.png){kind=icon}
返回
SHELLCORE: Automating Malicious IoT Software Detection Using Shell Commands Representation
.png){kind=icon}
OA
AI
DOI：10.1109/JIOT.2021.3086398.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
The Linux shell is a command-line interpreter that provides users with a command interface to the operating system, allowing them to perform various functions. Although very useful in building capabilities at the edge, the Linux shell can be exploited, giving adversaries a prime opportunity to use them for malicious activities. With access to Internet of Things (IoT) devices, malware authors can abuse the Linux shell of those devices to propagate infections and launch large-scale attacks, e.g., Distributed Denial of Service. In this work, we provide a first look at the tasks managed by shell commands in Linux-based IoT malware toward detection. We analyze malicious shell commands found in IoT malware and build a neural network-based model, ShellCore, to detect malicious shell commands. Namely, we collected a large data set of shell commands, including malicious commands extracted from 2891 IoT malware samples and benign commands collected from real-world network traffic analysis and volunteered data from Linux users. Using conventional machine and deep learning-based approaches trained with a term- and character-level features, ShellCore is shown to achieve an accuracy of more than 99% in detecting malicious shell commands and files (i.e., binaries).
Keyword:
Internet of Things (IoT) security
Linux shell commands
machine learning
malware detection
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:
8.9
论文数:
1.4W
被引数:
7.8W
机构
引用论文
Highly specific colorimetric detection based on aggregation of l-cysteine functionalized gold nanoparticles for cypermethrin in water samples.png)
RSC Advances
OAAI
RSC AdvancesIF0
OAAI分享收藏
查看更多内容