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A Case Study of Industrial Software Defect Prediction in Maritime and Ocean Transportation Industries
Jonggu Kang, Duksan Ryu, Jongmoon Baik
http://doi.org/10.5626/JOK.2020.47.8.769
Software defect prediction is a field of study that predicts defects in newly developed software in advance of use, based on models trained with past software defects and software update information using various latest machine learning techniques. It can provide a guide to effectively operate and deploy software quality assurance (SQA) resources in industry practices. Recently, there have been papers that have investigated the industrial application of software defect prediction, but more active research is needed to analyze how this can be applied over diverse domains with different characteristics. In this paper, we present the possibility of applying software defect prediction in the maritime and ocean transportation industries. These are facing challenges to build and deploy the types of emerging transportations such as high-efficiency eco-friendly ships, connected ships, smart ships, unmanned ships, or autonomous ships. In our experiments using actual data collected from the domain, the software defect prediction showed high defect prediction performance with 0.91 accuracy and 0.831 f-measure. This suggests that software defect prediction can be a useful tool to allocate SQA resources effectively in this field.
Analysis and Modeling of Advanced Persistent Threat through Case Study
http://doi.org/10.5626/JOK.2019.46.12.1328
Advanced Persistent Threat(APT) attack is one of the cyber-attack methods that continuously attacks the specific target with advanced tools. Since attackers use various methods that are specialized to targets, it is difficult to prevent the attacks with common security countermeasures. Currently, there exist various the APT attack stage models. However, the models only express APT attacks simply. Consequently, it is difficult to use them for risk assessment or as a recommendation for security requirements for a specific system. In order to overcome the limitations of such models, we derived factors of APT attack through a case study for defining the features of APT attack. We have also analyzed and defined the factors and their relationships to construct the APT attack factor model. For validation purpose, the model applied to the actual attack case has been referred to as ‘APT 1’. Through the proposed model, it would be possible to gain understanding about the overall flow of APT attacks and classify attack factors not only in terms of technical aspects but also with respect to social engineering facets.
Analysis of Case Scenario to Develop a System of Systems Meta-model for Ontology Representation
Young-Min Baek, Sumin Park, Yong-Jun Shin, Doo-Hwan Bae
http://doi.org/10.5626/JOK.2018.45.10.1056
Ontology is a formal and explicit specification technique that defines concepts and relationships of a system. It is utilized to establish a common knowledge base and to reduce mismatches or inconsistencies during communication. Since a System-of-Systems (SoS) is a large-scale complex system that achieves higher-level common goals by the collaboration of constituent systems, ontologies need to be established for overall SoS development and operations. In other words, refined development and communications among various stakeholders of an SoS can be achieved based on the conceptualization power of an ontology. However, in order to build an ontology effectively, SoS engineers require a systematic means to provide a guideline for domain analysis and ontology establishment. To fulfill these requirements, this study proposes a meta-model, called the Meta-model for System-of- Systems (M2SoS), which enables systematic specifications of ontologies for SoS development. M2SoS is developed based on existing studies on meta-modeling approaches in the multi-agent system domain, but M2SoS is improved to meet SoS-specific requirements by SoS case analysis.
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