• Title/Summary/Keyword: 비침습적 보안

Search Result 2, Processing Time 0.019 seconds

Security Technology Trends to Prevent Medical Device Hacking and Ransomware (커넥티드 의료기기 해킹 및 랜섬웨어 대응기술 동향)

  • Kwon, H.C.;Chung, B.H.;Moon, D.S.;Kim, I.K.
    • Electronics and Telecommunications Trends
    • /
    • v.36 no.5
    • /
    • pp.21-31
    • /
    • 2021
  • Ransomware attacks, such as Conti, Ryuk, Petya, and Sodinokibi, that target medical institutions are increasing rapidly. In 2020, in the United States., ransomware attacks affected over 600 separate clinics, hospitals, and organizations, and more than 18 million patient records. The cost of these attacks is estimated to be almost $21 billion USD. The first death associated with a ransomware attack was reported in 2020 by the University Hospital of Düesseldorf in Germany. In the case of medical institutions, as introduced in the Medjack report issued by TrapX Labs, in many cases, attackers target medical devices that are relatively insecure and then penetrate deep into more critical network infrastructure, such as EMR servers. This paper introduces security vulnerabilities of hospital medical devices, considerations for ransomware response by medical institutions, and related technology trends.

Quantified Lockscreen: Integration of Personalized Facial Expression Detection and Mobile Lockscreen application for Emotion Mining and Quantified Self (Quantified Lockscreen: 감정 마이닝과 자기정량화를 위한 개인화된 표정인식 및 모바일 잠금화면 통합 어플리케이션)

  • Kim, Sung Sil;Park, Junsoo;Woo, Woontack
    • Journal of KIISE
    • /
    • v.42 no.11
    • /
    • pp.1459-1466
    • /
    • 2015
  • Lockscreen is one of the most frequently encountered interfaces by smartphone users. Although users perform unlocking actions every day, there are no benefits in using lockscreens apart from security and authentication purposes. In this paper, we replace the traditional lockscreen with an application that analyzes facial expressions in order to collect facial expression data and provide real-time feedback to users. To evaluate this concept, we have implemented Quantified Lockscreen application, supporting the following contributions of this paper: 1) an unobtrusive interface for collecting facial expression data and evaluating emotional patterns, 2) an improvement in accuracy of facial expression detection through a personalized machine learning process, and 3) an enhancement of the validity of emotion data through bidirectional, multi-channel and multi-input methodology.