Research on the Performance Analysis of Network Intrusion Detection of AC Algorithm

2014 ◽  
Vol 886 ◽  
pp. 646-649
Author(s):  
Dong Ming Zhao
Keyword(s):  
Performance Analysis ◽  
Intrusion Detection ◽  
Network Intrusion Detection ◽  
Good Efficiency ◽  
Use Of Resources ◽  
Network Intrusion ◽  
Simulation Results

As the use of computers become more widely, its intrusion security has become a key issue. In order to solve this problem, this article proposes a analysis research on AC algorithm for the detection of network intrusion. Through the research on the patterns of network intrusion, the AC algorithm for the detection of network intrusion is proposed and analyzed. Simulation results show that the proposed AC algorithm of network intrusion detection has a good efficiency, higher accuracy and efficient use of resources.

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.

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