STAP Journal of Security Risk Management

ISSN: 3080-9444 (Online)

Editorial: STAP Journal of Security Risk Management

By Mohammed Amin Almaiah

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Abstract

Dear Readers, It is with great pleasure that we introduce to you our upcoming journal, " STAP Journal of Security Risk Management." This journal is dedicated to exploring the advancements in the field of cybersecurity and providing a platform for researchers and scholars to exchange ideas, fostering progress in the area of security and risk management. On behalf of the editorial team, I extend our heartfelt gratitude and a warm welcome to the scholars, experts, researchers, and readers who support and follow our journal. Purpose of the Journal The STAP Journal of Security Risk Management aims to promote the development of cybersecurity fields, enhance the research level of cybersecurity technologies, and strengthen academic exchanges on an international scale. We are committed to building an open, inclusive, and innovative platform for researchers in the field of cybersecurity to present their findings, share experiences, and exchange ideas.

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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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.