TY  - EJOU
AU  - Haider, Amir 
AU  - Khan, Muhammad Adnan 
AU  - Rehman, Abdur 
AU  - Rahman, Muhib Ur 
AU  - Kim, Hyung Seok 

TI  - A Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System
T2  - Computers, Materials \& Continua

PY  - 2021
VL  - 66
IS  - 2
SN  - 1546-2226

AB  - In recent years, cybersecurity has attracted significant interest due to the rapid growth of the Internet of Things (IoT) and the widespread development of computer infrastructure and systems. It is thus becoming particularly necessary to identify cyber-attacks or irregularities in the system and develop an efficient intrusion detection framework that is integral to security. Researchers have worked on developing intrusion detection models that depend on machine learning (ML) methods to address these security problems. An intelligent intrusion detection device powered by data can exploit artificial intelligence (AI), and especially ML, techniques. Accordingly, we propose in this article an intrusion detection model based on a Real-Time Sequential Deep Extreme Learning Machine Cybersecurity Intrusion Detection System (RTS-DELM-CSIDS) security model. The proposed model initially determines the rating of security aspects contributing to their significance and then develops a comprehensive intrusion detection framework focused on the essential characteristics. Furthermore, we investigated the feasibility of our proposed RTS-DELM-CSIDS framework by performing dataset evaluations and calculating accuracy parameters to validate. The experimental findings demonstrate that the RTS-DELM-CSIDS framework outperforms conventional algorithms. Furthermore, the proposed approach has not only research significance but also practical significance.
KW  - Security; DELM; intrusion detection system; machine learning

DO  - 10.32604/cmc.2020.013910