Improved Radial Movement Optimization With Fuzzy Neural Network Enabled Anomaly Detection for IoT Assisted Smart Cities

Fatma S. Alrayes; Wafa Mtouaa; Sumayh S. Aljameel; Mashael Maashi; Mohammed Rizwanullah; Ahmed S. Salama

doi:10.1109/ACCESS.2023.3342698

IEEE Access (Jan 2023)

Improved Radial Movement Optimization With Fuzzy Neural Network Enabled Anomaly Detection for IoT Assisted Smart Cities

Fatma S. Alrayes,
Wafa Mtouaa,
Sumayh S. Aljameel,
Mashael Maashi,
Mohammed Rizwanullah,
Ahmed S. Salama

Affiliations

Fatma S. Alrayes: ORCiD; Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, Saudi Arabia
Wafa Mtouaa: Department of Mathematics, Faculty of Sciences and Arts, King Khalid University, Muhayil, Asir, Saudi Arabia
Sumayh S. Aljameel: ORCiD; Department of Computer Science, College of Computer Science and Information Technology, Saudi Aramco Cybersecurity Chair, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
Mashael Maashi: ORCiD; Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh, Saudi Arabia
Mohammed Rizwanullah: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, Al-Kharj, Saudi Arabia
Ahmed S. Salama: ORCiD; Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, Egypt

DOI: https://doi.org/10.1109/ACCESS.2023.3342698
Journal volume & issue: Vol. 11
pp. 143060 – 143068

Abstract

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Recently, an extensive implementation of the recent Internet of Things (IoT) model has resulted in the development of smart cities. The network traffic of smart cities using loT systems has developed rapidly and established novel cybersecurity problems later these loT devices are linked to sensors that are directly linked to huge cloud servers. Unfortunately, IoT systems and networks can be identified as extremely exposed to security attacks that aim at service accessibility and data integrity. Additionally, the heterogeneity of data gathered in distinct IoT devices, composed of the disturbances acquired in the IoT systems, renders the recognition of anomalous performance and threatened nodes very difficult related to typical Information Technology (IT) networks. Accordingly, there is a critical requirement for reliable and effectual anomaly detection (AD) for identifying malicious data to promise that it could not be utilized in IoT lead to decision support systems (DSS). This manuscript offers an Improved Radial Movement Optimization with Fuzzy Neural Network Enabled Anomaly Detection (IRMOFNN-AD) technique for IoT Assisted Smart Cities. The main purpose of the IRMOFNN-AD algorithm lies in the accurate and automated detection of the anomalies that exist in the IoT environment. For the feature selection process, the IRMOFNN-AD technique uses the IRMO system to elect an optimum set of features. Additionally, the IRMOFNN-AD algorithm applies the FNN model for the detection and classification of anomalies. Besides, the sine cosine algorithm (SCA) has been employed for the parameter tuning of the FNN algorithm. The simulation value of the IRMOFNN-AD system has been tested on benchmark IDS datasets. The extensive results illustrate the better detection outcomes of the IRMOFNN-AD system interms of different measures.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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