Keywords

IoT, embedded systems, malware detection, machine learning

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Abstract

Embedded devices are increasingly connected to the Internet to provide new and innovative applications in many domains. However, these devices can also contain security vulnerabilities, which allow attackers to compromise them using malware. In this paper, we present SIMBIoTA-ML, a light-weight antivirus solution that enables embedded IoT devices to take advantage of machine learning-based malware detection. We show that SIMBIoTA-ML can respect the resource constraints of embedded IoT devices, and it has a true positive malware detection rate of ca. 95%, while having a low false positive detection rate at the same time. In addition, the detection process of SIMBIoTA-ML has a near-constant running time, which allows IoT developers to better estimate the delay introduced by scanning a file for malware, a property that is advantageous in real-time applications, notably in the domain of cyber-physical systems.

Keywords

embedded systems, Internet of Things, security, malware

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Abstract

IoT systems are subject to cyber attacks, including infecting embedded IoT devices with rootkits. Rootkits are malicious software that typically run with elevated privileges, which makes their detection challenging. In this paper, we address this challenge: we propose a rootkit detection approach for embedded IoT devices that takes advantage of a trusted execution environ- ment (TEE), which is often supported on popular IoT platforms, such as ARM based embedded boards. The TEE provides an isolated environment for our rootkit detection algorithms, and prevents the rootkit from interfering with their execution even if the rootkit has root privileges on the untrusted part of the IoT device. Our rootkit detection algorithms identify modifications made by the rootkit to the code of the operating system kernel, to system pro- grams, and to data influencing the control flow (e.g., hooking system calls), as well as inconsistencies created by the rootkit in certain kernel data struc- tures (e.g., those responsible to handle process related information). We also propose algorithms to detect rootkit components in the persistent storage of the device. Besides describing our approach and algorithms in details, we also report on a prototype implementation and on the evaluation of our design and implementation, which is based on testing our prototype with rootkits that we developed for this purpose.

Keywords

Internet of Things, embedded systems, malware, remote attestation, Trusted Execution Environment

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Abstract

The Internet of Things (IoT) consists of network-connected embedded devices that enable a multitude of new applications, but also create new risks. In particular, embedded IoT devices can be infected by malware. Operators of IoT systems not only need malware detection tools, but also scalable methods to reliably and remotely verify malware freedom of their IoT devices. In this paper, we address this problem by proposing T-RAID, a remote attestation scheme for IoT devices that takes advantage of the security guarantees provided by a Trusted Execution Environment running on each device.

Abstract

Rootkits are malicious programs that try to maintain their presence on infected computers while remaining invisible. They have been used to attack traditional computers (PCs and servers), but they may also target embedded IoT devices. In this work, we propose a rootkit detection approach for such embedded IoT devices, where the detection mechanism is executed in an isolated execution environment that protects it from manipulation by the rootkit. Our rootkit detection approach is focused on detecting Direct Kernel Object Manipu- lation (DKOM) and it is based on detecting inconsistencies caused by the presence of a rootkit in various Linux kernel data structures such as the process list, the process tree, and different scheduling queues. We also report on the current status of our implementation using OP-TEE, an open source Trusted Execution Environment.