Robust DDoS Attack Detection Using Piecewise Harris Hawks Optimizer with Deep Learning for a Secure Internet of Things Environment

Mahmoud Ragab (), Sultanah M. Alshammari, Louai A. Maghrabi, Dheyaaldin Alsalman, Turki Althaqafi and Abdullah AL-Malaise AL-Ghamdi
Additional contact information
Mahmoud Ragab: Information Technology Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Sultanah M. Alshammari: Center of Excellence in Smart Environment Research, King Abdulaziz University, Jeddah 21589, Saudi Arabia
Louai A. Maghrabi: Department of Software Engineering, College of Engineering, University of Business and Technology, Jeddah, Saudi Arabia
Dheyaaldin Alsalman: Department of Cybersecurity, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Saudi Arabia
Turki Althaqafi: Information Systems Department, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Saudi Arabia
Abdullah AL-Malaise AL-Ghamdi: Information Systems Department, School of Engineering, Computing and Design, Dar Al-Hekma University, Jeddah 22246, Saudi Arabia

Mathematics, 2023, vol. 11, issue 21, 1-18

Abstract: The Internet of Things (IoT) refers to the network of interconnected physical devices that are embedded with software, sensors, etc., allowing them to exchange and collect information. Although IoT devices have several advantages and can improve people’s efficacy, they also pose a security risk. The malicious actor frequently attempts to find a new way to utilize and exploit specific resources, and an IoT device is an ideal candidate for such exploitation owing to the massive number of active devices. Especially, Distributed Denial of Service (DDoS) attacks include the exploitation of a considerable number of devices like IoT devices, which act as bots and transfer fraudulent requests to the services, thereby obstructing them. There needs to be a robust system of detection based on satisfactory methods for detecting and identifying whether these attacks have occurred or not in a network. The most widely used technique for these purposes is artificial intelligence (AI), which includes the usage of Deep Learning (DL) and Machine Learning (ML) to find cyberattacks. The study presents a Piecewise Harris Hawks Optimizer with an Optimal Deep Learning Classifier (PHHO-ODLC) for a secure IoT environment. The fundamental goal of the PHHO-ODLC algorithm is to detect the existence of DDoS attacks in the IoT platform. The PHHO-ODLC method follows a three-stage process. At the initial stage, the PHHO algorithm can be employed to choose relevant features and thereby enhance the classification performance. Next, an attention-based bidirectional long short-term memory (ABiLSTM) network can be applied to the DDoS attack classification process. Finally, the hyperparameter selection of the ABiLSTM network is carried out by the use of a grey wolf optimizer (GWO). A widespread simulation analysis was performed to exhibit the improved detection accuracy of the PHHO-ODLC technique. The extensive outcomes demonstrated the significance of the PHHO-ODLC technique regarding the DDoS attack detection technique in the IoT platform.

Keywords: cybersecurity; DDoS attacks; network security; Internet of Things; artificial intelligence; metaheuristics (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/21/4448/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/21/4448/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:21:p:4448-:d:1268489

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().