Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems最新文献

A group key agreement based encrypted traffic detection scheme for Internet of Things 基于组密钥协议的物联网加密流量检测方案

Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems Pub Date : 2020-11-16 DOI: 10.1145/3417312.3432093

Zhongqi Fan, Yong Zeng, Xiao-yan Zhu, Jianfeng Ma

{"title":"A group key agreement based encrypted traffic detection scheme for Internet of Things","authors":"Zhongqi Fan, Yong Zeng, Xiao-yan Zhu, Jianfeng Ma","doi":"10.1145/3417312.3432093","DOIUrl":"https://doi.org/10.1145/3417312.3432093","url":null,"abstract":"In CCS 2019, the privacy-preserving deep package inspection (PrivDPI) was proposed to detect anomalies and suspicious activities in encrypted network traffic, which is a provably secure and highly efficient approach for the end-to-end communication model. However, PrivDPI cannot be applied directly on the scenarios of the Internet of Things (IoT) due to its one/many-to-many communication model in which key agreement will bring giant power consumption. In this paper, we propose a group key agreement based encrypted traffic detection scheme for the Internet of Things (GKA_DPI) to solve it. In GKA_DPI, we still use BlindBox for traffic detection, which was used in PrivDPI and Sherry's scheme. The difference is that we use a dynamic group key agreement to replace the original key agreement protocol to reduce power consumption. Then we can perform deep traffic detection over encrypted packages on the widely used protocol Message Queuing Telemetry Transport (MQTT) of IoT. GKA_DPI can detect encrypted traffic without decrypting transmitted messages and find out malicious traffic to ensure the security of sensor network communication. Finally, we prove the forward and backward secrecy of proposed GKA_DPI.","PeriodicalId":361484,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125635644","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

GPS Spoofing Detection via SNR 基于信噪比的GPS欺骗检测

Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems Pub Date : 2020-11-16 DOI: 10.1145/3417312.3431826

Donghui Diao, Xin Wang, Chao Yang, Yue Wang, Jianfeng Ma

引用次数: 2

Hard-Label Black-Box Adversarial Attack on Deep Electrocardiogram Classifier 深度心电图分类器的硬标签黑盒对抗攻击

Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems Pub Date : 2020-11-16 DOI: 10.1145/3417312.3431827

Jonathan Lam, Pengrui Quan, Jiaming Xu, J. Jeyakumar, M. Srivastava

{"title":"Hard-Label Black-Box Adversarial Attack on Deep Electrocardiogram Classifier","authors":"Jonathan Lam, Pengrui Quan, Jiaming Xu, J. Jeyakumar, M. Srivastava","doi":"10.1145/3417312.3431827","DOIUrl":"https://doi.org/10.1145/3417312.3431827","url":null,"abstract":"Through aiding the process of diagnosing cardiovascular diseases (CVD) such as arrhythmia, electrocardiograms (ECGs) have progressively improved prospects for an automated diagnosis system in modern healthcare. Recent years have seen the promising applications of deep neural networks (DNNs) in analyzing ECG data, even outperforming cardiovascular experts in identifying certain rhythm irregularities. However, DNNs have shown to be susceptible to adversarial attacks, which intentionally compromise the models by adding perturbations to the inputs. This concept is also applicable to DNN-based ECG classifiers and the prior works generate these adversarial attacks in a white-box setting where the model details are exposed to the attackers. However, the black-box condition, where the classification model's architecture and parameters are unknown to the attackers, remains mostly unexplored. Thus, we aim to fool ECG classifiers in the black-box and hard-label setting where given an input, only the final predicted category is visible to the attacker. Our attack on the DNN classification model for the PhysioNet Computing in Cardiology Challenge 2017 [12] database produced ECG data sets mostly indistinguishable from the white-box version of an adversarial attack on this same database. Our results demonstrate that we can effectively generate the adversarial ECG inputs in this black-box setting, which raises significant concerns regarding the potential applications of DNN-based ECG classifiers in security-critical systems.","PeriodicalId":361484,"journal":{"name":"Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems","volume":"118 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116353222","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: 3

Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems 第一届ACM智能网络物理系统安全与安全国际研讨会论文集

Proceedings of the 1st ACM International Workshop on Security and Safety for Intelligent Cyber-Physical Systems Pub Date : 1900-01-01 DOI: 10.1145/3417312

引用次数: 0