Journal Article

Advanced Feature Extraction and Selection Approach Using Deep Learning and Aquila Optimizer for IoT Intrusion Detection System

Fatani, Abdulaziz, Al-qaness, Mohammed A. A., Dahou, Abdelghani, Abd Elaziz, Mohamed Abd, Lu, Songfeng

2021

Overview

Developing cyber security is very necessary and has attracted considerable attention from academy and industry organizations worldwide. It is also very necessary to provide sustainable computing for the the Internet of Things (IoT). Machine learning techniques play a vital role in the cybersecurity of the IoT for intrusion detection and malicious identification. Thus, in this study, we develop new feature extraction and selection methods and for the IDS system using the advantages of the swarm intelligence (SI) algorithms. We design a feature extraction mechanism depending on the conventional neural networks (CNN). After that, we present an alternative feature selection (FS) approach using the recently developed SI algorithm, Aquila optimizer (AQU). Moreover, to assess the quality of the developed IDS approach, four well-known public datasets, CIC2017, NSL-KDD, BoT-IoT, and KDD99, were used. We also considered extensive comparisons to other optimization methods to verify the competitive performance of the developed method. The results show the high performance of the developed approach using different evaluation indicators.

• Publisher: MDPI AG,MDPI
• Subject: Accuracy / Aquila optimizer / Classification / Cybersecurity / Datasets / Deep learning / Exploitation / Feature selection / Internet of Things / intrusion detection system / Intrusion detection systems / Machine learning / Neural networks / Optimization algorithms / Optimization techniques / Support vector machines / sustainable computing / swarm Intelligence