The human aspect is a major vulnerability in information security that relates to knowledge, attitude, and behavior in information security. This study aims to determine the essential variables used to assess the level of information security in a person. This study used Principal Component Analysis (PCA) and Multicluster Feature Selection (MCFS) approaches. Data collection using questionnaires based on The Human Aspects of Information Security Questionnaire (HAIS-Q) with adaptation to the Indonesian environment. The questionnaire distributed to 148 employees at XYZ institution as respondents. The level of information security applied to each individual based on the result of respondents' responses to the questionnaire. Data processing has been conducted using the Python programming language and Notebook Juypter as tools. The aim of PCA is to help in reducing some features with a specific explained variance ratio. MCFS is used to help reduce the number of features which can represent all features on HAIS-Q. The selected features based on knowledge are password security, social media, mobile device, and incident report. The selected features based on personal attitudes are password security, email usage, internet usage, mobile device, information handling, and incident reporting. The selected features based on behavior are password security, email used, internet used, mobile device, information handling, and incident reporting.
|Number of pages||10|
|Journal||International Journal of Advanced Science and Technology|
|Issue number||7 Special Issue|
|Publication status||Published - 14 Apr 2020|
- Information security
- Machine learning
- Unsupervised learning