2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)最新文献_第5页

Relevance of the Gaussian classification on the Detection of DDoS Attacks 高斯分类对DDoS攻击检测的相关性

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00018

Abdoulaye Tapsoba, F. Ouédraogo, Arnold Elvis Ouédraogo

引用次数: 0

A Re-configurable Interaction Model in Distributed IoT Environment 分布式物联网环境下的可重构交互模型

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00023

Fangze Qiu, Huaxiao Huang, Yuji Dong

引用次数: 0

Analysis of Zero-Key Authentication and Zero-Knowledge Proof 零密钥认证与零知识证明分析

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00012

Huan Lin, Zhehan Wang, Tong Xu, S. Zhou, Yang Hong, Jun Steed Huang

{"title":"Analysis of Zero-Key Authentication and Zero-Knowledge Proof","authors":"Huan Lin, Zhehan Wang, Tong Xu, S. Zhou, Yang Hong, Jun Steed Huang","doi":"10.1109/CyberC55534.2022.00012","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00012","url":null,"abstract":"Recently passwordless authentication such as zero-key authentication or zero-knowledge access control is becoming popular among businesses prioritizing their users' and employees' security and digital experience. A challenge-response mechanism and public key infrastructure (PKI) cryptography are employed to perform the zero-key authentication or zero- knowledge access control that authorizes user access to an online service without a password or any shared secret required. Using a large quantum computer, a quantum algorithm could break the hard mathematical problems underlying PKI. The National Institute of Standards and Technology (NIST) has launched a program and competition to standardize one or more post-quantum cryptographic (PQC) algorithms to fight against quantum attacks. In this paper, we have conducted the first-ever mathematical analysis of lattice-based and polynomial-based PQC by introducing the relationship between automorphism and homomorphism. This analysis can help enterprises and organizations leverage NIST-selected PQC algorithms to safeguard their online services from quantum attacks. We performed the simulation to illustrate brute force broken probability for polynomial-based or multivariate-based PQC to validate our mathematical analysis of PQC.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134403780","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: 0

An enhanced interpretable deep learning approach for diabetic retinopathy detection 一种用于糖尿病视网膜病变检测的增强可解释深度学习方法

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00029

Soha Alrajjou, Edward Kwadwo Boahen, Chunyun Meng, Keyang Cheng

{"title":"An enhanced interpretable deep learning approach for diabetic retinopathy detection","authors":"Soha Alrajjou, Edward Kwadwo Boahen, Chunyun Meng, Keyang Cheng","doi":"10.1109/CyberC55534.2022.00029","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00029","url":null,"abstract":"Diabetic Retinopathy (DR) is a consequence of type1 or type-2 diabetes. It is critical to identify complications early since they may result in visual issues such as retinal detachment, vitreous hemorrhage, and glaucoma. The interpretability of automated classifiers for medical diagnoses such as diabetic retinopathy is critical. The primary issue is the difficulties inherent in inferring reasonable conclusions from them. In recent years, numerous efforts have been made to transform deep learning classifiers from statistical black box machines with high confidence to self-explanatory models. The concern of effective data preprocessing before classification remains unsolved. Although the application of machine Learning schemes has proven to be effective when trained in a supervised way, it still has limitations with data redundancy, feature selection, and human expert interference. Hence, a combinatorial deep learning approach is proposed to interpret diabetic retinopathy (DR) detection. The proposed method combines the Shapley Additive Explainability (SHAP) and Local 127:1679 Model-Agnostic Explanations (LIME) to analyze the deep learning output effectively. Results from our experiment show that our proposed approach outperformed the existing schemes in detecting DR.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117263344","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: 0

A Multimodal Off-Road Terrain Classification Benchmark for Extraterrestrial Traversability Analysis 地外可穿越性分析的多模式越野地形分类基准

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00028

Huang Huang, Yi Yang, Liang Tang, Zhang Zhang, Nailong Liu, Mou Li, Liang Wang

{"title":"A Multimodal Off-Road Terrain Classification Benchmark for Extraterrestrial Traversability Analysis","authors":"Huang Huang, Yi Yang, Liang Tang, Zhang Zhang, Nailong Liu, Mou Li, Liang Wang","doi":"10.1109/CyberC55534.2022.00028","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00028","url":null,"abstract":"A rover in extraterrestrial exploration works in challenging environment featured by primitive landforms and hidden dangerous areas. Due to the far distance from the rover to Earth, it is one of the most crucial capabilities that the rover can recognize and model the terrain properties efficiently and autonomously. In this paper, we present a Multimodal Off-road Terrain Classification (MOTC) dataset which is collected by a four-wheeled rover equipped with ego-centric visual cameras and inertial measurement unit (IMU). The dataset is generated from a boulder-strewn mock-up of the real Mars at the Intelligent Autonomous System Laboratory in Beijing Institute of Control Engineering. 24,982 images and corresponding sensor sequences are collected and annotated into 3 kinds of surface materials and 3 kinds of scene geometries. Based on the MOTC dataset, a baseline model with a multimodal fusion architecture is proposed for terrain classification. The experiment shows that the features extracted from visual images and from IMU complement each other to achieve improvements of terrain classification accuracy of the challenging extraterrestrial surface.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130438646","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: 0

Security hardening solution for docker container docker容器安全加固解决方案

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/cyberc55534.2022.00049

Nan Yang, Cen Chen, Tao Yuan, Yujie Wang, Xiao Gu, Dan Yang

引用次数: 0

Blockchain-based Multi-layer Data Security Sharing Model 基于区块链的多层数据安全共享模型

2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) Pub Date : 2022-10-01 DOI: 10.1109/CyberC55534.2022.00020

Yishan Wei, Xiaomei Cao

{"title":"Blockchain-based Multi-layer Data Security Sharing Model","authors":"Yishan Wei, Xiaomei Cao","doi":"10.1109/CyberC55534.2022.00020","DOIUrl":"https://doi.org/10.1109/CyberC55534.2022.00020","url":null,"abstract":"Facing the era of big data, the traditional data sharing model has many problems such as low execution efficiency, opaque data sharing process, and inflexible processing, which can no longer meet the demand for secure data sharing. So we present a blockchain-based multi-layer data security sharing model (ML-DSM). The model not only combines blockchain and attribute-based access control technology, but also introduces malicious access behavior judgment and user rating mechanism. Judging malicious access behavior helps detect malicious users in time at the early stage of access control, and effectively prevents malicious attacks such as DoS. In the user rating mechanism, the smart contract evaluates the user's reputation level based on the user's current and historical reputation scores and judges whether the reputation level is higher than the level of the requested data. The rating mechanism not only dynamically adjusts the access rights of the requestor but also effectively reduces the average judgment time of access control. The final experimental analysis results show that the data sharing model in this paper can improve the execution efficiency of access control while ensuring the security of the shared data. Thus ML-DSM achieves more secure and efficient data sharing.","PeriodicalId":234632,"journal":{"name":"2022 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115117106","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: 0