Malware Squid: A Novel IoT Malware Traffic Analysis Framework using Convolutional Neural Network and Binary Visualisation
This addresses security threats for IoT networks, which are vulnerable due to resource limitations and evolving malware, though it appears incremental in applying existing techniques to a specific domain.
The paper tackled the problem of detecting unknown malware in IoT devices by introducing a novel traffic analysis framework using convolutional neural networks and binary visualization, achieving accuracy that meets practical application requirements.
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.