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Internet of Things (IoT) devices have become ubiquitous and are spread across many application domains including the industry, transportation, healthcare, and households. However, the proliferation of the IoT devices has raised the concerns about their security, especially when observing that many manufacturers focus only on the core functionality of their products due to short time to market and low-cost pressures, while neglecting security aspects. Moreover, it does not exist any established or standardized method for measuring and ensuring the security of IoT devices. Consequently, vulnerabilities are left untreated, allowing attackers to exploit IoT devices for various purposes, such as compromising privacy, recruiting devices into a botnet, or misusing devices to perform cryptocurrency mining. In this paper, we present a practical Host-based Anomaly DEtection System for IoT (HADES-IoT) that represents the last line of defense. HADES-IoT has proactive detection capabilities, provides tamper-proof resistance, and it can be deployed on a wide range of Linux-based IoT devices. The main advantage of HADES-IoT is its low performance overhead, which makes it suitable for the IoT domain, where state-of-the-art approaches cannot be applied due to their high-performance demands. We deployed HADES-IoT on seven IoT devices to evaluate its effectiveness and performance overhead. Our experiments show that HADES-IoT achieved 100% effectiveness in the detection of current IoT malware such as VPNFilter and IoTReaper; while on average, requiring only 5.5% of available memory and causing only a low CPU load.
IoT devices are increasingly deployed in daily life. Many of these devices are, however, vulnerable due to insecure design, implementation, and configuration. As a result, many networks already have vulnerable IoT devices that are easy to compromise.
This work investigates the possibilities enabled by federated learning concerning IoT malware detection and studies security issues inherent to this new learning paradigm. In this context, a framework that uses federated learning to detect malware af
Due to their rapid growth and deployment, the Internet of things (IoT) have become a central aspect of our daily lives. Unfortunately, IoT devices tend to have many vulnerabilities which can be exploited by an attacker. Unsupervised techniques, such
Internet of Things (IoT) devices have been increasingly integrated into our daily life. However, such smart devices suffer a broad attack surface. Particularly, attacks targeting the device software at runtime are challenging to defend against if IoT
The internet of things refers to the network of devices connected to the internet and can communicate with each other. The term things is to refer non-conventional devices that are usually not connected to the internet. The network of such devices or