2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)最新文献

Applications of Stream Data Mining on the Internet of Things: A Survey 流数据挖掘在物联网中的应用综述

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625289

E. Guler, S. Ozdemir

引用次数: 2

Privacy Preserving Big Data Publishing 隐私保护大数据出版

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625358

Yavuz Canbay, Yilmaz Vural, Ş. Sağiroğlu

引用次数: 5

On the Performance Analysis of Map-Reduce Programming Model on In-Memory NoSQL Storage Platforms: A Case Study 内存NoSQL存储平台上Map-Reduce编程模型的性能分析

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625300

Secil Yuzuk, Murat G. Aktaş, M. Aktaş

{"title":"On the Performance Analysis of Map-Reduce Programming Model on In-Memory NoSQL Storage Platforms: A Case Study","authors":"Secil Yuzuk, Murat G. Aktaş, M. Aktaş","doi":"10.1109/IBIGDELFT.2018.8625300","DOIUrl":"https://doi.org/10.1109/IBIGDELFT.2018.8625300","url":null,"abstract":"The financial data analysis, which is the road map of the future and at the same time the mirror of today, is of vital importance for many institutions. Therefore, it is common to apply statistical analysis on financial data. In such cases, data size becomes very important when performing financial data analysis. While analyzing the financial data, as the size and variety of data and increase, one can achieve the most accurate financial data analysis outcome. However, the increase in data size also brings some disadvantages such as performance-loss due to processing large-scale data. These disadvantages occur in both query performance and various functions that are used in data analysis. In this respect, it is necessary to examine the data storage platforms comparatively, which will investigate the performance of query and statistical functions, used in financial data analysis, at the highest level for large-scale financial data sets. For this purpose, the first step of this study was to compare the performance of the query on the Relational and Non-SQL-based storage environments, and to compare the performance of the query in the single-node and double-node in-memory NoSQL data storage environment. To facilitate testing of these platforms; as the SQL database system, MSSQL was selected and as the distributed in-memory NoSQL database system, Hazelcast was selected. For different data sizes on these platforms, the run times of the query and statistical functions were measured. In order to examine the ability of the in-memory NoSQL data storage platforms, to manage and manipulate the data, map-reduce programming model was used. Performance tests on single nodes and multiple nodes show that in-memory NoSQL platforms are very successful compared to relational database systems. In addition, it has been found that in-memory NoSQL storage platforms provide higher performance gains when using the map-reduce programming model.","PeriodicalId":290302,"journal":{"name":"2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114875199","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

A Review on RNA Secondary Structure Prediction Algorithms RNA二级结构预测算法研究进展

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625347

Ian Kings Oluoch, Abdullah Akalin, Yilmaz Vural, Yavuz Canbay

引用次数: 11

Static and Dynamic Analysis of Third Generation Cerber Ransomware 第三代Cerber勒索软件的静态和动态分析

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625353

Ilker Kara, M. Aydos

{"title":"Static and Dynamic Analysis of Third Generation Cerber Ransomware","authors":"Ilker Kara, M. Aydos","doi":"10.1109/IBIGDELFT.2018.8625353","DOIUrl":"https://doi.org/10.1109/IBIGDELFT.2018.8625353","url":null,"abstract":"Cyber criminals have been extensively using malicious Ransomware software for years. Ransomware is a subset of malware in which the data on a victim's computer is locked, typically by encryption, and payment is demanded before the ransomed data is decrypted and access returned to the victim. The motives for such attacks are not only limited to economical scumming. Illegal attacks on official databases may also target people with political or social power. Although billions of dollars have been spent for preventing or at least reducing the tremendous amount of losses, these malicious Ransomware attacks have been expanding and growing. Therefore, it is critical to perform technical analysis of such malicious codes and, if possible, determine the source of such attacks. It might be almost impossible to recover the affected files due to the strong encryption imposed on such files, however the determination of the source of Ransomware attacks have been becoming significantly important for criminal justice. Unfortunately, there are only a few technical analysis of real life attacks in the literature. In this work, a real life Ransomware attack on an official institute is investigated and fully analyzed. The analysis have been performed by both static and dynamic methods. The results show that the source of the Ransomware attack has been shown to be traceable from the server's whois information.","PeriodicalId":290302,"journal":{"name":"2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131249154","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: 12

