Rough Set Theory Based Hybrid Method for Network Intrusion Detection

2013 ◽  
Vol 373-375 ◽  
pp. 815-818
Author(s):  
Na Jiao
Keyword(s):  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Hybrid Method ◽  
Rough Set Theory ◽  
Network Intrusion Detection ◽  
Large Network ◽  
Fuzzy C Means ◽  
Network Intrusion ◽  
International Knowledge

In this paper, we propose an intrusion detection method that combines rough set theory and Fuzzy C-Means for network intrusion detection. The first step consists of feature selection which is based on rough set theory. The next phase is clustering by using Fuzzy C-Means. Rough set theory is an efficient tool for further reducing redundancy. Fuzzy C-Means allows objects which are belong to several clusters simultaneously, with different degrees of membership. To evaluate the performance of the introduced approach, we applied them to the international Knowledge Discovery and Data mining intrusion detection dataset. In the experimentations, we compare the performance of the rough set theory based hybrid method for network intrusion detection. Experimental results illustrate that our algorithm is accurate model for handling complex attack patterns in large network. And the method can increase the efficiency and reduce the dataset by looking for overlapping categories.

A New Intrusion Detection Method Based on Rough Sets for Network Security

2014 ◽  
Vol 915-916 ◽  
pp. 1311-1314
Author(s):  
Na Jiao
Keyword(s):  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Sets ◽  
Rough Set Theory ◽  
Detection Method ◽  
Large Network ◽  
Fuzzy C Means ◽  
Attack Patterns ◽  
International Knowledge

In this paper, we introduce a new intrusion detection method based on rough sets for network. The first step consists of feature selection which is based on rough set theory. The next phase is clustering by using Fuzzy C-Means. Rough set theory is an efficient tool for further reducing redundancy. Fuzzy C-Means allows objects which are belong to several clusters simultaneously, with different degrees of membership. To evaluate the performance of the introduced approaches, we applied them to the international Knowledge Discovery and Data mining intrusion detection dataset. Experimental results illustrate that our algorithm is accurate models for handling complex attack patterns in large network.

A Survey on Data-driven Network Intrusion Detection

2022 ◽  
Vol 54 (9) ◽  
pp. 1-36
Author(s):  
Dylan Chou ◽  
Meng Jiang
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
Real World ◽  
Data Driven ◽  
Network Intrusion Detection ◽  
Large Network ◽  
Learning Models ◽  
Simulated Environments ◽  
Network Intrusion ◽  
Machine Learning Models

Data-driven network intrusion detection (NID) has a tendency towards minority attack classes compared to normal traffic. Many datasets are collected in simulated environments rather than real-world networks. These challenges undermine the performance of intrusion detection machine learning models by fitting machine learning models to unrepresentative “sandbox” datasets. This survey presents a taxonomy with eight main challenges and explores common datasets from 1999 to 2020. Trends are analyzed on the challenges in the past decade and future directions are proposed on expanding NID into cloud-based environments, devising scalable models for large network data, and creating labeled datasets collected in real-world networks.

NB Tree Based Intrusion Detection Technique Using Rough Set Theory Model

2019 ◽  
pp. 93-101
Author(s):  
Neha Gupta ◽  
Ritu Prasad ◽  
Praneet Saurabh ◽  
Bhupendra Verma
Keyword(s):  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Theory Model ◽  
Detection Technique

Intrusion Detection Using Modern Techniques

2011 ◽  
pp. 259-273
Author(s):  
Tarum Bhaskar ◽  
Narasimha Kamath B.
Keyword(s):  
Neural Network ◽  
Data Mining ◽  
Artificial Neural Network ◽  
Intrusion Detection ◽  
Set Theory ◽  
Rough Set ◽  
Intrusion Detection System ◽  
Rough Set Theory ◽  
Detection System ◽  
Mining Tools

Intrusion detection system (IDS) is now becoming an integral part of the network security infrastructure. Data mining tools are widely used for developing an IDS. However, this requires an ability to find the mapping from the input space to the output space with the help of available data. Rough sets and neural networks are the best known data mining tools to analyze data and help solve this problem. This chapter proposes a novel hybrid method to integrate rough set theory, genetic algorithm (GA), and artificial neural network. Our method consists of two stages: First, rough set theory is applied to find the reduced dataset. Second, the results are used as inputs for the neural network, where a GA-based learning approach is used to train the intrusion detection system. The method is characterized not only by using attribute reduction as a pre-processing technique of an artificial neural network but also by an improved learning algorithm. The effectiveness of the proposed method is demonstrated on the KDD cup data.

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