Network intrusion detection system by using genetic algorithm

Hamizan Suhaimi, Saiful Izwan Suliman, Ismail Musirin, Afdallyna Fathiyah Harun, Roslina Mohamad


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.


Intrusion detection, Genetic algorithm, Pattern recognition

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