Detecting Port Scan Attempts with Comparative Analysis of Deep Learning and Support Vector Machine Algorithms 用深度学习和支持向量机算法的比较分析检测端口扫描尝试

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625370

Dogukan Aksu, M. Ali Aydın

引用次数: 57

International Congress on Bigdata Deep Learning and Fighting Cyber Terrorism 大数据、深度学习与打击网络恐怖主义国际大会

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/ibigdelft.2018.8625368

引用次数: 0

Font and Turkish Letter Recognition in Images with Deep Learning 深度学习图像中的字体和土耳其字母识别

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625333

A. Sevik, P. Erdoğmuş, Erdi Yalein

{"title":"Font and Turkish Letter Recognition in Images with Deep Learning","authors":"A. Sevik, P. Erdoğmuş, Erdi Yalein","doi":"10.1109/IBIGDELFT.2018.8625333","DOIUrl":"https://doi.org/10.1109/IBIGDELFT.2018.8625333","url":null,"abstract":"The purpose of this article is to recognize letter and especially font from images which are containing texts. In order to perform recognition process, primarily, the text in the image is divided into letters. Then, each letter is sended to the recognition system. Results are filtered according to vowels which are most used in Turkish texts. As a result, font of the text is obtained. In order to separate letters from text, an algorithm used which developed by us to do separation. This algorithm has been developed considering Turkish characters which has dots or accent such as i, j, ü, ö and g and helps these characters to be perceived by the system as a whole. In order to provide recognition of Turkish characters, all possibilities were created for each of these characters and the algorithm was formed accordingly. After recognizing the each character, these individual parts are sended to the pre-trained deep convolutional neural network. In addition, a data set has been created for this pre-trained network. The data set contains nearly 13 thousands of letters with 227*227*3 size have been created with different points, fonts and letters. As a result, 100 percent of success has been attained in the training. %79.08 letter and %75 of font success has been attained in the tests.","PeriodicalId":290302,"journal":{"name":"2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129437871","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: 16

A Decentralized Application for Secure Messaging in a Trustless Environment 无信任环境中安全消息传递的去中心化应用

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625362

M. Abdulaziz, Davut Çulha, A. Yazici

引用次数: 8

Deep Learning in Intrusion Detection Systems 入侵检测系统中的深度学习

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625278

Gozde Karatas, Onder Demir, Ozgur Koray Sahingoz

{"title":"Deep Learning in Intrusion Detection Systems","authors":"Gozde Karatas, Onder Demir, Ozgur Koray Sahingoz","doi":"10.1109/IBIGDELFT.2018.8625278","DOIUrl":"https://doi.org/10.1109/IBIGDELFT.2018.8625278","url":null,"abstract":"In recent years, due to the emergence of boundless communication paradigm and increased number of networked digital devices, there is a growing concern about cybersecurity which tries to preserve either the information or the communication technology of the system. Intruders discover new attack types day by day, therefore to prevent these attacks firstly they need to be identified correctly by the used intrusion detection systems (IDSs), and then proper responses should be given. IDSs, which play a very crucial role for the security of the network, consist of three main components: data collection, feature selection/conversion and decision engine. The last component directly affects the efficiency of the system and use of machine learning techniques is one of most promising research areas. Recently, deep learning has been emerged as a new approach which enables the use of Big Data with a low training time and high accuracy rate with its distinctive learning mechanism. Consequently, it has been started to use in IDS systems. In this paper, it is aimed to survey deep learning based intrusion detection system approach by making a comparative work of the literature and by giving the background knowledge either in deep learning algorithms or in intrusion detection systems.","PeriodicalId":290302,"journal":{"name":"2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127526308","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: 75