DeepMIA: An Integrated and Accelerated approach for Malicious Insider Attack Detection in IOT using Deep Learning

Abstract

Abstract: The Internet of Things (IoT) are poised to transform our lives and are becoming increasingly popular in smart homes, smart industrial networks. IoT devices can be used for a variety of purposes, including healthcare. Always, IoT device security is an issue because they are in charge of creating and handling large amounts of sensitive data. A security breach has been found to have an influence on people and eventually, the entire planet. Artificial intelligence (AI) has a greater range of applications and is currently being investigated for use in IoT device security. A malicious insider attack is the most serious security concern associated with IoT devices. Although much IoT security research has focused on ways to prevent unauthorized and unlawful access to systems and information, the most severe malicious insider attacks, which are often the result of internal attack within an IoT network or environment, have gone unnoticed. Here we have proposed a model called ‘DeepMIA’, which uses Deep Learning to detect dangerous insider attacks in the IoT context. This in resource-constrained IoT contexts, the research proposes a lightweight technique for detecting insider assaults that can detect abnormalities arising from sensors data or device data that are connected in a IoT Environment. The DeepMIA model is evaluated with UNSW-NB15 Dataset and achieves a decent accuracy of 99% with deep learning models