Adaptive Security Awareness Training Using Linked Open Data Datasets

Autor/inn/en	Tan, Zheyu; Beuran, Razvan; Hasegawa, Shinobu; Jiang, Weiwei; Zhao, Min; Tan, Yasuo
Titel	Adaptive Security Awareness Training Using Linked Open Data Datasets
Quelle	In: Education and Information Technologies, 25 (2020) 6, S.5235-5259 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1360-2357
DOI	10.1007/s10639-020-10155-x
Schlagwörter	Computer Security; Information Security; Training; Computer Assisted Instruction; Instructional Materials; Delivery Systems + Suchen Sie Ihr Suchwort? Computervirus; Computersicherheit; Ausbildung; Computer based training; Computerunterstützter Unterricht; Lehrmaterial; Lehrmittel; Unterrichtsmedien; Auslieferung
Abstract	Cybersecurity is no longer an issue discussed only between the professionals or technologists, but it is also closely related to ordinary people whose daily life is exposed to kinds of cyberattacks. And Womabat Security Technologies conducted a survey revealed that ransomware is an unknown concept to nearly two-thirds of employees. In practical, almost 95% of cybersecurity attacks are due to human error. At fact, expensive and sophisticated systems cannot work effectively without considering the human factor, while human factor is the major vulnerability in cybersecurity. Thus, it has great significance to give people cybersecurity awareness training. In this paper, we present a system, named ASURA, providing adaptive training aimed at improving cybersecurity awareness of people. Three issues can't be neglected in adaptive cybersecurity awareness training, as follows. Firstly, we need to decide the proper training contents from the huge training materials. Secondly, the training contents should be timely updated, as cyber attacks constantly changing. At last, we should conduct training through effective and acceptable approach. We solved above three issues in this paper, and the innovative idea of this paper is constructing hierarchical concept map from the LOD database DBpedia. Then, we employ a series of processing on hierarchical concept map, including PageRank algorithm used to calculate the importance of each concept node, and filtering used to filtered out undefined and unrelated concepts. In particular, we get training contents from DBpedia dynamically and timely updated, so that training contents is keeping up to date. ASURA delivered training contents completely online, thus significant trimmed budget and allowed learners accessing training outside of a traditional classroom. Moreover, ASURA provide adaptive training targeted to individual learner, as it generate training contents based on the keyword from the learner. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01