KSII Transactions on Internet and Information Systems (TIIS)

  • 제18권10호
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  • Pages.3010-3039
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  • 2024
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  • 1976-7277(pISSN)
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  • 1976-7277(eISSN)
한국인터넷정보학회 (Korean Society for Internet Information)
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Maritime Cybersecurity Leveraging Artificial Intelligence Mechanisms Unveiling Recent Innovations and Projecting Future Trends

  • Parasuraman Kumar (Department of Information Technology and Engineering, Manonmaniam Sundaranar University (School of Computer Science and Engineering)) ;
  • Arumugam Maharajan (Department of Information Technology and Engineering, Manonmaniam Sundaranar University, (School of Computer Science and Engineering))
  • 투고 : 2024.06.14
  • 심사 : 2024.09.25
  • 발행 : 2024.10.31
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초록

This research delves into the realm of Maritime Cybersecurity, focusing on the application of Artificial Intelligence (AI) mechanisms, namely K-Nearest Neighbors (KNN), Random Forest (RF), and Artificial Neural Networks (ANN). The maritime industry faces evolving cyber threats, necessitating innovative approaches for robust defense. The maritime sector is increasingly reliant on digital technologies, making it susceptible to cyber threats. Traditional security measures are insufficient against sophisticated attacks, necessitating the integration of AI mechanisms. This research aims to evaluate the effectiveness of KNN, RF, and ANN in fortifying maritime cybersecurity, providing a proactive defense against emerging threats. Investigate the application of KNN, RF, and ANN in the maritime cybersecurity landscape. Assess the performance of these AI mechanisms in detecting and mitigating cyber threats. Explore the adaptability of KNN, RF, and ANN to the dynamic maritime environment. Provide insights into the strengths and limitations of each algorithm for maritime cybersecurity. The study employs these AI algorithms to analyze historical maritime cybersecurity data, evaluating their accuracy, precision, and recall in threat detection. Results demonstrate the effectiveness of KNN in identifying localized anomalies, RF in handling complex threat landscapes, and ANN in learning intricate patterns within maritime cybersecurity data. Comparative analyses reveal the strengths and weaknesses of each algorithm, offering valuable insights for implementation. In conclusion, the integration of KNN, RF, and ANN mechanisms presents a promising avenue for enhancing maritime cybersecurity. The study underscores the importance of adopting AI solutions to the maritime domain's unique challenges. While each algorithm demonstrates efficacy in specific scenarios, a hybrid approach may offer a comprehensive defense strategy. As the maritime industry continues to evolve, leveraging AI mechanisms becomes imperative for staying ahead of cyber threats and safeguarding critical assets. This research contributes to the ongoing discourse on maritime cybersecurity, providing a foundation for future developments in the field.

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