Ransomware has becoming a current trend of cyberattack where its reputation among malware that cause a massive amount recovery in terms of cost and time for ransomware victims. Previous studies and solutions have showed that when it comes to malware detection, malware behavior need to be prioritized and analyzed in order to recognize malware attack pattern. Although the current state-of-art solutions and frameworks used dynamic analysis approach such as machine learning that provide more impact rather than static approach, but there is not any approachable way in representing the analysis especially a detection that relies on malware behavior. Therefore, this paper proposed a graph theory approach which is analysis of the ransomware behavior that can be visualized into graph-based pattern. An experiment has been conducted with ten ransomware samples for malware analysis and verified using VirusTotal. Then, file system among features were selected in the experiment as a medium to understand the behavior of ransomware using data capturing tools. After that, the result of the analysis was visualized in a graph pattern based on Neo4j which is graph database tool. By using graph as a base, the discussion has been made to recognize each type of ransomware that acts differently in the file system and analyze which node that have the most impact during analysis part.

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Information exchange is a key aspect of using technology in everyday life. Crimes associated with the lack of information security awareness (ISA), misuse and carelessness are on the increase and often result in heavy losses and serious consequences. In order for ISA campaigns and programmes to be effective, the most successful and influential factors must be employed in the human component of the security awareness process. The purpose of this study is to investigate the causes of human breaches to information security and undertake a weight analysis of the models’ predictors’ relationships utilised in ISA literature from the current decade. Usable data were collected from twenty-one empirical studies related to ISA research in order to obtain the correlations required to perform a weight analysis process for a predictor's relationships. The relationships examined in all studies used in this research (significant–nonsignificant) are presented in a diagram. Findings show that six independent variables were found to be classified as ‘wellutilised’ variables, and the rest of the independent variables converge at the ‘Promising’ classification level. Contributions, limitations and directions of future work are presented.

This paper has reviewed the blockchain domain that suits with the current Industrial Revolution (Industry 4.0) uses cryptographic method blockchain. The implementation of this new cryptographic method in Industry 4.0 is currently being used widely as it eases the process of financial transaction and other process that have been as issue related with cyber security. The collection of review papers shows that the blockchain technology has high potential to grow wider not just in financial technology services and manufacturing industry but also in public sector, health care and even media industry. The papers elaborate how the blockchain able to be applied in different fields of technology in order make a secure and more protected services without entrusted the third party to avoid vulnerability to be attacked and misused. Nevertheless, the applications of the blockchain in various field helps in making the industry revolution.