Cascaded classifier approach based on Adaboost to increase detection rate of rare network attack categories

Author(s):  
P. Natesan ◽  
P. Rajesh
2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Jayakumar Kaliappan ◽  
Revathi Thiagarajan ◽  
Karpagam Sundararajan

An intrusion detection system (IDS) helps to identify different types of attacks in general, and the detection rate will be higher for some specific category of attacks. This paper is designed on the idea that each IDS is efficient in detecting a specific type of attack. In proposed Multiple IDS Unit (MIU), there are five IDS units, and each IDS follows a unique algorithm to detect attacks. The feature selection is done with the help of genetic algorithm. The selected features of the input traffic are passed on to the MIU for processing. The decision from each IDS is termed as local decision. The fusion unit inside the MIU processes all the local decisions with the help of majority voting rule and makes the final decision. The proposed system shows a very good improvement in detection rate and reduces the false alarm rate.


2007 ◽  
Vol 177 (4S) ◽  
pp. 651-651
Author(s):  
Nicolas B. Delongchamps ◽  
Vishal Chandan ◽  
Richard Jones ◽  
Gregory Threatte ◽  
Mary Jumbelic ◽  
...  

2004 ◽  
Vol 171 (4S) ◽  
pp. 479-479
Author(s):  
Roger Paul ◽  
Christian Korzineck ◽  
Ulrike Necknig ◽  
Herbert Leyh ◽  
Thomas Niesel ◽  
...  

2019 ◽  
Author(s):  
S Michopoulos ◽  
G Axiaris ◽  
P Baxevanis ◽  
M Stoupaki ◽  
V Gagari ◽  
...  

2019 ◽  
Author(s):  
T Rath ◽  
F Vitali ◽  
E Klenske ◽  
J Siebler ◽  
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...  

2019 ◽  
Author(s):  
E Waldmann ◽  
H Sinkovec ◽  
G Heinze ◽  
D Penz ◽  
B Majcher ◽  
...  

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