed detection and defense methods, existing approaches face two fundamental limitations in large-scale industrial applications: intolerable system overheads and notorious alert fatigue. To address these challenges, we propose CanCal, a real-time and lightweight ransomware detection system. Specifically, CanCal selectively filters suspicious processes by the monitoring layers and then performs in-depth behavioral analysis to isolate ransomware activities from benign operations, minimizing alert fatigue while ensuring lightweight computational and storage overhead. The experimental results on a large-scale industrial environment~(1,761 ransomware, ~3 million events, continuous test over 5 months) indicate that CanCal is as effective as state-of-the-art techniques while enabling rapid inference within 30ms and real-time response within a maximum of 3 seconds. CanCal dramatically reduces average CPU utilization by 91.04% (from 6.7% to 0.6%) and peak CPU utilization by 76.69% (from 26.6% to 6.2%), while avoiding 76.50% (from 3,192 to 750) of the inspection efforts from security analysts. By the time of this writing, CanCal has been integrated into a commercial product and successfully deployed on 3.32 million endpoints for over a year. From March 2023 to April 2024, CanCal successfully detected and thwarted 61 ransomware attacks, demonstrating the effectiveness of CanCal in combating sophisticated ransomware threats in real-world scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_16515 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CanCal: Towards Real-time and Lightweight Ransomware Detection and Response in Industrial Environments Wang, Shenao Dong, Feng Yang, Hangfeng Xu, Jingheng Wang, Haoyu Cryptography and Security Ransomware attacks have emerged as one of the most significant cybersecurity threats. Despite numerous proposed detection and defense methods, existing approaches face two fundamental limitations in large-scale industrial applications: intolerable system overheads and notorious alert fatigue. To address these challenges, we propose CanCal, a real-time and lightweight ransomware detection system. Specifically, CanCal selectively filters suspicious processes by the monitoring layers and then performs in-depth behavioral analysis to isolate ransomware activities from benign operations, minimizing alert fatigue while ensuring lightweight computational and storage overhead. The experimental results on a large-scale industrial environment~(1,761 ransomware, ~3 million events, continuous test over 5 months) indicate that CanCal is as effective as state-of-the-art techniques while enabling rapid inference within 30ms and real-time response within a maximum of 3 seconds. CanCal dramatically reduces average CPU utilization by 91.04% (from 6.7% to 0.6%) and peak CPU utilization by 76.69% (from 26.6% to 6.2%), while avoiding 76.50% (from 3,192 to 750) of the inspection efforts from security analysts. By the time of this writing, CanCal has been integrated into a commercial product and successfully deployed on 3.32 million endpoints for over a year. From March 2023 to April 2024, CanCal successfully detected and thwarted 61 ransomware attacks, demonstrating the effectiveness of CanCal in combating sophisticated ransomware threats in real-world scenarios. |
| title | CanCal: Towards Real-time and Lightweight Ransomware Detection and Response in Industrial Environments |
| topic | Cryptography and Security |
| url | https://arxiv.org/abs/2408.16515 |