Intrusion Detection Using Chaotic Poor and Rich Optimization with Deep Learning Model for Smart City Environment

Fatma S. Alrayes, Mashael M. Asiri, Mashael Maashi, Ahmed S. Salama, Manar Ahmed Hamza (), Sara Saadeldeen Ibrahim, Abu Sarwar Zamani and Mohamed Ibrahim Alsaid
Additional contact information
Fatma S. Alrayes: Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia
Mashael M. Asiri: Department of Computer Science, College of Science & Art at Mahayil, King Khalid University, Abha 62529, Saudi Arabia
Mashael Maashi: Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 103786, Riyadh 11671, Saudi Arabia
Ahmed S. Salama: Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
Manar Ahmed Hamza: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia
Sara Saadeldeen Ibrahim: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia
Abu Sarwar Zamani: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia
Mohamed Ibrahim Alsaid: Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj 16278, Saudi Arabia

Sustainability, 2023, vol. 15, issue 8, 1-14

Abstract: Artificial intelligence (AI) techniques play a vital role in the evolving growth and rapid development of smart cities. To develop a smart environment, enhancements to the execution, sustainability, and security of traditional mechanisms become mandatory. Intrusion detection systems (IDS) can be considered an effective solutions to achieve security in the smart environment. This article introduces intrusion detection using chaotic poor and rich optimization with a deep learning model (IDCPRO-DLM) for ubiquitous and smart atmospheres. The IDCPRO-DLM model follows preprocessing, feature selection, and classification stages. At the initial stage, the Z-score data normalization system is exploited to scale the input data. Additionally, the IDCPRO-DLM method designs a chaotic poor and rich optimization algorithm-based feature selection (CPROA-FS) approach for selecting feature subsets. For intrusion detection, butterfly optimization algorithm (BOA) with a deep sparse autoencoder (DSAE) is used. The simulation analysis of the IDCPRO-DLM technique is studied on the benchmark CICIDS dataset and the comparison results show the better performance of the IDCPRO-DLM algorithm over recent state-of-the-art approaches with a maximum accuracy of 98.53%.

Keywords: smart cities; intrusion detection system; feature selection; deep learning; smart environment; security (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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