Dictionary Services Research Articles

“Off-Label” Use of DNS

The Domain Name System ( DNS ) is known to be one of the most widely abused protocols by threat actors to use in unconventional ways to hide under normal traffic. Apart from threat actors, DNS is being actively used or rather misused by many other service providers, vendors, and so on, to provide the intended services. An in-depth examination of the DNS logs collected over a long period revealed some very interesting legitimate use cases of the DNS protocol by the industry and other players, apart from its normal name resolution service function. We coin the term “Off-label use of DNS” to represent those use cases. Legitimate here simply means using DNS for non-malicious purposes other than what it was traditionally designed for, which is for providing domain resolution; a dictionary service mapping domain names to corresponding IP addresses. One of the main reasons DNS is used, or possibly misused, for these off-label use cases is the speed of data transfer and reduced overhead in terms of bandwidth. These off-label use cases of DNS can often leak important information about the clients and software they are running and can be leveraged in a variety of ways by the network security defenders/analysts to improve their detection on the network. This research will detail some of those legitimate off-label use cases and how they can be leveraged by the analysts to detect malware trends in the network and much more just by analyzing an enterprise's DNS logs.

Correction to: The development of Korean online dictionaries: a case study of Naver dictionary services

In the original publication, one of the reference texts was incorrectly published as “Tono, Y. 2009.

The development of Korean online dictionaries: a case study of Naver dictionary services

The advances in information technology have been affecting people’s consultation behaviors and thus shaping the direction of development of Korean online dictionaries. This paper aims (1) to describe the development of Korean online dictionaries by analyzing a case study of the dictionary services of Naver, South Korea’s biggest web portal site; (2) to identify the characteristics of the online dictionary evolution; and (3) to provide primary data for further research. In order to provide background information on the evolution of Naver dictionary services, it introduces the history of Korean online dictionaries and identifies the changes in the sizes and characteristics of the audiences, which were affected by the shift from the personal computer to mobile devices. Then, the development of Naver dictionary services is examined in terms of the development phases, entry data acquisition methods, and the development of tool features. This study reveals three findings: (1) the history could be divided into three periods: the formation period (1999–2004), the growth period (2005–2009), and the expansion period (2010–now). The expansion period is characterized by the popularity of smartphones, the enlargement of dictionary services, and the emergence of mechanical translation; (2) the four main sources for entries in the dictionary services are learner dictionaries, terminological–technical dictionaries, machine-generated data such as web-crawled data and parallel texts collected from the internet, and user-participated entries; (3) most of the tool features appeared during the growth period, and they are now evolving in the direction towards appification and individualization. These findings may allow predictions about the future of Korean online dictionaries.

Requirements cybernetics: Elicitation based on user behavioral data

Users' behavioral data provides important cue for product improvement.Requirements elicitation process is formulated as a feedback control system.This is an attempt to seek synergies between requirements and cybernetics.Potential control variables of data-driven elicitation are discussed. Users' behavioral data provides important cue for product improvement. Today's web based applications collect various kinds of service data, which is an ideal source of information for product designers to better understand users' needs and behaviors. This paper first discusses the types of data collected so far, and then such data-driven requirements elicitation process is formulated as a feedback control system, where the classical requirements elicitation philosophy turns into a continuous optimization to user behavioral models. To this end, it is important to know how the data collection function reflects user behavior, and how specific data analysis approaches help making design decisions. This is an attempt to seek practical synergies between the two disciplines of requirements and cybernetics, to explore the possibilities of formulating problems in requirements with concepts and frameworks from cybernetics, and understand to what extent that known research results from cybernetics can be applied to address requirements problems. In particular, control frameworks for the user data driven requirements elicitation process are experimented, and potential control variables are discussed. We use two example cases to illustrate the proposed approach, an online dictionary service and a mobile music player service.

Going beyond the current neuroinformatics infrastructure.

Going beyond the current neuroinformatics infrastructure.

Data dictionary services in XNAT and the Human Connectome Project.

The XNAT informatics platform is an open source data management tool used by biomedical imaging researchers around the world. An important feature of XNAT is its highly extensible architecture: users of XNAT can add new data types to the system to capture the imaging and phenotypic data generated in their studies. Until recently, XNAT has had limited capacity to broadcast the meaning of these data extensions to users, other XNAT installations, and other software. We have implemented a data dictionary service for XNAT, which is currently being used on ConnectomeDB, the Human Connectome Project (HCP) public data sharing website. The data dictionary service provides a framework to define key relationships between data elements and structures across the XNAT installation. This includes not just core data representing medical imaging data or subject or patient evaluations, but also taxonomical structures, security relationships, subject groups, and research protocols. The data dictionary allows users to define metadata for data structures and their properties, such as value types (e.g., textual, integers, floats) and valid value templates, ranges, or field lists. The service provides compatibility and integration with other research data management services by enabling easy migration of XNAT data to standards-based formats such as the Resource Description Framework (RDF), JavaScript Object Notation (JSON), and Extensible Markup Language (XML). It also facilitates the conversion of XNAT's native data schema into standard neuroimaging vocabularies and structures.