2018 International Symposium on Networks, Computers and Communications (ISNCC)最新文献_第2页

IoT based Smart Cities 基于物联网的智慧城市

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8530997

Husam Rajab, T. Cinkler

引用次数: 59

Malicious Host Detection by Imaging SYN Packets and A Neural Network 基于SYN报文成像和神经网络的恶意主机检测

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8530964

Ryo Nakamura, Y. Sekiya, Daisuke Miyamoto, Kazuya Okada, Tomohiro Ishihara

{"title":"Malicious Host Detection by Imaging SYN Packets and A Neural Network","authors":"Ryo Nakamura, Y. Sekiya, Daisuke Miyamoto, Kazuya Okada, Tomohiro Ishihara","doi":"10.1109/ISNCC.2018.8530964","DOIUrl":"https://doi.org/10.1109/ISNCC.2018.8530964","url":null,"abstract":"As is the case for recently networked systems, traffic analysis that detects malicious hosts is one of the most important issues in achieving better security. Many methods for detecting cyber attacks have been proposed; however, methods that specialize in detecting and identifying specific attacks will be obsolete in the near future. Instead of identifying types of cyber attacks, we propose a novel method to detect malicious hosts based on their behavior characteristics when they send SYN packets. Our method does not identify types of cyber attacks. It detects malicious hosts suspiciously sending SYN packets. This paper shows that (1) we have developed a method to convert a series of SYN packets to a visual image as input for neural networks, (2) the image represents distinctive features of the behavior of the hosts, and (3) our convolutional neural network model successfully distinguishes malicious host from normal ones. Our preliminary evaluation using real-word traffic shows that the detection accuracy for identifying malicious hosts is over 98% regardless of types of attack.","PeriodicalId":313846,"journal":{"name":"2018 International Symposium on Networks, Computers and Communications (ISNCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131356260","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: 5

Open Access to Intersystem Handover Control Using Multi-access Edge Computing 利用多址边缘计算实现系统间切换控制的开放访问

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8531051

E. Pencheva, D. Kireva, I. Atanasov, V. Trifonov

引用次数: 3

Search Algorithms for Distributed Data Structures in P2P Networks P2P网络中分布式数据结构的搜索算法

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8530977

Raed Al-Aaridhi, I. Dlikman, Newton Masinde, Kalman Graffi

引用次数: 3

On a Dynamic Data Placement Strategy for Heterogeneous Hadoop Clusters 异构Hadoop集群的动态数据放置策略研究

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8530970

Yang Liu, C. Wu, M. Wang, Aiqin Hou, Yongqiang Wang

{"title":"On a Dynamic Data Placement Strategy for Heterogeneous Hadoop Clusters","authors":"Yang Liu, C. Wu, M. Wang, Aiqin Hou, Yongqiang Wang","doi":"10.1109/ISNCC.2018.8530970","DOIUrl":"https://doi.org/10.1109/ISNCC.2018.8530970","url":null,"abstract":"Hadoop is one of the most popular distributed systems for big data computing in both industry and science communities. The default data placement strategy of Hadoop Distributed File System (HDFS), which was initially designed for homogenous environments, may suffer from performance degradation when deployed in heterogeneous clusters comprised of data nodes with disparate computing power and disk capacity, hence undermining the performance of MapReduce applications. In this paper, we use a Grey Forecast model to predict data hotness dynamically and determine an appropriate number of data block replicas on the fly. Based on such information, we further propose a dynamic data placement strategy (DDPS) to decide the best location for new replicas according to their hotness. The proposed method is able to dynamically adjust data replicas stored on each node in a heterogeneous Hadoop cluster and reduce the response time of big data applications. Experimental results on a heterogeneous Hadoop cluster show that DDPS together with the prediction model significantly increases application execution efficiency and improve MapReduce performance over the default HDFS configuration.","PeriodicalId":313846,"journal":{"name":"2018 International Symposium on Networks, Computers and Communications (ISNCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122577244","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: 6

Traffic Sign Detection and Recognition Using Adaptive Threshold Segmentation with Fuzzy Neural Network Classification 基于模糊神经网络分类的自适应阈值分割交通标志检测与识别

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8531070

Abdulrahman S. Alturki

{"title":"Traffic Sign Detection and Recognition Using Adaptive Threshold Segmentation with Fuzzy Neural Network Classification","authors":"Abdulrahman S. Alturki","doi":"10.1109/ISNCC.2018.8531070","DOIUrl":"https://doi.org/10.1109/ISNCC.2018.8531070","url":null,"abstract":"Traffic Sign Recognition (TSR) system is a significant component of Intelligent Transport System (ITS) as traffic signs assist the drivers to drive more safely and efficiently. In this paper, a new traffic sign detection and recognition approach is presented by using Fuzzy Neural Network (FNN) and it is including three stages. The first stage segments the images to extract ROIs. The segmentation is usually performed based on Adaptive thresholding to overcome the color segmentation problems. The second one detects traffic shapes. Given that the geometric form of traffic signs is limited to triangular, circular, rectangular and octagonal forms, the geometric information is used to identify traffic shapes from ROIs provided by the first stage. The third stage recognizes the traffic signs based on the information including included in their pictograms. Moreover, in this work, six types of features are extracted. These features were provided to the FNN classifier to perform the recognition. As a classifier, FNN, Artificial Neural Network (ANN) and Support Vector Machine (SVM) classifiers have been tested together with the new descriptor. The proposed method has been tested on both the German Traffic Sign Detection and Recognition Benchmark dataset. The results obtained are satisfactory when compared to the state-of-the-art methods.","PeriodicalId":313846,"journal":{"name":"2018 International Symposium on Networks, Computers and Communications (ISNCC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122704117","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

Performance of IA-MMSE Iterative Structures for SFBC Decoding in MIMO Systems Using Realistic System Parameters MIMO系统中IA-MMSE迭代结构的SFBC译码性能

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8530966

M. Mondin, F. Daneshgaran, F. D. Stasio

引用次数: 0

TCP performance for Satellite M2M applications over Random Access links 随机访问链路上卫星M2M应用的TCP性能

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8531048

A. Salam, C. Roseti, F. Zampognaro

引用次数: 2

Underdetermined Blind Separation Via Rough Equivalence Clustering for Satellite Communications 基于粗糙等效聚类的卫星通信欠定盲分离

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8530911

Chengjie Li, Lidong Zhu, Zhongqiang Luo

引用次数: 1

SHINE: Secure Hybrid In Network caching Environment SHINE:安全混合网络缓存环境

2018 International Symposium on Networks, Computers and Communications (ISNCC) Pub Date : 2018-06-01 DOI: 10.1109/ISNCC.2018.8530996

S. Romano, C. Roseti, A. Tulino

引用次数: 7