1 resultado para Data Structures, Cryptology and Information Theory
em Academic Archive On-line (Mid Sweden University
Filtro por publicador
- JISC Information Environment Repository (2)
- Aberdeen University (2)
- Aberystwyth University Repository - Reino Unido (6)
- Academic Archive On-line (Jönköping University; Sweden) (1)
- Academic Archive On-line (Mid Sweden University; Sweden) (1)
- Acceda, el repositorio institucional de la Universidad de Las Palmas de Gran Canaria. España (1)
- AMS Tesi di Dottorato - Alm@DL - Università di Bologna (8)
- AMS Tesi di Laurea - Alm@DL - Università di Bologna (2)
- Applied Math and Science Education Repository - Washington - USA (1)
- Aquatic Commons (6)
- ArchiMeD - Elektronische Publikationen der Universität Mainz - Alemanha (3)
- Archive of European Integration (57)
- Archivo Digital para la Docencia y la Investigación - Repositorio Institucional de la Universidad del País Vasco (1)
- Aston University Research Archive (27)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (6)
- Biblioteca Digital da Produção Intelectual da Universidade de São Paulo (BDPI/USP) (5)
- Biblioteca Digital de Teses e Dissertações Eletrônicas da UERJ (1)
- Biodiversity Heritage Library, United States (1)
- BORIS: Bern Open Repository and Information System - Berna - Suiça (50)
- Brock University, Canada (3)
- Bucknell University Digital Commons - Pensilvania - USA (4)
- Bulgarian Digital Mathematics Library at IMI-BAS (7)
- CaltechTHESIS (4)
- Cambridge University Engineering Department Publications Database (40)
- CentAUR: Central Archive University of Reading - UK (48)
- Chinese Academy of Sciences Institutional Repositories Grid Portal (6)
- Cochin University of Science & Technology (CUSAT), India (3)
- Coffee Science - Universidade Federal de Lavras (1)
- Comissão Econômica para a América Latina e o Caribe (CEPAL) (20)
- CORA - Cork Open Research Archive - University College Cork - Ireland (2)
- CUNY Academic Works (2)
- Dalarna University College Electronic Archive (1)
- Deakin Research Online - Australia (89)
- Department of Computer Science E-Repository - King's College London, Strand, London (4)
- DI-fusion - The institutional repository of Université Libre de Bruxelles (1)
- Digital Commons - Michigan Tech (1)
- Digital Commons - Montana Tech (1)
- Digital Commons @ DU | University of Denver Research (3)
- Digital Commons at Florida International University (11)
- Digital Howard @ Howard University | Howard University Research (1)
- Digital Peer Publishing (3)
- DigitalCommons - The University of Maine Research (1)
- DigitalCommons@The Texas Medical Center (6)
- DigitalCommons@University of Nebraska - Lincoln (1)
- DRUM (Digital Repository at the University of Maryland) (1)
- Duke University (1)
- Glasgow Theses Service (1)
- Greenwich Academic Literature Archive - UK (2)
- Helda - Digital Repository of University of Helsinki (6)
- Indian Institute of Science - Bangalore - Índia (30)
- Instituto Politécnico do Porto, Portugal (4)
- Lume - Repositório Digital da Universidade Federal do Rio Grande do Sul (1)
- Massachusetts Institute of Technology (2)
- Memoria Académica - FaHCE, UNLP - Argentina (3)
- National Center for Biotechnology Information - NCBI (7)
- Plymouth Marine Science Electronic Archive (PlyMSEA) (2)
- Publishing Network for Geoscientific & Environmental Data (47)
- QUB Research Portal - Research Directory and Institutional Repository for Queen's University Belfast (57)
- Queensland University of Technology - ePrints Archive (134)
- Repositorio Académico de la Universidad Nacional de Costa Rica (1)
- Repositório Alice (Acesso Livre à Informação Científica da Embrapa / Repository Open Access to Scientific Information from Embrapa) (1)
- Repositório digital da Fundação Getúlio Vargas - FGV (9)
- Repositório Digital da UNIVERSIDADE DA MADEIRA - Portugal (1)
- Repositório Institucional da Universidade de Aveiro - Portugal (1)
- Repositório Institucional UNESP - Universidade Estadual Paulista "Julio de Mesquita Filho" (16)
- Research Open Access Repository of the University of East London. (1)
- RUN (Repositório da Universidade Nova de Lisboa) - FCT (Faculdade de Cienecias e Technologia), Universidade Nova de Lisboa (UNL), Portugal (4)
- SAPIENTIA - Universidade do Algarve - Portugal (1)
- School of Medicine, Washington University, United States (1)
- Scientific Open-access Literature Archive and Repository (1)
- Universidad de Alicante (3)
- Universidad Politécnica de Madrid (21)
- Universidade Complutense de Madrid (1)
- Universidade Federal do Pará (1)
- Universidade Técnica de Lisboa (1)
- Universitat de Girona, Spain (2)
- Universitätsbibliothek Kassel, Universität Kassel, Germany (2)
- Université de Lausanne, Switzerland (1)
- Université de Montréal, Canada (6)
- University of Connecticut - USA (3)
- University of Michigan (107)
- University of Queensland eSpace - Australia (37)
- University of Southampton, United Kingdom (3)
- University of Washington (2)
- WestminsterResearch - UK (7)
- Worcester Research and Publications - Worcester Research and Publications - UK (1)
Resumo:
Data mining can be defined as the extraction of implicit, previously un-known, and potentially useful information from data. Numerous re-searchers have been developing security technology and exploring new methods to detect cyber-attacks with the DARPA 1998 dataset for Intrusion Detection and the modified versions of this dataset KDDCup99 and NSL-KDD, but until now no one have examined the performance of the Top 10 data mining algorithms selected by experts in data mining. The compared classification learning algorithms in this thesis are: C4.5, CART, k-NN and Naïve Bayes. The performance of these algorithms are compared with accuracy, error rate and average cost on modified versions of NSL-KDD train and test dataset where the instances are classified into normal and four cyber-attack categories: DoS, Probing, R2L and U2R. Additionally the most important features to detect cyber-attacks in all categories and in each category are evaluated with Weka’s Attribute Evaluator and ranked according to Information Gain. The results show that the classification algorithm with best performance on the dataset is the k-NN algorithm. The most important features to detect cyber-attacks are basic features such as the number of seconds of a network connection, the protocol used for the connection, the network service used, normal or error status of the connection and the number of data bytes sent. The most important features to detect DoS, Probing and R2L attacks are basic features and the least important features are content features. Unlike U2R attacks, where the content features are the most important features to detect attacks.