Network intrusion detection and classification with Decision Tree and rule based approaches

2009 9th International Symposium on Communications and Information Technology ◽

10.1109/iscit.2009.5341005 ◽

2009 ◽

Author(s):

Thanvarat Komviriyavut ◽

Phurivit Sangkatsanee ◽

Naruemon Wattanapongsakorn ◽

Chalermpol Charnsripinyo

Keyword(s):

Intrusion Detection ◽

Decision Tree ◽

Network Intrusion Detection ◽

Network Intrusion

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Evolutionary optimization of a fuzzy rule-based network intrusion detection system

2010 Annual Meeting of the North American Fuzzy Information Processing Society ◽

10.1109/nafips.2010.5548289 ◽

2010 ◽

Author(s):

Terrence P. Fries

Keyword(s):

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Evolutionary Optimization ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Network Intrusion Detection System

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DAREnsemble: Decision Tree and Rule Learner Based Ensemble for Network Intrusion Detection System

Proceedings of First International Conference on Information and Communication Technology for Intelligent Systems: Volume 1 - Smart Innovation, Systems and Technologies ◽

10.1007/978-3-319-30933-0_20 ◽

2016 ◽

pp. 185-193 ◽

Author(s):

Dwarkoba Gaikwad ◽

Ravindra Thool

Keyword(s):

Intrusion Detection ◽

Decision Tree ◽

Intrusion Detection System ◽

Detection System ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Network Intrusion Detection System

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Network intrusion detection based on deep learning model optimized with rule-based hybrid feature selection

Information Security Journal A Global Perspective ◽

10.1080/19393555.2020.1767240 ◽

2020 ◽

Vol 29 (6) ◽

pp. 267-283

Author(s):

Femi Emmanuel Ayo ◽

Sakinat Oluwabukonla Folorunso ◽

Adebayo A. Abayomi-Alli ◽

Adebola Olayinka Adekunle ◽

Joseph Bamidele Awotunde

Keyword(s):

Feature Selection ◽

Deep Learning ◽

Intrusion Detection ◽

Learning Model ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Deep Learning Model

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An Efficient Network Intrusion Detection Based on Decision Tree Classifier and Simple K-Mean Clustering using Dimensionality Reduction � A Review

International Journal on Recent and Innovation Trends in Computing and Communication ◽

10.17762/ijritcc2321-8169.150276 ◽

2015 ◽

Vol 3 (2) ◽

pp. 789-791 ◽

Author(s):

Vandna Malviya ◽

Keyword(s):

Intrusion Detection ◽

Decision Tree ◽

Dimensionality Reduction ◽

Network Intrusion Detection ◽

Decision Tree Classifier ◽

Network Intrusion ◽

Tree Classifier

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Network intrusion detection system using J48 Decision Tree

2015 International Conference on Advances in Computing, Communications and Informatics (ICACCI) ◽

10.1109/icacci.2015.7275914 ◽

2015 ◽

Author(s):

Shailendra Sahu ◽

B M Mehtre

Keyword(s):

Intrusion Detection ◽

Decision Tree ◽

Intrusion Detection System ◽

Detection System ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Network Intrusion Detection System ◽

J48 Decision Tree

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A MACHINE LEARNING APPROACH WITH VERIFICATION OF PREDICTIONS AND ASSISTED SUPERVISION FOR A RULE-BASED NETWORK INTRUSION DETECTION SYSTEM

Proceedings of the Fourth International Conference on Web Information Systems and Technologies ◽

10.5220/0001524801430148 ◽

2008 ◽

Keyword(s):

Machine Learning ◽

Intrusion Detection ◽

Intrusion Detection System ◽

Detection System ◽

Network Intrusion Detection ◽

Learning Approach ◽

Network Intrusion ◽

Network Intrusion Detection System ◽

Machine Learning Approach

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Improved Privacy Preserving decision tree Approach for Network Intrusion Detection

International Journal of Computer & Organization Trends ◽

10.14445/22492593/ijcot-v30p301 ◽

2016 ◽

Vol 30 (1) ◽

pp. 1-6

Author(s):

S.Navya Sai ◽

◽

K.Kishore Raju

Keyword(s):

Intrusion Detection ◽

Decision Tree ◽

Privacy Preserving ◽

Network Intrusion Detection ◽

Network Intrusion ◽

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A Decision Tree Algorithm for Distributed Data Mining: Towards Network Intrusion Detection

Computational Science and Its Applications – ICCSA 2004 - Lecture Notes in Computer Science ◽

10.1007/978-3-540-24768-5_22 ◽

2004 ◽

pp. 206-212 ◽

Author(s):

Sung Baik ◽

Jerzy Bala

Keyword(s):

Data Mining ◽

Intrusion Detection ◽

Decision Tree ◽

Distributed Data Mining ◽

Network Intrusion Detection ◽

Distributed Data ◽

Decision Tree Algorithm ◽

Tree Algorithm ◽

Network Intrusion

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Anomaly Base Network Intrusion Detection by Using Random Decision Tree and Random Projection: A Fast Network Intrusion Detection Technique

Network Protocols and Algorithms ◽

10.5296/npa.v3i4.1342 ◽

2011 ◽

Vol 3 (4) ◽

Author(s):

Mahendra Singh Sisodia ◽

Virendra Raghuwanshi

Keyword(s):

Intrusion Detection ◽

Decision Tree ◽

Random Projection ◽

Detection Technique ◽

Network Intrusion Detection ◽

Network Intrusion ◽

Fast Network ◽

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Study of Intrusion Detection Based on Genetic Algorithm and Rule

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.602-605.1797 ◽

2014 ◽

Vol 602-605 ◽

pp. 1797-1802

Author(s):

Li Wen Xu ◽

Li Juan Qiao ◽

Xun Yong Ou ◽

Kun Zhang

Keyword(s):

Genetic Algorithm ◽

Information Technology ◽

Intrusion Detection ◽

Maintenance Cost ◽

Network Intrusion Detection ◽

Technology Usage ◽

Efficient Approach ◽

Network Intrusion ◽

The rapid increase of information technology usage demands the high level of security in order to keep the data resources and equipments of the user secure. In this current era of networks, there is an eventual stipulate for development which is consistent, extensible and easily manageable, with low maintenance cost solutions for Intrusion Detection. Network Intrusion Detection based on rules formulation is an efficient approach to classify various types of attack. DoS or Probing attack are relatively more common, and can be detected more accurately if contributing parameters are formulated in terms of rules. Genetic Algorithm is used to devise such rule. It is found that accuracy of rule based learning increases with the number of iteration.

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