Behavior-based Malware Detection using Deep Learning for Improve Security of IoT Infrastructure

Abstract

Background/Objectives: Recent advances in Internet of Things (IoT) technologies require a new type of IoT security environment. Various heterogeneous smart devices have easy access to IoT environment, and as the number of users increases, they are exposed to various threats such as malicious attacks on IoT devices and IoT infrastructure, and data tampering by malicious code. Malware detection in IoT requires data and models for continuous and changing learning of smart devices.

Methods/Statistical analysis: To minimize these security threats, various malware detection techniques in the field of IoT security have been studied. Malware detection in IoT environment is important for data derivation and learning model required for continuous and changing learning of smart devices. The metadata of malware detection can be normalized by the value of device id, time, behavior, location and state. This paper proposes behavior-based malware detection using deep learning (BMD-DL).

Findings: BMD-DL was able to collect metadata about behavior-based malicious behavior and learn and detect malicious codes through deep learning. In addition, through the learned model, IoT Security is provided by disconnecting malicious devices that cause malicious behavior in the IoT environment.

Improvements/Applications: BMD-DL collects behavioral data generated from multiple devices in the IoT and applies the results learned through deep learning to detect persistent malware.

Keywords: Internet of Things Infrastructure,IoT Security,Malware Detection, Malicious Behavior Detection, Deep Learning, Behavior-based Data Collection