2017 20th Conference of Open Innovations Association (FRUCT)最新文献_第5页

Anomaly detection in wireless sensor network of the “smart home” system “智能家居”系统无线传感器网络中的异常检测

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071301

A. Kanev, Aleksandr Nasteka, Catherine Bessonova, D. Nevmerzhitsky, A. Silaev, Aleksandr Efremov, K. Nikiforova

{"title":"Anomaly detection in wireless sensor network of the “smart home” system","authors":"A. Kanev, Aleksandr Nasteka, Catherine Bessonova, D. Nevmerzhitsky, A. Silaev, Aleksandr Efremov, K. Nikiforova","doi":"10.23919/FRUCT.2017.8071301","DOIUrl":"https://doi.org/10.23919/FRUCT.2017.8071301","url":null,"abstract":"Subject. The paper reviews the problem of anomaly detection in home automation systems. Authors define specificities of the existing security networks and accentuate the need of the detection of informational and physical impact on sensors. Characteristics of the transmitted information and physical impacts on automation devices are analysed and used as metrics for the anomalous behavior detection. Various machine learning algorithms for anomaly detection are compared and reviewed. Methods. The paper reviews the anomaly detection method that includes artificial neural networks as a detection tool. In this method characteristics of the security network devices are analysed to detect an anomalous behaviour, and exactly this type of data should be used to train the artificial neural network. This paper describes tools that can be used to implement the offered anomaly detection method. Main results. As an experiment the scenario has been created so that the model of the “Smart home” system produces the data of network information streams and the artificial neural network decides from this data. As a result the training and testing sets has been produced. The configuration of the artificial neural network has been defined as a result of tests. The experiment shows the potential of described method due to the fact that the area under ROC curve is 0.9689, which is better than basic machine learning algorithms performance. Practical importance. The offered method can be used at the development stage while implementation of the information and security systems requiring monitoring of the connected devices. Anomaly detection technology excludes the possibility of the inconspicuous violation of the information's confidentiality and integrity.","PeriodicalId":114353,"journal":{"name":"2017 20th Conference of Open Innovations Association (FRUCT)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126341757","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: 30

Visual development environment for OpenVX OpenVX的可视化开发环境

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071346

A. Syschikov, B. Sedov, K. Nedovodeev, S. Pakharev

引用次数: 3

Testing of the hypothesis in the research of computer incidents on the basis of the analysis of attributes and their values 计算机事件研究中的假设检验是在分析事件属性及其值的基础上进行的

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071333

I. Pantiukhin, I. Zikratov, A. Sizykh, Addy Christian Crosby Nii

引用次数: 1

Localising unsafe software resource usage with typed code model 使用类型化代码模型对不安全的软件资源使用进行本地化

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071349

L. Tsvetkov, A. Spivak

引用次数: 0

The method of analysis pseudo-phase portrait in the problem of recognition of biomedical signals 生物医学信号识别问题中的伪相位肖像分析方法

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071305

Karina Khachatryan, L. Manilo, A. Anisimov

引用次数: 2

Open services provider for supply chains 开放的供应链服务提供商

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071298

A. Ivashchenko, P. Sitnikov, M. Andreev, O. Surnin

引用次数: 2

Approaches to communication organization within cyber-physical systems 网络物理系统中的通信组织方法

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071352

I. Viksnin, Nikita D. Schcepin, Roman O. Patrikeev, A. A. Shlykov, I. Komarov

引用次数: 3

A study of different web-crawler behaviour 对不同网络爬虫行为的研究

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071322

A. Menshchikov, A. Komarova, Y. Gatchin, A. Korobeynikov, Nina Tishukova

引用次数: 8

On color-to-gray transformation for distributing color digital images 彩色数字图像分布的色灰变换研究

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071295

V. N. Gorbachev, E. Kaynarova, I. Metelev, E. Yakovleva

引用次数: 2

Reduction of the feature space for the detection of attacks of wireles sensor networks 减少无线传感器网络攻击检测的特征空间

2017 20th Conference of Open Innovations Association (FRUCT) Pub Date : 2017-04-01 DOI: 10.23919/FRUCT.2017.8071311

Victoria M. Korzhuk, Ilya Shilov, Julia Torshenko

{"title":"Reduction of the feature space for the detection of attacks of wireles sensor networks","authors":"Victoria M. Korzhuk, Ilya Shilov, Julia Torshenko","doi":"10.23919/FRUCT.2017.8071311","DOIUrl":"https://doi.org/10.23919/FRUCT.2017.8071311","url":null,"abstract":"The article evaluates the informativeness of the features of the abnormal behaviour of node in wireless sensor network. The estimation is carried out for the basic methods of attacking on wireless sensor networks, such as ≪funnel≫, ≪wormhole≫, ≪selective forwarding≫, etc. The estimation is performed using three basic methods: the method of Shannon, the method of Kullback and the method of accumulated frequencies. Special attention is paid to the dependence of the feature informativeness on various characteristics of the network (topology, packet generation periods, the degree of stochasticity of the selection of addresses for the generated packets transmission). Estimates are compared with previously obtained estimates for the simplest network with the mesh topology. Key results are the reduction of the feature space by uninformative features extracting (when reducing the introduced scale of feature informativeness degree is used), the formation of samples with estimates of informativeness for each network and each pair ≪normal behaviour≫-≪specific attack type≫. Also the program for automatic calculation of estimates of the informativeness and its subsequent analysis is created. In the future the obtained results can be used as the basis for methods of classification, aimed at identifying of anomalous behaviour in wireless sensor networks.","PeriodicalId":114353,"journal":{"name":"2017 20th Conference of Open Innovations Association (FRUCT)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115361838","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