Improving Intrusion Detection System using the Combination of Neural Network and Genetic Algorithm

One of the essential issues in network-based systems is a fault attack which is caused by intrusion. It is the responsibility of intrusion detection to provide capabilities such as adaptation, fault tolerance, high computational speed, and error resilience in the face of noisy information. Thus, the construction of an efficient intrusion detection model is highly appreciated to increase the detection rates as well as to decrease false detection. Currently, researchers are more focusing on abnormal behaviour of network as this system can easily recognize new attacks without updating the daily recognized databases. However, the capability of current developing machine learning algorithms suffers from inefficient use of intrusion detection particularly once it involved some huge datasets of irrelevant and redundant features. The main objective of this thesis is to achieve the higher detection rate with lower false detection for attack recognition in order to support efficient application of intrusion detection. To achieve this goal, a new machine learning model was designed and developed to provide intelligent recognition with new attack patterns. New proposed and improved algorithm GA-ANN is constructed to support the proof of concept. For evaluation, five datasets namely KDD CUP 99 from the online data repositories are used in the experiment. In the above scenarios, GA-ANN provides the highest detection rate for pattern recognition which was 98.98% based on 18 selected features. This means that the proposed IDS model is significant and increases the network security.