Network intrusion detection using hybrid binary PSO and random forests algorithm

2012 ◽  
Vol 8 (16) ◽  
pp. 2646-2660 ◽  
Author(s):  
Arif Jamal Malik ◽  
Waseem Shahzad ◽  
Farrukh Aslam Khan
Keyword(s):  
Intrusion Detection ◽  
Random Forests ◽  
Network Intrusion Detection ◽  
Network Intrusion ◽  
Binary Pso

scholarly journals A hybrid network intrusion detection framework based on random forests and weighted k-means

2013 ◽  
Vol 4 (4) ◽  
pp. 753-762 ◽  
Author(s):  
Reda M. Elbasiony ◽  
Elsayed A. Sallam ◽  
Tarek E. Eltobely ◽  
Mahmoud M. Fahmy
Keyword(s):  
Intrusion Detection ◽  
Random Forests ◽  
Hybrid Network ◽  
Network Intrusion Detection ◽  
Network Intrusion

Proposed Hybrid Classifier to Improve Network Intrusion Detection System using Data Mining Techniques

2020 ◽  
Vol 38 (1B) ◽  
pp. 6-14
Author(s):  
ٍٍSarah M. Shareef ◽  
Soukaena H. Hashim
Keyword(s):  
Intrusion Detection ◽  
False Alarm ◽  
Intrusion Detection System ◽  
Detection System ◽  
Multinomial Logistic Regression ◽  
Network Intrusion Detection ◽  
Limiting Factor ◽  
Network Intrusion ◽  
Network Intrusion Detection System ◽  
Normal Behavior

Network intrusion detection system (NIDS) is a software system which plays an important role to protect network system and can be used to monitor network activities to detect different kinds of attacks from normal behavior in network traffics. A false alarm is one of the most identified problems in relation to the intrusion detection system which can be a limiting factor for the performance and accuracy of the intrusion detection system. The proposed system involves mining techniques at two sequential levels, which are: at the first level Naïve Bayes algorithm is used to detect abnormal activity from normal behavior. The second level is the multinomial logistic regression algorithm of which is used to classify abnormal activity into main four attack types in addition to a normal class. To evaluate the proposed system, the KDDCUP99 dataset of the intrusion detection system was used and K-fold cross-validation was performed. The experimental results show that the performance of the proposed system is improved with less false alarm rate.

Cooperative network intrusion detection based on data fusion

2009 ◽  
Vol 29 (1) ◽  
pp. 284-287 ◽  
Author(s):  
Wei ZHANG ◽  
Shao-hua TENG ◽  
Xiu-fen FU
Keyword(s):  
Intrusion Detection ◽  
Data Fusion ◽  
Network Intrusion Detection ◽  
Cooperative Network ◽  
Network Intrusion
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