Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation

TL;DR

This paper introduces Malware Squid, a new IoT malware traffic analysis framework that leverages convolutional neural networks and binary visualization to detect and classify malware, including zero-day threats, more effectively.

Contribution

It presents a novel approach combining CNN and binary visualization for IoT malware detection, addressing limitations of traditional signature-based methods.

Findings

Achieves high accuracy in malware detection

Effective in identifying zero-day malware

Faster detection compared to traditional methods

Abstract

Internet of Things devices have seen a rapid growth and popularity in recent years with many more ordinary devices gaining network capability and becoming part of the ever growing IoT network. With this exponential growth and the limitation of resources, it is becoming increasingly harder to protect against security threats such as malware due to its evolving faster than the defence mechanisms can handle with. The traditional security systems are not able to detect unknown malware as they use signature-based methods. In this paper, we aim to address this issue by introducing a novel IoT malware traffic analysis approach using neural network and binary visualisation. The prime motivation of the proposed approach is to faster detect and classify new malware (zero-day malware). The experiment results show that our method can satisfy the accuracy requirement of practical application.