STAP Journal of Security Risk Management

ISSN: 3080-9444 (Online)

Enhancing Intrusion Detection Systems by Using Machine Learning in Smart Cities: Issues, Challenges and Future Research Direction

by 

Rasha Almarshood ;

M. M. Hafizur Rahman

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Published: 2025/05/25

Abstract

With promising innovation and efficiency in smart city, it is still facing a growing threat of cyberattacks. The increasing interconnectedness of digital services makes these cities particularly vulnerable. Traditional security measures struggle to adapt to evolving threats. Due to the insufficient analysis of real-time attack patterns. Emerging new technologies are crucial for managing these issues. Machine Learning (ML) is a promising solution to enhance Intrusion Detection Systems (IDS). ML can effectively detect malicious activities. ML provides automation of network traffic analysis and anomalous pattern identification. This paper presents a systematic literature review to explore the potential of ML in improving IDS for smart city. Various ML approaches and specific applications in smart city services will be investigated. We will evaluate the effectiveness of existing approaches in smart city. Identifying key challenges and future research directions. We also aim to contribute to the development of smart city security systems. It will benefit critical infrastructures to be more robust and resilient against evolving threats.

Keywords

IDSMLSmart CityAnomaly DetectionReal-time Analysis

Enhancing Intrusion Detection Systems by Using Machine Learning in Smart Cities: Issues, Challenges and Future Research Direction is licensed under CC BY 4.0CCBY

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