DLTIF: Deep Learning-Driven Cyber Threat Intelligence Modelling &  Detection Methodology For IoT-Enable Marine Transportation Systems

Dr. V. ANANTHAKRISHNA, A.SHIVANI, SALONI TIWARI, B. PRIYANKA

DLTIF: Deep Learning-Driven Cyber Threat Intelligence Modelling & Detection Methodology For IoT-Enable Marine Transportation Systems

Authors

Dr. V. ANANTHAKRISHNA, A.SHIVANI, SALONI TIWARI, B. PRIYANKA

Keywords:

Maritime Transportation System (MTS), Internet of Things (IoT), Cyber Threat Intelligence (CTI), Artificial Intelligence (AI), Cybersecurity, DLTIF (Deep Learning Threat Intelligence Framework), Threat Detection, Interoperability, Cyber-attacks, Data Security..

Abstract

The maritime industry has recently seen a boom in Internet of Things (IoT) technologies,which are effectively digitalizing MTS. The smart maritime items in IoT-enabled MTScommunicate wirelessly with port or ship infrastructure through an open channel of the Internet.

References

Software Engineering, an Engineering approach- James F. Peters, Witold Pedrycz, John Wiley.

R. Kumar, P. Kumar, R. Tripathi, G. P. Gupta, T. R. Gadekallu, and G. Srivastava, “SP2F: A secured privacy-preserving framework for smart agricultural unmanned aerial vehicles,” Comput. Netw., vol. 187, Mar. 2021, Art. no. 107819

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Published

2024-02-15

How to Cite

Dr. V. ANANTHAKRISHNA, A.SHIVANI, SALONI TIWARI, B. PRIYANKA. (2024). DLTIF: Deep Learning-Driven Cyber Threat Intelligence Modelling & Detection Methodology For IoT-Enable Marine Transportation Systems . Journal of Computational Analysis and Applications (JoCAAA), 32(2), 319–329. Retrieved from https://www.eudoxuspress.com/index.php/pub/article/view/2706