Development of a Framework for Cybersecurity Risk Assessment in the Maritime Industry Using Machine Learning Techniques

Abstract

This study assesses the level of cybersecurity risk inherent in the maritime industry in order to improve process in the sector. The maritime sector has continued to witness cyber incidents due to its importance to national economy. Also, the growing dependence of the sector on information and communications technology (ICT), as a result of increased automation, has greatly exacerbated the threats. The underlying cyber infrastructure with its expanding threat landscape and vulnerabilities have also further exacerbated the risk landscape in the sector. More so, the dearth of empirical studies in this domain is an indication of knowledge gap occasioned by non-availability of empirical data on how organizations in this sector manage cybersecurity risk. That is, how organizational operations and technological assets, individuals and processes affect the sector. Thus, the study has identified and established the cybersecurity risks specific to the maritime sector and gauged the gap based on people, process and technology elements of cybersecurity. This study uses Artificial Intelligence, machine learning model in particular to carry out the assessment. The study identified how organizations applied security controls in the sector using the metrics of people, process and technology. The risk was analysed and graded into very high, high, moderate, low and very low from the established risk factors like threat and vulnerabilities. We used k-nearest neigbour and factorization methods for model training and risk ratings. The findings showed that the maritime sector has a high cybersecurity risk rating. This knowledge and the recommendations that followed, will help deepen the understanding of cybersecurity risks in the maritime sector as well as improve maritime process, its potential effects on service delivery, national security and economic wellbeing of the nation.