scholarly journals Marine Network Protocols and Security Risks

2021 ◽  
Vol 1 (2) ◽  
pp. 239-251
Author(s):  
Ky Tran ◽  
Sid Keene ◽  
Erik Fretheim ◽  
Michail Tsikerdekis

Marine network protocols are domain-specific network protocols that aim to incorporate particular features within the specialized marine context that devices are implemented in. Devices implemented in such vessels involve critical equipment; however, limited research exists for marine network protocol security. In this paper, we provide an analysis of several marine network protocols used in today’s vessels and provide a classification of attack risks. Several protocols involve known security limitations, such as Automated Identification System (AIS) and National Marine Electronic Association (NMEA) 0183, while newer protocols, such as OneNet provide more security hardiness. We further identify several challenges and opportunities for future implementations of such protocols.

2019 ◽  
Vol 75 (1) ◽  
pp. 99-101
Author(s):  
Gurpreet Singh Bhalla ◽  
Mahadevan Kumar ◽  
Pooja Mahajan ◽  
Kavita Sahai

Forests ◽  
2018 ◽  
Vol 9 (11) ◽  
pp. 691 ◽  
Author(s):  
André Tiemann ◽  
Irene Ring

In the context of considering natural capital in decision-making, the ecosystem services concept is steadily increasing in importance. This also holds for the forest sector in Germany. This development calls for a harmonisation of approaches and terms used in the forest sector, as well as being made compatible with the ecosystem services concept and relevant classifications. In Germany, and a number of Central European countries, a common way to assess the multifunctional benefits of forests is the forest function mapping method. Due to the federal multi-level governance system in Germany, each state has its own classification of forest functions and mapping. A first objective of this paper is to align the various forest function categories across German states as a basis to relate them to the ecosystem services concept. Second, this bottom-up approach is combined with a top-down approach, building on the Common International Classification of Ecosystem Services (CICES). The aim is to develop a harmonised, methodological framework, suitable for accounting forest-related ecosystem services, as a step towards future ecosystem services monitoring and reporting commitments in the forest sector. Finally, the challenges and opportunities of the ecosystem services concept for forest management are discussed and ways forward are elaborated.


2021 ◽  
Vol 2021 ◽  
pp. 1-21
Author(s):  
George Iakovakis ◽  
Constantinos-Giovanni Xarhoulacos ◽  
Konstantinos Giovas ◽  
Dimitris Gritzalis

The COVID-19 outbreak has forced businesses to shift to an unprecedented “work from home” company environment. While this provides advantages for employees and businesses, it also leads to a multitude of shortcomings, most prevalent of which is the emergence of additional security risks. Previous to the outbreak, company computer networks were mainly confined within its facilities. The pandemic has now caused this network to “spread thin,” as the majority of employees work remotely. This has opened up a variety of new vulnerabilities, as workers’ cyber protection is not the same at home as it is in office. Although the effects of the virus are now subsiding, working remotely has embedded itself as the new normal. Thus, it is imperative for company management to take the necessary steps to ensure business continuity and be prepared to deal with an increased number of cyber threats. In our research, we provide a detailed classification for a group of tools which will facilitate risk mitigation and prevention. We also provide a selection of automated tools such as vulnerability scanners, monitoring and logging tools, and antivirus software. We outline each tool using tables, to show useful information such as advantages, disadvantages, scalability, cost, and other characteristics. Additionally, we implement decision trees for each category of tools, in an attempt to assist in navigating the large amount of information presented in this paper. Our objective is to provide a multifaceted taxonomy and analysis of mitigation tools, which will support companies in their endeavor to protect their computer networks. Our contribution can also help companies to have some type of cyber threat intelligence so as to put themselves one step ahead of cyber criminals.


Plants are prone to different diseases caused by multiple reasons like environmental conditions, light, bacteria, and fungus. These diseases always have some physical characteristics on the leaves, stems, and fruit, such as changes in natural appearance, spot, size, etc. Due to similar patterns, distinguishing and identifying category of plant disease is the most challenging task. Therefore, efficient and flawless mechanisms should be discovered earlier so that accurate identification and prevention can be performed to avoid several losses of the entire plant. Therefore, an automated identification system can be a key factor in preventing loss in the cultivation and maintaining high quality of agriculture products. This paper introduces modeling of rose plant leaf disease classification technique using feature extraction process and supervised learning mechanism. The outcome of the proposed study justifies the scope of the proposed system in terms of accuracy towards the classification of different kind of rose plant disease.


Named Entity Recognition is the process wherein named entities which are designators of a sentence are identified. Designators of a sentence are domain specific. The proposed system identifies named entities in Malayalam language belonging to tourism domain which generally includes names of persons, places, organizations, dates etc. The system uses word, part of speech and lexicalized features to find the probability of a word belonging to a named entity category and to do the appropriate classification. Probability is calculated based on supervised machine learning using word and part of speech features present in a tagged training corpus and using certain rules applied based on lexicalized features.


2020 ◽  
Vol 54 (8) ◽  
pp. 922-929
Author(s):  
I. V. Alekseev ◽  
P. D. Zegzhda
Keyword(s):  

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