Transforming Malware Behavioural Dataset for Deep Denoising Autoencoders
Mohd Razif Shamsuddin, Fakariah Hani Mohd Ali, Mohd Shahril Bin Zainol Abidin · IOP Conference Series Materials Science and Engineering · 2020
Abstract This research is a part of a major research on automation of malware identification using Deep Denoising Autoencoders. Malicious software, or in short called malware refers to any software designed to cause damage to a single computer, server, or computer network. This malware term includes all kind of malicious software such as computer virus and spyware. All these malicious malware behaviour is monitored, logged and recorded using a cuckoo sandbox with the help of an x86 hosted supervisor software. The intent of recording the malware behaviour is to understand the pattern of behaviour of each known malware family. This collected data will be further trained to a Deep Denoising Autoencoders to automate the identification process of new malware within the identified malware families. However, the raw behaviour data is not suitable for an optimum training process. This paper will discuss the process of transforming the text based behavioural dataset to a more suitable dataset for deep learning purposes. At the end of the research a cleaned bit string format that should represent a unique malware behaviour will be produced.