Design and Analysis of Multilayered Neural Network-Based Intrusion Detection System in the Internet of Things Network

Abstract

A large array of objects is networked together under the sophisticated concept known as the Internet of Things (IoT). These connected devices collect crucial information that could have a big impact on society, business, and the entire planet. In hostile settings like the internet, the IoT is particularly susceptible to multiple threats. Standard high-end security solutions are insufficient for safeguarding an IoT system due to the low processing power and storage capacity of IoT devices. This emphasizes the demand for scalable, distributed, and long-lasting smart security solutions. Deep learning excels at handling heterogeneous data of varying sizes. In this study, the transport layer of IoT networks is secured using a multilayered security approach based on deep learning. The created architecture uses the intrusion detection datasets from CIC-IDS-2018, BoT-IoT, and ToN-IoT to evaluate the suggested multi-layered approach. Finally, the new design outperformed the existing methods and obtained an accuracy of 98% based on the examined criteria.

Context-aware for predicting gestational diabetes using rule – based system

“…In this paper the author suggests using inexpensive wireless sensors device intended to determine the stage of urine secreted in real-time, enabling the rapid identification of potential medical issues [14]. Healthcare systems in the context-aware field, are commonly researched by researchers [15,16] which is a combined system to contextualize sensor health information [17]. Nanotechnology could be characterized as tracking, replacing, constructing, and regulating the human biological systems based on a cellular level, and also using structures and molecular-level of materials [18].…”

Intrusion detection system to advance IoT security environment

“…Machine learning algorithms used [16], used to find anomalies in systems running on IoT networks, but it has some limitations like limited resources and limitation in energy and processing capacity. multi-layered security method [17], helps to monitor the IoT network to recognize the activities done by normal or malware user, it has limitation in processing power and only used for small size of data. A stacked sparse autoencoder network model [18] is presented for detecting purposes, but it has a limit constraint on the middle layer's sparsity.…”