Using Rough Set and Support Vector Machine for Network Intrusion Detection System

By Rung Ching Chen, Kai-Fan Cheng, Ying-Hao Chen, Chia-Fen Hsieh. 2009

173 citations recorded by the source corpus.

The main function of IDS (Intrusion Detection System) is to protect the system, analyze and predict the behaviors of users. Then these behaviors will be considered an attack or a normal behavior. Though IDS has been developed for many years, the large number of return alert messages makes managers maintain system inefficiently. In this paper, we use RST (Rough Set Theory) and SVM (Support Vector Machine) to detect intrusions. First, RST is used to preprocess the data and reduce the dimensions. Next, the features selected by RST will be sent to SVM model to learn and test respectively. The method is effective to decrease the space density of data. The experiments will compare the results with different methods and show RST and SVM schema could improve the false positive rate and accuracy.

Subfields: Artificial Intelligence, Computer Networks and Communications, Signal Processing

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Giuseppe Ateniese
2. Monowar Hussain Bhuyan
3. Luigi Vincenzo Mancini
4. Angelo Spognardi
5. Jugal Kumar Kalita
6. Dhruba Kumar Bhattacharyya
7. Kui Ren
8. Wenjing Lou

In Computer Networks and Communications

1. Monowar Hussain Bhuyan
2. Luigi Vincenzo Mancini
3. Giuseppe Ateniese
4. Angelo Spognardi
5. Jugal Kumar Kalita
6. Dhruba Kumar Bhattacharyya
7. André Costa Drummond
8. Paulo Angelo Alves Resende

In Signal Processing

1. Monowar Hussain Bhuyan
2. Giuseppe Ateniese
3. Angelo Spognardi
4. Jugal Kumar Kalita
5. Dhruba Kumar Bhattacharyya
6. Saoussen Krichen
7. Chaouki Khammassi

Citations in the corpus

• Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers (2013, background): “tially introduced to solve pattern recognition problems in an efficient way ([20]), nowadays they are suitable in several contexts. In fact, SVMs are used for intrusion detection and anomaly detection ([39, 34, 22]) or as part of complex systems for similar tasks ([48, 6]). Other authors propose SVM-b”
• Network Anomaly Detection: Methods, Systems and Tools (2014, methodology): “present a two-step classifier for network intrusion detection [152].”
• A novel feature-selection approach based on the cuttlefish optimization algorithm for intrusion detection systems (2015, methodology): “The proposed model is evaluated based on three performance measures: The detection rate (DR), False Positive Rate (FPR), and Accuracy Rate (AR) (Chen, Cheng, & Hsieh, 2009).”