TY  - JOUR
T1  - Enhance of Extreme Learning Machine-Genetic Algorithm
Hybrid Based on Intrusion Detection System
AU - Hasan Ali, Mohammed AU - Fadli Zolkipli, Mohamad AU - Mohammed, Mohammed Abdulameer AU - Musa Jaber, Mustafa 
JO  - Journal of Engineering and Applied Sciences
VL  - 12
IS  - 16
SP  - 4180
EP  - 4185
PY  - 2017
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2017.4180.4185
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2017.4180.4185
KW  - ELM
KW  -SLFN
KW  -ANN
KW  -IDS
KW  -GA
KW  -Malaysia
AB  - This study presents a new scheme of the hybrid Extreme Learning Machine-Genetic Algorithm
(ELM-GA). ELM has been proved to be exceptionally fast and achieves more generalized performance for
learning Single hidden Layer Feedforward Neural networks (SLFN). However, due to the random determination
of parameters for hidden nodes and the number of hidden neurons, some un-optimal parameters may be
generated to influence the generalization performance and stability. Some of the papers used GA as a hybrid
to solve this problem in ELM but ELM-GA still has some limitations where they used the GA to find the optimal
weights for the ELM. In this research, we try to let the GA not only find the best weights but find the best
classifier (weights and structure). Intrusion Detection System (IDS) facing big challenge in high rate of false
alarms. This research proposes a new method in validation of the classifiers to be sure that the classifiers
training enough to mitigate the false alarm’s rates.
ER  -