Survey on Hybrid Data Mining Algorithms for Intrusion Detection System

2018 ◽  
pp. 291-298
Author(s):  
Harshal N. Datir ◽  
Pradip M. Jawandhiya
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Data Mining Algorithms ◽  
Hybrid Data ◽  
Mining Algorithms

scholarly journals A Comparative Study of Data Mining Algorithms for High Detection Rate in Intrusion Detection System

2018 ◽  
Vol 2 (1) ◽  
pp. 49-57 ◽  
Author(s):  
Nabeela Ashraf ◽  
Waqar Ahmad ◽  
Rehan Ashraf
Keyword(s):  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection Rate ◽  
Detection System ◽  
Huge Amount ◽  
High Detection Rate ◽  
Data Mining Algorithms ◽  
Secure Networks ◽  
Mining Algorithms ◽  
Speed And Accuracy

Due to the fast growth and tradition of the internet over the last decades, the network security problems are increasing vigorously. Humans can not handle the speed of processes and the huge amount of data required to handle network anomalies. Therefore, it needs substantial automation in both speed and accuracy. Intrusion Detection System is one of the approaches to recognize illegal access and rare attacks to secure networks. In this proposed paper, Naive Bayes, J48 and Random Forest classifiers are compared to compute the detection rate and accuracy of IDS. For experiments, the KDD_NSL dataset is used.

Problems of KDD Cup 99 Dataset Existed and Data Preprocessing

2014 ◽  
Vol 667 ◽  
pp. 218-225 ◽  
Author(s):  
Yan Wang ◽  
Kun Yang ◽  
Xiang Jing ◽  
Huang Long Jin
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Detection System ◽  
Data Preprocessing ◽  
Vital Role ◽  
Data Mining Algorithms ◽  
Network Intrusion ◽  
Depth Analysis ◽  
Mining Algorithms ◽  
Kdd Cup 99

KDD Cup 99 dataset is not only the most widely used dataset in intrusion detection, but also the de facto benchmark on evaluating the performance merits of intrusion detection system. Nevertheless there are a lot of issues in this dataset which cannot be omitted. In order to establish good data mining models in intrusion detection and find the appropriate network intrusion attack types’ features, researchers should have a well-known understanding on this dataset. In this paper, first and foremost we have made an in-depth analysis on the problems which the dataset are existed, and given the related solutions. Secondly, we also have carried out plenty data preprocessing on the 10% subset of KDD Cup 99 dataset’s training set, giving better results to the following process. What’s more, by comparing 10 common kinds of data mining algorithms in our experiment, we have analyzed and summarized that data preprocessing plays a vital role on the performance and importance to data mining algorithms.

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