2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)最新文献_第2页

YOLO Approach in Digital Object Definition in Military Systems 军事系统数字对象定义中的YOLO方法

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625314

R. Benzer, Mithat Cagri Yildiz

引用次数: 2

Detection of Cyberbullying in Social Networks Using Machine Learning Methods 使用机器学习方法检测社交网络中的网络欺凌

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625321

Elif Varol Altay, B. Alatas

引用次数: 19

A New Model for Secure Joining to ZigBee 3.0 Networks in the Internet of Things 一种安全接入物联网ZigBee 3.0网络的新模式

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625315

Emre Deniz, R. Samet

引用次数: 5

Fighting Cyber Terrorism: Comparison of Turkey and Russia 打击网络恐怖主义:土耳其和俄罗斯的比较

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625270

Melih Burak Bicak, D. Bogdanova

引用次数: 1

New Techniques in Profiling Big Datasets for Machine Learning with a Concise Review of Android Mobile Malware Datasets 分析机器学习大数据集的新技术&对Android移动恶意软件数据集的简要回顾

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625275

Gürol Canbek, Ş. Sağiroğlu, Tugba Taskaya Temizel

引用次数: 11

Intrusion Detection System with Recursive Feature Elimination by Using Random Forest and Deep Learning Classifier 基于随机森林和深度学习分类器的递归特征消除入侵检测系统

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625318

S. Ustebay, Zeynep Turgut, M. Aydin

引用次数: 77

Comparision of String Matching Algorithms on Spam Email Detection 字符串匹配算法在垃圾邮件检测中的比较

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625317

C. Varol, H. Abdulhadi

{"title":"Comparision of String Matching Algorithms on Spam Email Detection","authors":"C. Varol, H. Abdulhadi","doi":"10.1109/IBIGDELFT.2018.8625317","DOIUrl":"https://doi.org/10.1109/IBIGDELFT.2018.8625317","url":null,"abstract":"Email is one of the most expedient approach to transfer messages among people all over the world. Its features, specifically reliability, quickness, and low cost makes it popular and useful among people in most parts of businesses and society. On the other hand, this popularity also created new harmful actions, such as email attacks (spam) in cyberspace. Spam is arguably one of the main reasons of drowning the WWW with many copies of similar messages generated through anonymous senders, which yields to time/space wasting of the email account holder and also a large virus and malware threat to Email providers. In spite of employing various filters to handle spam problem such as machine learning and content-based filtering, spammers are still able to bypass these defense mechanisms. In this paper, we investigate the use of string matching algorithms for spam email detection. Particularly this work examines and compares the efficiency of six well-known string matching algorithms, namely Longest Common Subsequence (LCS), Levenshtein Distance (LD), Jaro, Jaro-Winkler, Bi-gram, and TFIDF on two various datasets which are Enron corpus and CSDMC2010 spam dataset. We observed that Bi-gram algorithm performs best in spam detection in both datasets.","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":"125972601","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: 8

Application Areas of Community Detection: A Review 社区检测的应用领域综述

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625349

Arzum Karatas, Serap Sahin

引用次数: 32

Convolutional Neural Network Based Offline Signature Verification Application 基于卷积神经网络的离线签名验证应用

2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT) Pub Date : 2018-12-01 DOI: 10.1109/IBIGDELFT.2018.8625290

Muhammed Mutlu Yapici, Adem Tekerek, Nurettin Topaloglu

{"title":"Convolutional Neural Network Based Offline Signature Verification Application","authors":"Muhammed Mutlu Yapici, Adem Tekerek, Nurettin Topaloglu","doi":"10.1109/IBIGDELFT.2018.8625290","DOIUrl":"https://doi.org/10.1109/IBIGDELFT.2018.8625290","url":null,"abstract":"One of the most important biometric authentication technique is signature. Nowadays, there are two types of signatures, offline (static) and online (dynamic). Online signatures have higher distinctive features but offline signatures have fewer distinctive features. So offline signatures are more difficult to verify. In addition, the most important drawback of offline signatures is that they cannot be signed with the same way even by the most talented signer. This is called intra-personal variability. All these make the offline signature verification a challenging problem for researchers. In this study, we proposed a Deep Learning (DL) based offline signature verification method to prevent signature fraud by malicious people. The DL method used in the study is the Convolutional Neural Network (CNN). CNN was designed and trained separately for two different models such one Writer Dependent (WD) and the other Writer Independent (WI). The experimental results showed that WI has 62.5% of success and WD has 75% of success. It is predicted that the success of the obtained results will increase if the CNN method is supported by adding extra feature extraction methods.","PeriodicalId":290302,"journal":{"name":"2018 International Congress on Big Data, Deep Learning and Fighting Cyber Terrorism (IBIGDELFT)","volume":"29 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":"115545822","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: 20

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.2018.8625369

引用次数: 0