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4차 산업혁명 시대의 IoT 서비스 참여 주체에 대한 적합한 인증수단 선택을 위한 평가기준

Evaluation Criteria for Suitable Authentication Method for IoT Service Provider in Industry 4.0 Environment

  • 투고 : 2017.06.30
  • 심사 : 2017.09.18
  • 발행 : 2017.09.30

초록

Advances in information technology, communication and network technology are radically facilitating digital convergences as the integration of human, equipment, and space in the current industry 4.0 era. In industry 4.0 environment, the vast amount of information with networked computing technology can be simultaneously accessible even in limited physical space. Two main benefit points out of these information are the convenience and efficiency in their online transactions either buying things online or selling online. Even though there exist so many benefits that information technology can create for the people doing business over the internet there is a critical problem to be answered. In spite of many such advantages, however, online transactions have many dysfunctions such as personal information leakage, account hacking, and cybercrime. Without preparing the appropriate protection methods or schema people reluctantly use the transaction or would find some other partners with enhanced information security environment. In this paper we suggested a novel selection criteria that can be used to evaluate the reliable means of authentication against the expected risks under on-going IoT based environment. Our selection criteria consists of 4 steps. The first step is services and risk identification step. The second step is evaluation of risk occurrence step. The third step includes the evaluation of the extent of damage. And the final step is the assessment of the level of risk. With the help of the above 4 step-approach people can systematically identify potential risks hiding in the online transactions and effectively avoid by taking appropriate counter actions.

키워드

참고문헌

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피인용 문헌

  1. IoT에서 효율적인 서비스 제공을 위한 이름 기반 서비스 탐색 메커니즘 vol.19, pp.6, 2017, https://doi.org/10.5762/kais.2018.19.6.46
  2. 개인 정보가 노출된 목표 객체의 블로킹 알고리즘 vol.20, pp.4, 2019, https://doi.org/10.5762/kais.2019.20.4.43
  3. Automatic Malicious Code Classification System through Static Analysis Using Machine Learning vol.13, pp.1, 2017, https://doi.org/10.3390/sym13010035