.png){kind=icon}
返回
A Framework for Anomaly Detection in Time-Driven and Event-Driven Processes Using Kernel Traces
.png){kind=icon}
PRE
AI
DOI：10.1109/TKDE.2020.2978469.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Model-checking and verification using Kripke structures and computational tree logic* (CTL*) use abstractions from the model/process/application to create the state-transition graphs that verify the model behavior. This scheme of profiling the performance of a process imports that the depth of the process operation correlates with the level abstraction. However, because of state explosion problems, these abstractions tend to restrict the scope to create manageable execution states. Therefore, for context modeling, this procedure does not generate a fine-grained behavioral model as generated states limit the ability of the abstraction to capture the execution time interactions amongst the processes, the hardware, and the kernel. Hence, in this paper, we present an end-to-end framework that comprises auto-encoders and probabilistic models to understand the behavior of system processes and detect deviant behaviors. We test this framework with a publicly available dataset generated from an autonomous aerial vehicle (UAV) application and the results show that by creating a fine-grained model that exploits previously unharnessed properties of the system calls, we can create a dynamic anomaly detection framework that evolves as the threats change.
Keyword:
Context modeling
unsupervised learning
anomaly detection
kernel events
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.4
论文数:
6.8K
被引数:
3.2W
机构
暂无机构信息
引用论文
分享收藏
DReAM: Deep Recursive Attentive Model for Anomaly Detection in Kernel EventsDReAM: 内核事件异常检测的深度递归注意模型.png)
IEEE ACCESS
OAAI
IEEE ACCESSIF3.6
OAAI分享收藏
查看更多内容