Open Access

ARTICLE

Intrusion Detection and Security Attacks Mitigation in Smart Cities with Integration of Human-Computer Interaction

Abeer Alnuaim^*

Department of Computer Science and Engineering, College of Applied Studies, King Saud University, Riyadh, 11451, Saudi Arabia

* Corresponding Author: Abeer Alnuaim. Email:

Computers, Materials & Continua 2026, 86(1), 1-33. https://doi.org/10.32604/cmc.2025.069110

Received 15 June 2025; Accepted 01 August 2025; Issue published 10 November 2025

Abstract

The rapid digitalization of urban infrastructure has made smart cities increasingly vulnerable to sophisticated cyber threats. In the evolving landscape of cybersecurity, the efficacy of Intrusion Detection Systems (IDS) is increasingly measured by technical performance, operational usability, and adaptability. This study introduces and rigorously evaluates a Human-Computer Interaction (HCI)-Integrated IDS with the utilization of Convolutional Neural Network (CNN), CNN-Long Short Term Memory (LSTM), and Random Forest (RF) against both a Baseline Machine Learning (ML) and a Traditional IDS model, through an extensive experimental framework encompassing many performance metrics, including detection latency, accuracy, alert prioritization, classification errors, system throughput, usability, ROC-AUC, precision-recall, confusion matrix analysis, and statistical accuracy measures. Our findings consistently demonstrate the superiority of the HCI-Integrated approach utilizing three major datasets (CICIDS 2017, KDD Cup 1999, and UNSW-NB15). Experimental results indicate that the HCI-Integrated model outperforms its counterparts, achieving an AUC-ROC of 0.99, a precision of 0.93, and a recall of 0.96, while maintaining the lowest false positive rate (0.03) and the fastest detection time (~1.5 s). These findings validate the efficacy of incorporating HCI to enhance anomaly detection capabilities, improve responsiveness, and reduce alert fatigue in critical smart city applications. It achieves markedly lower detection times, higher accuracy across all threat categories, reduced false positive and false negative rates, and enhanced system throughput under concurrent load conditions. The HCI-Integrated IDS excels in alert contextualization and prioritization, offering more actionable insights while minimizing analyst fatigue. Usability feedback underscores increased analyst confidence and operational clarity, reinforcing the importance of user-centered design. These results collectively position the HCI-Integrated IDS as a highly effective, scalable, and human-aligned solution for modern threat detection environments.

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Keywords

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