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2020 15th International Conference for Internet Technology and Secured Transactions (ICITST)最新文献

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Toward Compact Data from Big Data 从大数据走向紧凑数据
2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) Pub Date : 2020-12-08 DOI: 10.23919/ICITST51030.2020.9351315
S. Kim
{"title":"Toward Compact Data from Big Data","authors":"S. Kim","doi":"10.23919/ICITST51030.2020.9351315","DOIUrl":"https://doi.org/10.23919/ICITST51030.2020.9351315","url":null,"abstract":"Bigdata is a dataset of which size is beyond the ability of handling a valuable raw material that can be refined and distilled into valuable specific insights. Compact data is a method that optimizes the big dataset that gives best assets without handling complex bigdata. The compact dataset contains the maximum knowledge patterns at fine grained level for effective and personalized utilization of bigdata systems without big data. The compact data method is a tailor-made design which depends on problem situations. Various compact data techniques have been demonstrated into various data-driven research area in the paper.","PeriodicalId":346678,"journal":{"name":"2020 15th International Conference for Internet Technology and Secured Transactions (ICITST)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122170164","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
Session 4: Internet Application and Technology 第四部分:互联网应用与技术
2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) Pub Date : 2020-12-08 DOI: 10.23919/icitst51030.2020.9351323
{"title":"Session 4: Internet Application and Technology","authors":"","doi":"10.23919/icitst51030.2020.9351323","DOIUrl":"https://doi.org/10.23919/icitst51030.2020.9351323","url":null,"abstract":"","PeriodicalId":346678,"journal":{"name":"2020 15th International Conference for Internet Technology and Secured Transactions (ICITST)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121809512","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
Session 8: Digital Forensics and Cyber Security 第八部分:数字取证和网络安全
2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) Pub Date : 2020-12-08 DOI: 10.23919/icitst51030.2020.9351344
{"title":"Session 8: Digital Forensics and Cyber Security","authors":"","doi":"10.23919/icitst51030.2020.9351344","DOIUrl":"https://doi.org/10.23919/icitst51030.2020.9351344","url":null,"abstract":"","PeriodicalId":346678,"journal":{"name":"2020 15th International Conference for Internet Technology and Secured Transactions (ICITST)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117147837","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
Towards robust classification detection for adversarial examples 面向对抗样本的鲁棒分类检测
2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) Pub Date : 2020-12-08 DOI: 10.23919/ICITST51030.2020.9351309
Huangxiaolie Liu, Dong Zhang, Hui-Bing Chen
{"title":"Towards robust classification detection for adversarial examples","authors":"Huangxiaolie Liu, Dong Zhang, Hui-Bing Chen","doi":"10.23919/ICITST51030.2020.9351309","DOIUrl":"https://doi.org/10.23919/ICITST51030.2020.9351309","url":null,"abstract":"In the field of computer vision, machine learning (ML) models have been widely used in various tasks to achieve better performance. ML models, however, do a poor job of identifying malicious inputs such as adversarial examples. Abuse adversarial examples can cause security threats in ML-based products or applications. According to the definition of adversarial examples, the feature distribution of adversarial examples and normal examples are different. Besides, classification results of adversarial examples are sensitive to additive perturbance while normal examples are robust. This provides a theoretical basis for detecting adversarial examples from its own distribution. In this paper, we summarized some adversarial attack methods and defense methods, and a detection method based on the robustness of the classification result is proposed. This detection method has relatively good performance on gradient-based adversarial attack methods and does not rely on the structure or other information of ML model, so the structure of ML models need not be modified, which has a certain significance in practical engineering.","PeriodicalId":346678,"journal":{"name":"2020 15th International Conference for Internet Technology and Secured Transactions (ICITST)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123956461","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
Network Security Evaluation Using Deep Neural Network 基于深度神经网络的网络安全评估
2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) Pub Date : 2020-12-08 DOI: 10.23919/ICITST51030.2020.9351326
Loreen Mahmoud, R. Praveen
{"title":"Network Security Evaluation Using Deep Neural Network","authors":"Loreen Mahmoud, R. Praveen","doi":"10.23919/ICITST51030.2020.9351326","DOIUrl":"https://doi.org/10.23919/ICITST51030.2020.9351326","url":null,"abstract":"One of the most significant systems in computer network security assurance is the assessment of computer network security. With the goal of finding an effective method for performing the process of security evaluation in a computer network, this paper uses a deep neural network to be responsible for the task of security evaluating. The DNN will be built with python on Spyder IDE, it will be trained and tested by 17 network security indicators then the output that we get represents one of the security levels that have been already defined. The maj or purpose is to enhance the ability to determine the security level of a computer network accurately based on its selected security indicators. The method that we intend to use in this paper in order to evaluate network security is simple, reduces the human factors interferences, and can obtain the correct results of the evaluation rapidly. We will analyze the results to decide if this method will enhance the process of evaluating the security of the network in terms of accuracy.","PeriodicalId":346678,"journal":{"name":"2020 15th International Conference for Internet Technology and Secured Transactions (ICITST)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124592489","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: 14
Partitioned Private User Storages in End-to-End Encrypted Online Social Networks 端到端加密在线社交网络中的分区私有用户存储
2020 15th International Conference for Internet Technology and Secured Transactions (ICITST) Pub Date : 2020-10-08 DOI: 10.23919/ICITST51030.2020.9351335
Fabian Schillinger, C. Schindelhauer
{"title":"Partitioned Private User Storages in End-to-End Encrypted Online Social Networks","authors":"Fabian Schillinger, C. Schindelhauer","doi":"10.23919/ICITST51030.2020.9351335","DOIUrl":"https://doi.org/10.23919/ICITST51030.2020.9351335","url":null,"abstract":"In secure Online Social Networks (OSN), often end-to-end encryption is used to ensure the privacy of the communication. To manage, store, or transfer cryptographic keys from one device to another, encrypted private storages can be used. To gain access to such storages, login credentials, only known to the user, are needed. Losing these credentials results in a permanent loss of cryptographic keys and messages because the storage is encrypted. We present a scheme to split encrypted user storages into multiple storages. Each one can be reconstructed with the help of other participants of the OSN. The more of the storages can be reconstructed, the higher the chance of successfully reconstructing the complete private storage is. Therefore, regaining possession of the cryptographic keys used for communication is increased. We achieve high rates of successful reconstructions, even if a large fraction of the distributed shares is not accessible anymore because the shareholders are inactive or malicious.","PeriodicalId":346678,"journal":{"name":"2020 15th International Conference for Internet Technology and Secured Transactions (ICITST)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129710383","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
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