Towards a secure behavior modeling for IoT networks using Blockchain
This addresses security vulnerabilities in IoT networks for applications like smart devices, but it is incremental as it combines existing technologies like blockchain and deep learning.
The paper tackles security threats in IoT networks by proposing a blockchain-based behavior modeling system that uses a custom Behavior Monitor and deep learning to analyze device activities, achieving high accuracy and reduced detection time against Mirai attacks.
Internet of Things (IoT) occupies a vital aspect of our everyday lives. IoT networks composed of smart-devices which communicate and transfer the information without the physical intervention of humans. Due to such proliferation and autonomous nature of IoT systems make these devices threatened and prone to a severe kind of threats. In this paper, we introduces a behavior capturing, and verification procedures in blockchain supported smart-IoT systems that can be able to show the trust-level confidence to outside networks. We defined a custom \emph{Behavior Monitor} and implement on a selected node that can extract the activity of each device and analyzes the behavior using deep machine learning strategy. Besides, we deploy Trusted Execution Technology (TEE) which can be used to provide a secure execution environment (enclave) for sensitive application code and data on the blockchain. Finally, in the evaluation phase we analyze various IoT devices data that is infected by Mirai attack. The evaluation results show the strength of our proposed method in terms of accuracy and time required for detection.