JoVE - Journal of Visualized Experiments
JoVE Visualize
Contact Us

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

  • 0Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia.
Peerj. Computer Science +

|

June 26, 2025

|

June 26, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

This study introduces a resource-efficient intrusion detection system (IDS) for smart city public transport. The Deep Maxout Network with Walrus Optimization (DMN-WO) model achieves high accuracy in detecting cyber threats.

Area Of Science

  • Cybersecurity
  • Internet of Things (IoT)
  • Smart City Infrastructure

Background

  • Smart cities utilize IoT for urban optimization, increasing the need for secure public transportation.
  • Securing interconnected digital infrastructure in urban environments is critical.

Purpose Of The Study

  • To develop a robust intrusion detection system (IDS) for public transportation in smart cities.
  • To address the unique security challenges of IoT-enabled urban transit systems.

Main Methods

  • An IDS model integrating a Deep Maxout Network (DMN) with Walrus Optimization (WO) was developed.
  • The DMN-WO model features maxout activation functions for complex pattern recognition in IoT traffic.
  • The model is designed for resource efficiency, suitable for real-time deployment on devices like Raspberry Pi.

Main Results

  • The DMN-WO model was trained and validated using CIC-IDS-2018, CIC-IDS-2029 datasets, and real-time data.
  • Achieved high performance metrics: 98.06% accuracy, 98.50% detection rate, 98.81% precision, 98.24% specificity, and 98.57% F1-score.
  • Demonstrated effectiveness in real-time threat detection within a smart city's public transport network.

Conclusions

  • The research provides a resilient cybersecurity solution for smart city public transportation.
  • The DMN-WO model advances threat detection and mitigation in IoT-based urban infrastructure.
  • This work establishes a foundation for future real-world deployments in smart city environments.

Keywords: