Abstract
The number of Internet of Things (IoT) devices is growing at a fast pace in smart homes, producing large amounts of data, which are mostly transferred over wireless communication channels. However, various IoT devices are vulnerable to different threats, such as cyber-attacks, fluctuating network connections, leakage of information, etc. Statistical analysis and machine learning can play a vital role in detecting the anomalies in the data, which enhances the security level of the smart home IoT system which is the goal of this paper. This paper investigates the trustworthiness of the IoT devices sending house appliances’ readings, with the help of various parameters such as feature importance, root mean square error, hyper-parameter tuning, etc. A spamicity score was awarded to each of the IoT devices by the algorithm, based on the feature importance and the root mean square error score of the machine learning models to determine the trustworthiness of the device in the home network. A dataset publicly available for a smart home, along with weather conditions, is used for the methodology validation. The proposed algorithm is used to detect the spamicity score of the connected IoT devices in the network. The obtained results illustrate the efficacy of the proposed algorithm to analyze the time series data from the IoT devices for spam detection.
Highlights
IntroductionAdvanced metering infrastructure (AMI) is one of the most principal components of the smart grid, and it is comprised of hardware (smart meters) and software (data management systems and communication networks) components
Advanced metering infrastructure (AMI) is one of the most principal components of the smart grid, and it is comprised of hardware and software components
As the focus of this paper is to assign a spamicity score to the Internet of Things (IoT) devices, this section focuses on the data handling procedures, statistical insights, detection of anomalies in the data, and Machine learning (ML) models used for the prediction, followed by the algorithm deployed to calculate the spamicity score
Summary
Advanced metering infrastructure (AMI) is one of the most principal components of the smart grid, and it is comprised of hardware (smart meters) and software (data management systems and communication networks) components. An AMI has structural similarities to a communication network; techniques utilized in communication networks to combat privacy breaches, malicious activities, and monetary gain can be applied in the field of power grids [1]. Among the various types of smart grid threats, those concerning smart meters involve threats to network hub (poor isolation between meters power-line communication (PLC), and the smart meter’s outlet), distributor’s servers, link, management networks (user injecting frames supplanting the network hub identity), firmware updates, and hardware manipulation. In the case of the complete AMI, except for the displays, all the operations are vulnerable to alterations such as protocol design, network initialization, and key management and pose a threat to the AMI infrastructure.
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