KEYWORDS: Data modeling, Performance modeling, Internet of things, Education and training, Deep learning, Machine learning, Decision trees, Systems modeling, Feature extraction, Data fusion
In recent years Internet of Things (IoT) devices have made their way into many different industries. Deep learning and machine learning methodologies have been applied to many IoT-related tasks123 such as intrusion detection systems or anomaly detection. The efficiency of IoT systems is often hindered by anomalies in data present within the system, often leading to undesirable behavior or possibly a full system shutdown. Due to this, the detection of these anomalies is of the utmost importance. Over the years, various traditional and neural network-based machine learning models have emerged for anomaly detection and classification of corrupted IoT data. However, many of these models fail to capture important features in the data which can lead to false anomaly detection or none at all. In this paper we investigate the applicability of using data fusion to improve the detection of data anomalies. This method uses many different models, such as VGG16, Inception, Xception, and ResNet, to extract features from the data. These extracted features are then fused together, to see if the use of multiple models is better than relying on a single model. This paper also provides a detailed analysis of the efficacy of this fusion-based classification method compared to simpler classification methods. This work investigates the applicability of various machine learning and deep learning models, for anomaly detection in various IoT datasets45.
Zigbee is a popular specification for Internet of Things (IoT) mesh networking that provides a suite of protocols built on the IEEE 802.15.4 standard for radio communication. The Zigbee protocol stack is designed as series of layers each with a specific set of functions for communicating data throughout the network. These protocols provide a comprehensive functionality for performing various network tasks, including commissioning new networks and devices, performing broadcasting, unicasting, groupcasting with end-to-end acknowledgement, securing network traffic through AES-128 encryption, and full-packet message authentication. Security features of the Zigbee protocol alone may not be a complete solution for deploying secure IoT networks. It has some vulnerabilities and real-world attacks as discussed in this paper. Zigbee may be improved upon or added to for the purpose of securing it using real-time anomaly detection in IoT Local Area Networks (LANs).
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