How Can I Gain Access to Data Sources?

2019 ◽  
pp. 761-788
Author(s):  
Ray Cooksey ◽  
Gael McDonald
Keyword(s):  
Data Sources ◽  
Access To Data ◽  
Gain Access

scholarly journals Ontology-Based Data Access: A Survey

2018 ◽  
Author(s):  
Guohui Xiao ◽  
Diego Calvanese ◽  
Roman Kontchakov ◽  
Domenico Lembo ◽  
Antonella Poggi ◽  
...  
Keyword(s):  
Data Access ◽  
Data Sources ◽  
Relational Data ◽  
Data Repositories ◽  
The Past ◽  
Future Challenges ◽  
Access To Data ◽  
User Friendly ◽  
Theoretical Results

We present the framework of ontology-based data access, a semantic paradigm for providing a convenient and user-friendly access to data repositories, which has been actively developed and studied in the past decade. Focusing on relational data sources, we discuss the main ingredients of ontology-based data access, key theoretical results, techniques, applications and future challenges.

scholarly journals An Explainable AI-based Intrusion Detection System for DNS over HTTPS (DoH) Attacks

2022 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy, ◽  
Yuan Luo
Keyword(s):  
Detection System ◽  
Accurate Solution ◽  
Privacy And Security ◽  
Learning Framework ◽  
Internet Users ◽  
Explainable Ai ◽  
Secure Network ◽  
Access To Data ◽  
Network Administrators ◽  
Gain Access

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91\%), recall (99.92\%) and F1 score (99.91\%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model.

Negotiating a Way In: A Special Collection of Essays on Accessing Information and Socio-legal Research

2011 ◽  
Vol 26 (3) ◽  
pp. 585-590 ◽  
Author(s):  
Michael S. Mopas ◽  
Sarah Turnbull
Keyword(s):  
Research Process ◽  
Public Officials ◽  
Liberal Democracies ◽  
State Institutions ◽  
Legal Studies ◽  
Government Documents ◽  
Graduate Research ◽  
Moral Duties ◽  
Access To Data ◽  
Gain Access

For most scholars, what we choose to research is largely determined by personal interest and a desire to produce new knowledge that will meaningfully contribute to the advancement of our chosen field of study. For those of us who do work in the areas of criminology and socio-legal studies, this pursuit of knowledge often requires that we gain access to public officials, state institutions, or government documents to collect necessary data. Regardless of whether we engage in quantitative or qualitative research, the findings we produce are highly dependent upon the information we are able to gather. Access therefore emerges as a key topic for researchers, raising important methodological concerns as well as broader political questions regarding the availability of information in liberal democracies.However, although gaining access to data is a crucial step in the research process, it is one that seems to garner little scholarly attention within criminological and socio-legal circles. Indeed, although there are a countless number of textbooks on the market (many of which are used every year as required reading in undergraduate and graduate research methods classes) that discuss the various ways researchers can go about collecting and analysing data, very little is said about the processes involved in negotiating entry with the gate-keepers of these data. Much of what is discussed in most methods text-books and classrooms is often limited to questions of ethics and the moral duties and obligations of researchers to their participants, rather than critical considerations related to the actual practice of gaining access.

scholarly journals An Explainable AI-based Intrusion Detection System for DNS over HTTPS (DoH) Attacks

2022 ◽  
Author(s):  
Tahmina Zebin ◽  
Shahadate Rezvy, ◽  
Yuan Luo
Keyword(s):  
Detection System ◽  
Accurate Solution ◽  
Privacy And Security ◽  
Learning Framework ◽  
Internet Users ◽  
Explainable Ai ◽  
Secure Network ◽  
Access To Data ◽  
Network Administrators ◽  
Gain Access

Over the past few years, Domain Name Service (DNS) remained a prime target for hackers as it enables them to gain first entry into networks and gain access to data for exfiltration. Although the DNS over HTTPS (DoH) protocol has desirable properties for internet users such as privacy and security, it also causes a problem in that network administrators are prevented from detecting suspicious network traffic generated by malware and malicious tools. To support their efforts in maintaining a secure network, in this paper, we have implemented an explainable AI solution using a novel machine learning framework. We have used the publicly available CIRA-CIC-DoHBrw-2020 dataset for developing an accurate solution to detect and classify the DNS over HTTPS attacks. Our proposed balanced and stacked Random Forest achieved very high precision (99.91\%), recall (99.92\%) and F1 score (99.91\%) for the classification task at hand. Using explainable AI methods, we have additionally highlighted the underlying feature contributions in an attempt to provide transparent and explainable results from the model.

scholarly journals A Future Trend in Sensing Enterprise Systems

2018 ◽  
Author(s):  
Majid Zamiri ◽  
Andreia Artifice ◽  
Elsa Marcelino-Jesus ◽  
Joao Sarraipa ◽  
Ricardo Jardim-Goncalves
Keyword(s):  
Business Models ◽  
Business Processes ◽  
Enterprise Systems ◽  
Business Environment ◽  
Future Trend ◽  
Data Sources ◽  
Free Access ◽  
Blockchain Technology ◽  
Intelligent Knowledge Management ◽  
Access To Data

The widespread use of sensorial technologies has created new opportunities for enterprises, as these new sources of data assist in increasing the context-awareness in which enterprises operate and enable them to better anticipate and adapt to changes in the business environment. This leads to better decision-making and greater profitability, as well as to reduced operating risks. However, if enterprises are to get any benefit from these new data sources, there is a need for tools, which will enable them to deal with the complexity of the multitude of data sources, both from physical and virtual objects, as well as a means to extract relevant and correct information and knowledge from it. Having access to data is not enough. The real value to enterprises comes from being able to process it, interpret it and being able to make accurate forecasts upon which they can base their business decisions. This paper presents the blockchain technology which is intended to support the development of sensing enterprise systems for intelligent knowledge management. Since the creation of the internet, blockchain is the most important technology created that is in constant development and has still much more to develop. In a simple way, blockchain is a computational technology for register of operations, decentralized, free-access, transparent, global, continuous. It is a public database that is accessible for everyone and is much more secure and reliable than other forms currently known to perform similar operations. Thus, it is intended to demonstrate the importance of this recent technology in business processes and the future trends of its use in sensing enterprise business processes. The proposed framework intends to demonstrate and serve as the foundation for new business models supported by the new capabilities provided by sensorial technologies in the support of enterprise applications.

scholarly journals Data Watch: Research Data from Transition Economies

2002 ◽  
Vol 16 (1) ◽  
pp. 225-240 ◽  
Author(s):  
Randall K Filer ◽  
Jan Hanousek
Keyword(s):  
Transition Economies ◽  
Research Data ◽  
Data Sources ◽  
Interested Reader ◽  
Maryland Department ◽  
University Of Maryland ◽  
College Park ◽  
Gain Access

This section will offer a description of data sources that may be of interest to economists. The purpose is to describe what data are available from those sources, what questions can be addressed because of the unique features of the data, and how an interested reader can gain access to the data. Suggestions for data sources that might be discussed here (or comments on past columns) can be sent to William N. Evans, c/o Data Watch, University of Maryland, Department of Economics, College Park, Maryland 20742, or they can be e-mailed to 〈 [email protected] 〉.

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