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Enterprise Internet of Things (E-IoT) systems allow users to control audio, video, scheduled events, lightning fixtures, door access, and relays in complex smart installations. These systems are widely used in government or smart private offices, smart buildings/homes, conference rooms, schools, hotels, and similar professional settings. However, even with their widespread use, the security of many E-IoT systems and components has not been researched in the literature. To address this research gap, we focus on E-IoT communication buses, one of the core components used to connect E-IoT devices, and introduce LightningStrike attacks that demonstrate several weaknesses with E-IoT proprietary communication protocols used in E-IoT communication buses. Specifically, we show that popular E-IoT proprietary communication protocols are susceptible to Denial-of-Service (DoS), eavesdropping, impersonation, and replay attacks. As such threats cannot be mitigated through traditional defense mechanisms due to the limitations posed by E-IoT, we propose LGuard, a defense system to protect E-IoT systems against the attacks over communication buses. LGuard uses closed-circuit television footage and computer vision techniques to detect replay attacks. For impersonation and DoS attacks, LGuard utilizes traffic analysis. Finally, LGuard obfuscates the E-IoT traffic via inserting redundant traffic to the bus against eavesdropping attacks. We evaluated the performance of LGuard in a realistic E-IoT deployment, and our detailed evaluations show that LGuard achieves an overall accuracy and precision of 99% in detecting DoS, impersonation, and replay attacks while effectively increasing the difficulty of extracting valuable information for eavesdroppers. In addition, LGuard does not incur any operational overhead or modification to the existing E-IoT system. [ABSTRACT FROM AUTHOR]

Recently, there has been a substantial interest in on-device Machine Learning (ML) models to provide intelligence for the Internet of Things (IoT) applications such as image classification, human activity recognition, and anomaly detection. Traditionally, ML models are deployed in the cloud or centralized servers to take advantage of their abundant computational resources. However, sharing data with the cloud and third parties degrades privacy and may cause propagation delay in the network due to a large amount of transmitted data impacting the performance of real-time applications. To this end, deploying ML models on-device (i.e., on IoT devices), in which data does not need to be transmitted, becomes imperative. However, deploying and running ML models on already resource-constrained IoT devices is challenging and requires intense energy consumption. Numerous works have been proposed in the literature to address this issue. Although there are considerable works that discuss energy-aware ML approaches for on-device implementation, there remains a gap in the literature on a comprehensive review of this subject. In this paper, we provide a review of existing studies focusing on-device ML models for IoT applications in terms of energy consumption. One of the key contributions of this study is to introduce a taxonomy to define approaches for employing energy-aware on-device ML models on IoT devices in the literature. Based on our review in this paper, our key findings are provided and the open issues that can be investigated further by other researchers are discussed. We believe that this study will be a reference for practitioners and researchers who want to employ energy-aware on-device ML models for IoT applications. [ABSTRACT FROM AUTHOR]