Editorial: STAP Journal of Security Risk Management
By Mohammed AminAlmaiah
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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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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.
Cyber Security Risk Management for Threats in Wireless LAN: A Literature Review
By Michael Saad Alghareeb, MohammedAlmaayah
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Wireless LANs have been widely deployed in places such as business organizations, government agencies, hospitals, schools, and even the home environment. Mobility, flexibility, scalability, cost-effectiveness, and rapid deployment are some of the factors driving the spread of this technology. However, due to their nature wireless LANs are vulnerable to several types of attacks. Therefore, this study aims to discuss common threats related to the wireless LAN system, and a comprehensive review of existing studies regarding cybersecurity threats in Wireless LAN. A systematic literature review (SLR) was conducted to identify potential threats and identify appropriate countermeasures for each wireless WLA.
Responsive Machine Learning Framework and Lightweight Utensil of Prevention of Evasion Attacks in the IoT-Based IDS
By Dena Abu Laila
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The proliferation of Internet of Things (IoT) devices in smart homes and industrial environments has created unprecedented security challenges, particularly regarding intrusion detection systems (IDS) susceptible to adversarial machine learning attacks. This paper presents a novel adversarial-aware defensive framework specifically designed for resource-constrained IoT environments, addressing the critical vulnerability of machine learning-based IDS to evasion attacks. Our lightweight protection mechanism integrates adversarial training techniques with computational efficiency optimizations, enabling real-time threat detection while maintaining robustness against sophisticated evasion attempts. The proposed framework employs a multi-layered defense strategy combining feature space transformations, ensemble-based detection, and adaptive threshold mechanisms to counter adversarial perturbations. Experimental evaluation on diverse IoT datasets demonstrates that our approach achieves 94.7% detection accuracy against clean traffic and maintains 89.3% effectiveness against state-of-the-art evasion attacks, while requiring only 15% additional computational overhead compared to traditional IDS. The framework’s adaptability to various IoT deployment scenarios and its ability to operate within stringent resource constraints make it particularly suitable for real-world implementation in smart infrastructure systems.