A Survey on Cyber Security Threats in IoT-Enabled Maritime Industry

Ashraf, Imran; Park, Yongwan; Hur, Soojung; Kim, Sung Won; Alroobaea, Roobaea; Zikria, Yousaf Bin; Nosheen, Summera

doi:10.1109/tits.2022.3164678

Cited by 62 publications

(36 citation statements)

References 74 publications

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“…[19]. The use of IoT devices fosters the technological advancement of maritime transportation, but introduces several novel risks that can considerably impact the maritime industry such as the high cost of implementation, the accuracy of data analysis in IoT systems [20] and cyber-security threats [21]. In this work, we rely on robust NB-IoT devices that allow transmitting data from maritime containers located on ships to sophisticated applications in the cloud with minimum packet loss and low latency.…”

Section: Related Workmentioning

confidence: 99%

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Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services

Noto

Gharbaoui

Falcitelli

et al. 2023

ASI

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In recent years, the adoption of innovative technologies in maritime transport and logistics systems has become a key aspect towards their development and growth, especially due to the complex and heterogeneous nature of the maritime environment. On the other hand, Internet of Things (IoT) solutions are gaining importance in the shipping industry thanks to the huge number of distributed cameras and sensors in modern ships, cargoes and sea ports, which can be exploited to improve safety, costs and productivity. This paper presents an experimental evaluation of a maritime platform, which enables a wide range of 5G-based services in the context of logistics and maritime transportation. Its core is a Narrow Band (NB)-IoT framework used to run massive IoT services on top of a hybrid terrestrial–satellite network and feed a OneM2M platform with significant data on maritime transport to develop high-level and value-added logistic applications on top. Among the many different services that could be provided by the maritime platform, we focus on the cargo-ship container tracking use case through the Global Tracking System, which allows for continuous container monitoring all over the seas in a port-to-port service scenario. The results of the experimental tests illustrate the capacity of the platform in managing the high number of messages transmitted by the container tracking devices (i.e., more than 3000) and its efficiency in limiting the average maximum latency and packet loss below 5.5 s and 0.9%, respectively.

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Section: Related Workmentioning

confidence: 99%

“…[ 15,20,21] Comprehensive overview on data management in maritime transport, IoT systems and cyber-security threats.…”

Section: Strengthsmentioning

confidence: 99%

Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services

Noto

Gharbaoui

Falcitelli

et al. 2023

ASI

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“…30 different ultrasonic techniques have been reported. Multiple reports were made on 7 techniques [39][40][41][42].…”

Section: Related Workmentioning

confidence: 99%

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

Nallasivan¹,

Ramachandran²,

Alroobaea³

et al. 2023

Computer Systems Science and Engineering

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An ultrasonic filter detects signs of malignant tumors by analysing the image's pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver's condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system in or close to real-time is examined. In this article, a Deep Learning (DL) model is implemented and modified to fit liver CT segmentation, and a semantic pixel classification of road scenes is recommended. The architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers. A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training phase. For the normal class, we obtained 100% precision for chronic cirrhosis hepatitis (73%), offset cirrhosis (59.26%), and offensive cirrhosis (91.67%) for chronic hepatitis or cirrhosis (73,0%). The aim is to develop a Computer-Aided Detection (CAD) screening tool to detect steatosis. The results proved 98.33% exactness, 94.59% sensitivity, and 92.11% case with Convolutional Neural Networks (CNN) classification. Although the classifier's performance did not differentiate so clearly at this level, it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve (AUC) and accuracy.

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“…This linking of the objects to the internet makes it possible for users to access the objects from any location. IoT applications have become increasingly widespread across a variety of sectors over the course of the last several years, including the manufacturing industry, the home automation industry, the transportation industry, and the healthcare industry [ [4] , [5] , [6] ]. Large-scale Agriculture Internet of Things (AIoT) infrastructures are often built, deployed, and managed independently by a number of different companies in the modern day.…”

Section: Introductionmentioning

confidence: 99%

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

Sonavane,

Prashantha,

Nikam

et al. 2024

Heliyon

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A Survey on Cyber Security Threats in IoT-Enabled Maritime Industry

Cited by 62 publications

References 74 publications

Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services

Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

Optimizing QoS and security in agriculture IoT deployments: A bioinspired Q-learning model with customized shards

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