Improved smart city security using a deep maxout network-based intrusion detection system with walrus optimization

• Published in: PeerJ. Computer science Vol. 11; p. e2743
• Main Authors: Rajeh, Wahid, Aborokbah, Majed, S., Manimurugan, Albalawi, Umar, Aljuhani, Ahamed, Younes, Osama Shibl Abdalghany, Periyasami, Karthikeyan
• Format: Journal Article
• Language: English
• Published: United States PeerJ. Ltd 31.03.2025; PeerJ Inc
• Subjects: Analysis; Artificial Intelligence; Cybersecurity; Cyberterrorism; Detectors; IDS; Innovations; IoT; Marine mammals; Neural Networks; Public transportation; RFE; Safety and security measures; Smart city; Smart transportation; Transportation authorities; Saudi Arabia; RFE; Raspberry Pi; DMN; IDS; Cybersecurity; Smart transportation; Smart city; IoT
• ISSN: 2376-5992; 2376-5992
• DOI: 10.7717/peerj-cs.2743

Smart cities, enabled by the Internet of Things (IoT), leverage technology to optimize urban living and enhance infrastructure. As urban environments become interconnected hubs of digital innovation, securing critical components like public transportation infrastructure becomes increasingly important. This research addresses the need for robust intrusion detection systems (IDS) tailored to the unique challenges of securing public transportation within smart cities. Focused on the Tabuk region in Saudi Arabia, the study introduces an IDS model integrating the deep maxout network with walrus optimization (DMN-WO). The DMN is configured with an architecture that includes multiple layers with maxout activation functions. These layers are capable of capturing complex patterns in the data, making the DMN particularly effective for identifying anomalies in IoT network traffic. The DMN-WO model is ensured to be resource-efficient and suitable for real-time deployment on constrained devices like Raspberry Pi, typical in IoT systems. Training and validation are conducted using the CIC-IDS-2018 dataset, CIC-IDS -2029 dataset and real-time data from Raspberry Pi devices deployed in the smart city's public transportation network. Real-time data application maintains robust performance, with 98.06% accuracy, 98.50% detection rate, 98.81% precision, 98.24% specificity, and a 98.57% F1-score. This research advances cybersecurity measures in smart city applications by providing a resilient solution for detecting and mitigating security threats in public transportation infrastructure. It lays the groundwork for further refinements and real-world deployments in the dynamic landscape of smart cities.