Application and Performance Analysis of Data Preprocessing for Intrusion Detection System

2019 ◽  
pp. 163-177
Author(s):  
Shuai Jiang ◽  
Xiaolong Xu
Keyword(s):  
Performance Analysis ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Data Preprocessing ◽  
And Performance

Data Preprocessing for Network Intrusion Detection

2010 ◽  
Vol 20-23 ◽  
pp. 867-871
Author(s):  
Li Li ◽  
Ye Yuan
Keyword(s):  
Intrusion Detection ◽  
Categorical Data ◽  
Intrusion Detection System ◽  
Detection System ◽  
Data Preprocessing ◽  
Network Intrusion Detection ◽  
Paper Briefly ◽  
Network Intrusion ◽  
The Neural Network ◽  
Data Source

Most of IDS(Intrusion Detection System) are very particular about data source which might be asked to be categorical data or need to be correctly labeled. Therefore, the data preprocessing is an indispensable part in intrusion detecting. KDD Cpu 1999 Dataset is usually used for experimental data. This paper briefly introduces the features and the structure of the KDD Cpu 1999 Dataset and presents the method of the data preprocessing at Intrusion Detection System based on the neural network clustering’s algorithm.

Packet Sampling Applications in Campus High-Speed Network Intrusion Detection System

2013 ◽  
Vol 760-762 ◽  
pp. 2010-2013
Author(s):  
Hui Qing Qiu ◽  
Cong Wang ◽  
Jie Lu
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
High Speed ◽  
Detection System ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
High Speed Network ◽  
Network Intrusion Detection System ◽  
Packet Sampling ◽  
And Performance

A technique of high-speed network intrusion detection system based on packet sampling theory is proposed. Starting with basic principles of packet sampling, this paper first analyses the significant mathematical conclusion of sampling strategies, then after discussing current strategies, mechanism and performance of different packet sampling methods, we specify an efficient strategy of packet sampling. Results show that this method can attain above 55% accurate rate with below 1% false rate in 94 specified attacking cases from DARPA 2000 IDS evaluation dataset.

scholarly journals An Application and Performance Evaluation of Twin Extreme Learning Machine Classifier for Intrusion Detection

2019 ◽  
Vol 8 (9) ◽  
pp. 219-222
Keyword(s):  
Intrusion Detection ◽  
Extreme Learning Machine ◽  
Intrusion Detection System ◽  
Detection System ◽  
Twin Support Vector Machine ◽  
Support Vector ◽  
Detection Techniques ◽  
Learning Machines ◽  
And Performance ◽  
Learning Machine

Network along with Security is most significant in the digitalized environment. It is necessary to secure data from hackers and intruders. A strategy involved in protection of information from hackers will be termed as Intrusion Detection System (IDS).By taking into nature of attack or the usual conduct of user, investigation along with forecasting activities of the clients will be performed by mentioned system.Variousstrategies are utilized for the intrusion detection system. For the purpose of identification of hacking activity, utilization of machine learning based approach might be considered as novel strategy.In this paper, for identification of the hacking activity will be carried out by Twin Extreme Learning Machines (TELM).Employing the concept of Twin Support Vector Machine with the fundamental structure of Extreme Learning Machine is considered in the establishment of Twin Extreme Learning Machine (TELM).Also, its performance and accuracy are compared with the other intrusion detection techniques

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