@Article{cmc.2020.013121,
AUTHOR = {Muhammad Adnan Khan, Abdur Rehman, Khalid Masood Khan, Mohammed A. Al Ghamdi, Sultan H. Almotiri},
TITLE = {Enhance Intrusion Detection in Computer Networks Based on Deep Extreme Learning Machine},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {66},
YEAR = {2021},
NUMBER = {1},
PAGES = {467--480},
URL = {http://www.techscience.com/cmc/v66n1/40459},
ISSN = {1546-2226},
ABSTRACT = {Networks provide a significant function in everyday life, and cybersecurity therefore developed a critical field of study. The Intrusion detection system
(IDS) becoming an essential information protection strategy that tracks the situation of the software and hardware operating on the network. Notwithstanding
advancements of growth, current intrusion detection systems also experience dif-
ficulties in enhancing detection precision, growing false alarm levels and identifying suspicious activities. In order to address above mentioned issues, several
researchers concentrated on designing intrusion detection systems that rely on
machine learning approaches. Machine learning models will accurately identify
the underlying variations among regular information and irregular information
with incredible efficiency. Artificial intelligence, particularly machine learning
methods can be used to develop an intelligent intrusion detection framework.
There in this article in order to achieve this objective, we propose an intrusion
detection system focused on a Deep extreme learning machine (DELM) which
first establishes the assessment of safety features that lead to their prominence
and then constructs an adaptive intrusion detection system focusing on the important features. In the moment, we researched the viability of our suggested DELMbased intrusion detection system by conducting dataset assessments and evaluating the performance factors to validate the system reliability. The experimental
results illustrate that the suggested framework outclasses traditional algorithms.
In fact, the suggested framework is not only of interest to scientific research
but also of functional importance.},
DOI = {10.32604/cmc.2020.013121}
}