Open Access
ARTICLE
Towards Improving the Intrusion Detection through ELM (Extreme Learning Machine)
Iftikhar Ahmad1, *, Rayan Atteah Alsemmeari1
1 D.I.T, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, 21589, Saudi Arabia.
* Corresponding Author: Iftikhar Ahmad. Email: .
(This article belongs to the Special Issue: Management of Security, Privacy and Trust of Multimedia Data in Mobile devices communication)
Computers, Materials & Continua 2020, 65(2), 1097-1111. https://doi.org/10.32604/cmc.2020.011732
Received 26 May 2020; Accepted 10 July 2020; Issue published 20 August 2020
Abstract
An IDS (intrusion detection system) provides a foremost front line mechanism
to guard networks, systems, data, and information. That’s why intrusion detection has
grown as an active study area and provides significant contribution to cyber-security
techniques. Multiple techniques have been in use but major concern in their
implementation is variation in their detection performance. The performance of IDS lies
in the accurate detection of attacks, and this accuracy can be raised by improving the
recognition rate and significant reduction in the false alarms rate. To overcome this
problem many researchers have used different machine learning techniques. These
techniques have limitations and do not efficiently perform on huge and complex data
about systems and networks. This work focused on ELM (Extreme Learning Machine)
technique due to its good capabilities in classification problems and dealing with huge
data. The ELM has different activation functions, but the problem is to find out which
function is more suitable and performs well in IDS. This work investigates this problem.
Here, Well-known activation functions like: sine, sigmoid and radial basis are explored,
investigated and applied to measure their performance on the GA (Genetic Algorithm)
features subset and with full features set. The NSL-KDD dataset is used as a benchmark.
The empirical results are analyzed, addressed and compared among different activation
functions of the ELM. The results show that the radial basis and sine functions perform
better on GA feature set than the full feature set while the performance of the sigmoid
function is almost equal on both features sets. So, the proposal of GA based feature
selection reduced 21 features out of 41 that brought up to 98% accuracy and enhanced
overall efficiency of extreme learning machine in intrusion detection.
Keywords
Cite This Article
I. Ahmad and R. Atteah Alsemmeari, "Towards improving the intrusion detection through elm (extreme learning machine),"
Computers, Materials & Continua, vol. 65, no.2, pp. 1097–1111, 2020. https://doi.org/10.32604/cmc.2020.011732
Citations