DataVXApplied SciencesPaper
journal article·2022-08-30Open access

Planet Optimization with Deep Convolutional Neural Network for Lightweight Intrusion Detection in Resource-Constrained IoT Networks

Khalid A. Alissa, Fatma S. Alrayes, Khaled Tarmissi, Ayman Yafoz, Raed Alsini, Omar Alghushairy, Mahmoud Othman, Abdelwahed Motwakel

Applied Sciences· Volume 12· Issue 17
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Source abstract

Abstract

Cyber security is becoming a challenging issue, because of the growth of the Internet of Things (IoT), in which an immense quantity of tiny smart gadgets push trillions of bytes of data over the Internet. Such gadgets have several security flaws, due to a lack of hardware security support and defense mechanisms, thus, making them prone to cyber-attacks. Moreover, IoT gateways present limited security features for identifying such threats, particularly the absence of intrusion detection techniques powered by deep learning (DL). Certainly, DL methods need higher computational power that exceeds the capability of such gateways. This article focuses on the development of Planet Optimization with a deep convolutional neural network for lightweight intrusion detection (PODCNN-LWID) in a resource-constrained IoT environment. The presented PODCNN-LWID technique primarily aims to identify and categorize intrusions. In the presented PODCNN-LWID model, two major processes are involved, namely, classification and parameter tuning. At the primary stage, the PODCNN-LWID technique applies a DCNN model for the intrusion identification process. Next, in the second stage, the PODCNN-LWID model utilizes the PO algorithm as a hyperparameter tuning process. The experimental validation of the PODCNN-LWID model is carried out on a benchmark dataset, and the results are assessed using varying measures. The comparison study reports the enhancements of the PODCNN-LWID model over other approaches.
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Research context

OpenAlex topics: Network Security and Intrusion Detection · Anomaly Detection Techniques and Applications · Advanced Malware Detection Techniques

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

SAUDI ARAMCO Cybersecurity Chair, Networks and Communications Department, College of Computer Science and Information Technology, Imam Abdulrahman Bin Faisal University, P.O. Box 1982, Dammam 31441, Saudi Arabia

Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia

Department of Computer Sciences, College of Computing and Information System, Umm Al-Qura University, P.O. Box 5555, Makkah 21955, Saudi Arabia

Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, P.O. Box 80200, Jeddah 21589, Saudi Arabia

Department of Information Systems and Technology, College of Computer Science and Engineering, University of Jeddah, Jeddah 21589, Saudi Arabia

Department of Computer Science, Faculty of Computers and Information Technology, Future University in Egypt, New Cairo 11835, Egypt

Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj 11942, Saudi Arabia

Funding

Princess Nourah bint Abdulrahman University Researchers Supporting Project · Scientific Research at Umm Al-Qura University

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