의료기기 네트워크 트래픽 보안 관련 머신러닝 알고리즘 성능 비교

• 영문명: Performance Comparison of Machine Learning Algorithms for Network Traffic Security in Medical Equipment
• 발행기관: 한국IT서비스학회
• 저자명: 고승형 박준호 왕다운 강은석 한현욱
• 간행물 정보: 『한국IT서비스학회지』제22권 제5호, 99~108쪽, 전체 10쪽
• 주제분류: 경제경영 > 경영학
• 파일형태: PDF
• 발행일자: 2023.10.31

영문 초록

As the computerization of hospitals becomes more advanced, security issues regarding data generated from various medical devices within hospitals are gradually increasing. For example, because hospital data contains a variety of personal information, attempts to attack it have been continuously made. In order to safely protect data from external attacks, each hospital has formed an internal team to continuously monitor whether the computer network is safely protected. However, there are limits to how humans can monitor attacks that occur on networks within hospitals in real time. Recently, artificial intelligence models have shown excellent performance in detecting outliers. In this paper, an experiment was conducted to verify how well an artificial intelligence model classifies normal and abnormal data in network traffic data generated from medical devices. There are several models used for outlier detection, but among them, Random Forest and Tabnet were used. Tabnet is a deep learning algorithm related to receive and classify structured data. Two algorithms were trained using open traffic network data, and the classification accuracy of the model was measured using test data. As a result, the random forest algorithm showed a classification accuracy of 93%, and Tapnet showed a classification accuracy of 99%. Therefore, it is expected that most outliers that may occur in a hospital network can be detected using an excellent algorithm such as Tabnet.

목차

1. 서 론

2. 이론적 배경

3. 연구 설계

4. 연구 결과

5. 결 론

참고문헌

키워드

Machine learning Hospital network Anomaly detection Data Security