Deep Learning enabled cyber threat intelligence framework for IoT networks

Abstract

The surging number of inherently vulnerable Internet of Things (IoT) devices necessitates robust security solutions as traditional CTI methods struggle with the dynamic threat landscape. This paper proposes a groundbreaking deep learning-enabled CTI framework specifically designed for IoT networks. The framework leverages powerful techniques like 1D convolution and autoencoders to dissect vast amounts of data from various network sources, unearthing subtle anomalies indicative of impending cyber threats like unusual traffic patterns. Additionally, boosting algorithms elevate the precision of predictive models, enabling proactive threat identification. This demonstrably bolsters threat detection and prevention, empowering organizations to fortify their infrastructure, mitigate cyber risks, and safeguard the integrity and functionality of their connected devices.