An efficient network optimized machine learning architecture framework for detection of malwares in IOT (NB-IOT) systems
Abstract
The Internet of things (IoT) is a wirelessly interconnected network of electrical gadgets. Due to their necessity of vast volumes of data in a single entity, the centralized machine learning (ML)-assisted systems that are widely used are difficult to use. Researchers and academics have a challenging issue about security and privacy in the IoT system. To overcome this issue, the paper presents Network optimised with machine learning classification is proposed. In terms of delay, security, accessibility, data transfer rate, energy consumption, spectral effectiveness, and coverage area, IoT and other wireless and mobile communication technologies function effectively. K-nearest neighbour (KNN) is one of the machine learning algorithms based on supervised learning technique is proposed. In specific applications, the proposed K nearest neighbour algorithm, is more accurate than existing methods such as decision tree (DT), random forest (RF), and support vector machine (SVM), and can be used to improve malware detection accuracy and also used to detect malware in NB-IoT. Using Aposemat IoT-23 datasets and assessment criteria including malware detection accuracy, recall, precision, and F1-score, the suggested technique was assessed. It was shown to be more accurate than competing methods and to increase security levels.
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DOI: https://doi.org/10.32629/jai.v7i5.1635
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