Network intrusion detection system by using genetic algorithm

Developing a better intrusion detection systems (IDS) has attracted many researchers in the area of computer network for the past decades. In this paper, Genetic Algorithm (GA) is proposed as a tool that capable to identify harmful type of connections in a computer network. Different features of con...

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Autori principali: Suhaimi, Hamizan (Autore), Suliman, Saiful Izwan (Autore), Musirin, Ismail (Autore), Harun, Afdallyna Fathiyah (Autore), Mohamad, Roslina (Autore)
Natura: EJournal Article
Pubblicazione: Institute of Advanced Engineering and Science, 2019-12-01.
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001 ijeecs20391_13196
042 |a dc 
100 1 0 |a Suhaimi, Hamizan  |e author 
100 1 0 |e contributor 
700 1 0 |a Suliman, Saiful Izwan  |e author 
700 1 0 |a Musirin, Ismail  |e author 
700 1 0 |a Harun, Afdallyna Fathiyah  |e author 
700 1 0 |a Mohamad, Roslina  |e author 
245 0 0 |a Network intrusion detection system by using genetic algorithm 
260 |b Institute of Advanced Engineering and Science,   |c 2019-12-01. 
500 |a https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20391 
520 |a Developing a better intrusion detection systems (IDS) has attracted many researchers in the area of computer network for the past decades. In this paper, Genetic Algorithm (GA) is proposed as a tool that capable to identify harmful type of connections in a computer network. Different features of connection data such as duration and types of connection in network were analyzed to generate a set of classification rule. For this project, standard benchmark dataset known as KDD Cup 99 was investigated and utilized to study the effectiveness of the proposed method on this problem domain. The rules comprise of eight variables that were simulated during the training process to detect any malicious connection that can lead to a network intrusion. With good performance in detecting bad connections, this method can be applied in intrusion detection system to identify attack thus improving the security features of a computer network. 
540 |a Copyright (c) 2019 Institute of Advanced Engineering and Science 
540 |a http://creativecommons.org/licenses/by-nc/4.0 
546 |a eng 
690
690 |a Intrusion detection, Genetic algorithm, Pattern recognition 
655 7 |a info:eu-repo/semantics/article  |2 local 
655 7 |a info:eu-repo/semantics/publishedVersion  |2 local 
655 7 |2 local 
786 0 |n Indonesian Journal of Electrical Engineering and Computer Science; Vol 16, No 3: December 2019; 1593-1599 
786 0 |n 2502-4760 
786 0 |n 2502-4752 
786 0 |n 10.11591/ijeecs.v16.i3 
787 0 |n https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20391/13196 
856 4 1 |u https://ijeecs.iaescore.com/index.php/IJEECS/article/view/20391/13196  |z Get fulltext