Machine Learning Algorithms to Detect Deepfakes Fine Tuned for Anomaly Detection of IoT

Introduction

Internet of Things refers to the network of physical objects that are capable of exchanging and requesting data from one another or in relation to the external environment through the utilization of technology that is incorporated into the design of the object. People will be able to take care of daily routines in different ways and give more complex administrations as a result of developments based on the Internet of Things in the years to come. Among the most evident domains in which IoT has been unquestionably established are the pharmaceutical business, the electricity sector, the production of high-quality pharmaceuticals, agriculture, sophisticated urban networks, and sophisticated residences (Farouk, et al., 2018). There are approximately 9 billion ‘Things’ (real-world, living items) that are connected to the internet at this same moment. It is anticipated that by the end of this decade, this number will have skyrocketed to a staggering 20 billion (Farouk, et al., 2020). When compared to the entire planet for a tiny town that is universally related by just going from one side of the globe to the other, the definition of the Internet of Things consists of just two words that clearly clarify what it means (Aoudni et al., 2022). There has been a significant increase in the use of the Internet of Things across many different areas, including the medical field, the information technology sector, and the agricultural sector (Farouk et al., 2015). The ability to provide assurance of safety is likely the most important factor, given that it is the factor that is at the heart of a variety of problems, including government enterprise (Adil et al., 2021).

Any deviation from the norm, often known as an anomaly, might serve as an early warning sign for impending trouble (Heidari, et al., 2019). A problem in a manufacturing unit, for instance, may be indicated by abnormalities in the time-series data collected by an Internet of Things sensor (Mendonça, et al., 2021). Despite this, the process of spotting anomalies has become significantly more difficult over time. The application of techniques from machine learning allows for the detection of anomalies in data. Unsupervised, semi-supervised, and supervised anomaly detection methods are the three categories that make up the overall anomaly detection landscape (Naseri, et al., 2018). Labels in a dataset indicate the most appropriate detection strategy. In this paper, a methodology for finding irregularities in IoT devices is proposed in order to identify them and alert the top management or senior managers in a business. This approach is proposed in order to identify them and bring them to the attention of the readers. We make use of a machine learning approach called K-Nearest Neighbor (KNN) in conjunction with a Random Forest (RF) algorithm in order to fine-tune the parameters of the spreading network. As a result, this framework improves the performance of the model without causing it to overfit, and it makes it possible to get a fit and a measure score by making use of cross-validation (CV). After that, look into the many discrepancies that are caused by the dataset’s use of substitute components. It is possible to utilize both a binary and a multi-class machine learning classification model to recognize dangers and abnormalities that may be present in an Internet of Things environment (Zhu, et al., 2021). After then, the study that is being proposed would test the model by attempting to predict the class of provided data points contained in the dataset. When this step is complete, the accuracy of the classifier is determined by dividing the total number of test models by the number of correct selections (Metwaly, et al., 2014). Finally, with the help of this research, we will be able to prevent attacks by identifying new dangers and anomalies in the Internet of Things configurations and smart devices. Following that, a description of the literature review that was carried out for this paper is presented. Following that, the following section of this study will detail our proposed approach and module. At this point, the report comes to an end with a succinct conclusion about an analysis of the results.