Real-Time Malware Detection on IoT Devices using Behavior-Based Analysis and Neural Networks

Abstract

IoT devices' constrained processing capabilities and malware's changing nature make traditional signature-based methods for malware detection ineffective. The focus of our suggested approach, in contrast, is on real-time analysis of IoT device behaviour patterns to find anomalies that might be signs of malicious activity. We can spot differences from typical behaviour on devices by continuously observing how they behave. These differences could indicate the existence of malware. We use deep neural networks to handle and analyse the enormous quantity of data produced by IoT devices in order to do this. Specifically, we use recurrent neural networks (RNNs) and convolutional neural networks (CNNs). These neural networks learn the anticipated behaviours of various IoT devices and their applications through training on historical data. They quickly detect unexpected behaviours that can be a sign of malware infestations or other harmful actions by comparing incoming data streams to these learned patterns in real-time. By reaching high detection rates while preserving low false-positive rates, our experimental results show the efficiency of the suggested approach. We can greatly improve the security posture of IoT devices or gateways by integrating this real-time malware detection technology into them, defending against new attacks in the ever-changing IoT landscape. By protecting the privacy and integrity of IoT-enabled environments, our research will help to mitigate the escalating cybersecurity challenges faced by IoT devices.