Hospital security and safety management最新文献_第5页

Optimal injection attack strategy for cyber-physical systems: a dynamic feedback approach 网络物理系统的最优注入攻击策略:动态反馈方法

Hospital security and safety management Pub Date : 2022-01-01 DOI: 10.1051/sands/2022005

Sheng Gao, Hao Zhang, Zhuping Wang, Chao Huang

引用次数: 3

Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems 智能交通系统DDoS预测中未标记数据的高效投毒攻击与防御

Hospital security and safety management Pub Date : 2022-01-01 DOI: 10.1051/sands/2022003

Zhong Li, Xianke Wu, Changjun Jiang

{"title":"Efficient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems","authors":"Zhong Li, Xianke Wu, Changjun Jiang","doi":"10.1051/sands/2022003","DOIUrl":"https://doi.org/10.1051/sands/2022003","url":null,"abstract":"Nowadays, large numbers of smart sensors (e.g., road-side cameras) which communicate with nearby base stations could launch distributed denial of services (DDoS) attack storms in intelligent transportation systems. DDoS attacks disable the services provided by base stations. Thus in this paper, considering the uneven communication traffic flows and privacy preserving, we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the future. However, in the federated learning, we need to consider the problem of poisoning attacks due to malicious participants. The poisoning attacks will lead to the intelligent transportation systems paralysis without security protection. Traditional poisoning attacks mainly apply to the classification model with labeled data. In this paper, we propose a reinforcement learning-based poisoning method specifically for poisoning the prediction model with unlabeled data. Besides, previous related defense strategies rely on validation datasets with labeled data in the server. However, it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving, and our datasets are also unlabeled. Furthermore, we give a validation dataset-free defense strategy based on Dempster–Shafer (D–S) evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS prediction. In our experiments, we simulate 3000 points in combination with DARPA2000 dataset to carry out evaluations. The results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short time. Meanwhile, we compare our proposed defense algorithm with three popularly used defense algorithms. The results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.","PeriodicalId":79641,"journal":{"name":"Hospital security and safety management","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90351526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Drug Testing In The Workplace: An Update 工作场所的药物检测:最新进展

Hospital security and safety management Pub Date : 2011-10-18 DOI: 10.19030/JABR.V8I2.6175

Patricia S. Wall

引用次数: 7

RFID Authentication Protocol Conforming to EPC Class-1 Generation-2 Standard 符合EPC第1类第2代标准的RFID认证协议

Hospital security and safety management Pub Date : 2011-09-22 DOI: 10.1007/978-3-642-24082-9_60

Guiyue Jin, Eun Young Jeong, Ho-Youl Jung, Ki Dong Lee

引用次数: 16

A ZRP Based Reliable Route Discovery Scheme in Ad-Hoc Networks 一种基于ZRP的Ad-Hoc网络可靠路由发现方案

Hospital security and safety management Pub Date : 2004-01-01 DOI: 10.3745/KIPSTC.2004.11C.3.293

Kyoung-Ja Kim, Tae-Mu Chang