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The Linux shell is a command-line interpreter that provides users with acommand interface to the operating system, allowing them to perform a varietyof functions. Although very useful in building capabilities at the edge, theLinux shell can be exploited, giving adversaries a prime opportunity to usethem for malicious activities. With access to IoT devices, malware authors canabuse the Linux shell of those devices to propagate infections and launchlarge-scale attacks, e.g., DDoS. In this work, we provide a first look at shellcommands used in Linux-based IoT malware towards detection. We analyzemalicious shell commands found in IoT malware and build a neural network-basedmodel, ShellCore, to detect malicious shell commands. Namely, we collected alarge dataset of shell commands, including malicious commands extracted from2,891 IoT malware samples and benign commands collected from real-world networktraffic analysis and volunteered data from Linux users. Using conventionalmachine and deep learning-based approaches trained with term- andcharacter-level features, ShellCore is shown to achieve an accuracy of morethan 99% in detecting malicious shell commands and files (i.e., binaries).

ShellCore: Automating Malicious IoT Software Detection by Using Shell Commands Representation