2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)最新文献_第3页

Filter Versus Wrapper Feature Selection for Network Intrusion Detection System 网络入侵检测系统的过滤器与包装器特征选择

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014797

Mahmoud M. Sakr, Medhat A. Tawfeeq, A. El-Sisi

{"title":"Filter Versus Wrapper Feature Selection for Network Intrusion Detection System","authors":"Mahmoud M. Sakr, Medhat A. Tawfeeq, A. El-Sisi","doi":"10.1109/ICICIS46948.2019.9014797","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014797","url":null,"abstract":"With the increased usage of the Internet, the need for providing security and privacy to protect computer networks is increased too. Network intrusion detection system (NIDS) is intended to observe and inspect the activities in a network. This system is highly dependent on the features of the input network data as these features describe the behaviour of the current network activities. Not only do the irrelevant and redundant network features cause the learning algorithm to build an inaccurate detection model, but they also increase the time complexity and exhaust computation resources as well. In this paper, several feature selection techniques are applied to boost the performance of the NIDS. Categories of the applied selection techniques are of the filter (Information Gain (IG), Principal Component Analysis (PCA), and Correlation Feature Selection (CFS)) and of the wrapper (Genetic Algorithm (GA), Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO)). Support vector machine (SVM) is utilized to classify the network connections. The benchmark network traffic NSL-KDD dataset is selected to build and test the NIDS. The impact of the applied selection approaches on enhancing the detection model performance is compared and discussed. Evaluation results stated that the wrapper approaches achieved better classification performance for the NIDS in terms of high classification accuracy, detection rate, true positive rates, and low false-positive rates than the filter approaches. Our ABC-NIDS is compared with other related NIDSs and the comparison result proved that our system achieved the best performance.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125947619","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: 7

Algorithm for Automatic Crack Analysis and Severity Identification 裂纹自动分析与严重性识别算法

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014762

Sara Ashraf, I. Hegazy, T. Elarif

{"title":"Algorithm for Automatic Crack Analysis and Severity Identification","authors":"Sara Ashraf, I. Hegazy, T. Elarif","doi":"10.1109/ICICIS46948.2019.9014762","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014762","url":null,"abstract":"Concrete structures are increasing every day, to facilitate people's lives. With this expansion, the traditional manual maintenance method becomes unpractical, costly and time-wasting. The fast detection and maintenance of concrete surfaces defects is necessary to save people's lives, reducing maintenance cost, and increase the lifetime of concrete structures. Thus, the researches came up over the last twenty years to find an automatic way in order to maintain, apply regularly check-ups over concrete structures and assist engineers to take fast decisions. The most researches came up with high precision algorithms to allocate cracks and defects over the concrete surfaces with no human intervention. Nowadays, the computer programs can be dependable to capture large data sets of concrete structure, and then give precise locations of cracks. However, there exists a lack of researches that work on crack interpretation and automatic decision-making, which is considered as a critical part of those systems. Therefore, there exists a need for methods that describe the crack characteristics in terms of width, length, and other morphological attributes. In this paper, a crack interpretation algorithm is proposed to extract crack geometrical attributes and support the decision maker.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127499982","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: 2

Broad Learning on Big Data via Fusion of Heterogeneous Information 基于异构信息融合的大数据广泛学习

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/icicis46948.2019.9014730

Philip S. Yu

引用次数: 0

DDoS attack detection and classification via Convolutional Neural Network (CNN) 基于卷积神经网络(CNN)的DDoS攻击检测与分类

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014826

Ahmed Ramzy Shaaban, Essam Abd-Elwanis, Mohamed Hussein

引用次数: 18

Image Retrieval Based on Self-Organizing Feature Map and Multilayer Perceptron Neural Networks Classifier 基于自组织特征映射和多层感知器神经网络分类器的图像检索

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014768

Moshira S. Ghaleb, H. M. Ebied, Howida A. Shedeed, M. Tolba

{"title":"Image Retrieval Based on Self-Organizing Feature Map and Multilayer Perceptron Neural Networks Classifier","authors":"Moshira S. Ghaleb, H. M. Ebied, Howida A. Shedeed, M. Tolba","doi":"10.1109/ICICIS46948.2019.9014768","DOIUrl":"https://doi.org/10.1109/ICICIS46948.2019.9014768","url":null,"abstract":"Content-based image retrieval (CBIR), is a type of computer vision application. It searches for an image by image content not text in large database images. According to the huge existence of image databases, the searching time and high accuracy retrieved images become a great challenge. This paper aims to find a solution to retrieve images with high accuracy results. Neural network became a hot topic in the image processing field for the past few years. This paper presents two approaches to Content-based image retrieval. The first approach used the Self-Organized Feature Map (SOFM) as a clustering method to image retrieval. The second approach consists of two phases. The first one used the SOFM as a feature extraction method. The second phase used the Multilayer Perceptron (MLP) as a classifier method. The paper studied the impact of changing some parameters values on recognition accuracy. The experiments carried out using the Wang Corel 1000 database. The results show that the SOFM+MLP improved the recognition accuracy compared to SOFM. The SOFM+MLP achieved approximately 99% average recognition accuracy.","PeriodicalId":200604,"journal":{"name":"2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131057907","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

Testing Techniques in IoT-based Systems 基于物联网系统的测试技术

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014711

N. Medhat, Sherin M. Moussa, N. Badr, M. Tolba

引用次数: 7

Future Generation Education Technological Model 下一代教育技术模式

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014852

S. Cakula, Ginta Majore

引用次数: 1

Big Data for Better Life 大数据让生活更美好

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/icicis46948.2019.9014800

Yi Guo

引用次数: 0

Proceedings Publication 学报出版

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/icicis46948.2019.9014850

引用次数: 0

A Review on Iris Liveness Detection Techniques 虹膜活性检测技术综述

2019 Ninth International Conference on Intelligent Computing and Information Systems (ICICIS) Pub Date : 2019-12-01 DOI: 10.1109/ICICIS46948.2019.9014719

Manar Ramzy Dronky, W. Khalifa, Mohamed Roushdy

引用次数: 4