An efficient intrusion detection system for identification from suspicious URLs using data mining algorithms

2017 ◽  
Vol 12 (2) ◽  
pp. 133
Author(s):  
Kotoju Rajitha ◽  
Doddapaneni VijayaLakshmi
Keyword(s):  
Data Mining ◽  
Intrusion Detection ◽  
Intrusion Detection System ◽  
Detection System ◽  
Data Mining Algorithms ◽  
Using 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.

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