In the field of cryptography, the substitution box (Sbox) becomes the most widely used ciphers. The process of creating new and powerful S-boxes never end. Various methods are proposed to make the S-box becomes strongest and hard to attack. The strength or weakness of S-box will be determined through the analysis of S-box properties. However, the analysis of the properties of the S-box in block ciphers is still lacking because there is no specific guidelines and technique based on Sbox properties. Hence, the cipher is easier to attack by an adversary if the S-box properties are not robust. The purpose of this paper is to describe and review of the S-box properties in block ciphers. As a result, for future work, a new model for analysis S-box properties will be proposed. The model can be used to analysis the properties to determine the strength and weakness of any S-boxes.

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.

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