Configuring and Implementing IPS Solutions for IoT Devices using NST
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Abstract
The necessity to ensure that Internet of Things (IoT) networks are secure is one of the biggest issues that has arisen as a result of the growing demand for technology that uses the IoT. Considering how many gadgets are linked to the internet, safeguarding their networks is a growing worry. Due to the IoT's network's complexity and resource constraints, traditional intrusion detection systems encounter a number of problems. The main objectives of this project are to design, develop, and evaluate a hybrid level placement method for an IDS based on multi- agent systems, BC technology (Block-Chain), and DL algorithms (Deep Learning). The breakdown of data administration, data collection, analysis, and reaction into its component parts reveals the overall system design. The National Security Laboratory's knowledge discovery and data mining dataset is used to test the system as part of the validation procedure. These results demonstrate how deep learning algorithms are effective at identifying risks at the network and transport levels. The experiment shows that deep learning techniques function well when used to find intrusions in a network environment for the Internet of Things.
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