  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.
