Wanwei Huang^1,*, Huicong Yu¹, Jiawei Ren², Kun Wang³, Yanbu Guo¹, Lifeng Jin⁴

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-24, 2026, DOI:10.32604/cmc.2025.068493 - 10 November 2025

Abstract Existing feature selection methods for intrusion detection systems in the Industrial Internet of Things often suffer from local optimality and high computational complexity. These challenges hinder traditional IDS from effectively extracting features while maintaining detection accuracy. This paper proposes an industrial Internet of Things intrusion detection feature selection algorithm based on an improved whale optimization algorithm (GSLDWOA). The aim is to address the problems that feature selection algorithms under high-dimensional data are prone to, such as local optimality, long detection time, and reduced accuracy. First, the initial population’s diversity is increased using the Gaussian Mutation More >

Mona Almofarreh¹, Amnah Alshahrani², Nouf Helal Alharbi³, Ahmed Omer Ahmed⁴, Hussain Alshahrani⁵, Abdulrahman Alzahrani^6,*, Mohammed Mujib Alshahrani⁷, Asma A. Alhashmi⁸

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 2631-2656, 2025, DOI:10.32604/cmes.2025.070545 - 26 November 2025

Abstract Zero-day attacks use unknown vulnerabilities that prevent being identified by cybersecurity detection tools. This study indicates that zero-day attacks have a significant impact on computer security. A conventional signature-based detection algorithm is not efficient at recognizing zero-day attacks, as the signatures of zero-day attacks are usually not previously accessible. A machine learning (ML)-based detection algorithm is proficient in capturing statistical features of attacks and, therefore, optimistic for zero-day attack detection. ML and deep learning (DL) are employed for designing intrusion detection systems. The improvement of absolute varieties of novel cyberattacks poses significant challenges for IDS… More >

Siraj Un Muneer¹, Ihsan Ullah¹, Zeshan Iqbal^2,*, Rajermani Thinakaran³

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 4959-4975, 2025, DOI:10.32604/cmc.2025.068566 - 23 October 2025

Abstract The complexity of cloud environments challenges secure resource management, especially for intrusion detection systems (IDS). Existing strategies struggle to balance efficiency, cost fairness, and threat resilience. This paper proposes an innovative approach to managing cloud resources through the integration of a genetic algorithm (GA) with a “double auction” method. This approach seeks to enhance security and efficiency by aligning buyers and sellers within an intelligent market framework. It guarantees equitable pricing while utilizing resources efficiently and optimizing advantages for all stakeholders. The GA functions as an intelligent search mechanism that identifies optimal combinations of bids More >

Muhammad Usama¹, Arshad Aziz², Imtiaz Hassan², Shynar Akhmetzhanova³, Sultan Noman Qasem^4,*, Abdullah M. Albarrak⁴, Tawfik Al-Hadhrami⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 1225-1248, 2025, DOI:10.32604/cmes.2025.067098 - 31 July 2025

Abstract The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges, particularly in safeguarding sensitive patient data and maintaining the integrity of medical services. As healthcare becomes more data-driven, cyberattacks targeting these systems continue to rise, necessitating the development of robust, domain-adapted Intrusion Detection Systems (IDS). However, current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare. To address this gap, this study introduces HCKDDCUP, a synthetic dataset modeled on the widely used KDDCUP benchmark, augmented with healthcare-relevant attributes such as patient data, treatments, and… More >

Khadija Bouzaachane^1,*, El Mahdi El Guarmah², Abdullah M. Alnajim³, Sheroz Khan⁴

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2391-2410, 2025, DOI:10.32604/cmc.2025.065031 - 03 July 2025

Abstract The rapid increase in the number of Internet of Things (IoT) devices, coupled with a rise in sophisticated cyberattacks, demands robust intrusion detection systems. This study presents a holistic, intelligent intrusion detection system. It uses a combined method that integrates machine learning (ML) and deep learning (DL) techniques to improve the protection of contemporary information technology (IT) systems. Unlike traditional signature-based or single-model methods, this system integrates the strengths of ensemble learning for binary classification and deep learning for multi-class classification. This combination provides a more nuanced and adaptable defense. The research utilizes the NF-UQ-NIDS-v2… More >

Tajinder Kumar¹, Purushottam Sharma^2,*, Xiaochun Cheng^3,*, Sachin Lalar⁴, Shubham Kumar⁵, Sandhya Bansal⁶

CMC-Computers, Materials & Continua, Vol.83, No.1, pp. 719-738, 2025, DOI:10.32604/cmc.2025.060836 - 26 March 2025

Abstract With cloud computing, large chunks of data can be handled at a small cost. However, there are some reservations regarding the security and privacy of cloud data stored. For solving these issues and enhancing cloud computing security, this research provides a Three-Layered Security Access model (TLSA) aligned to an intrusion detection mechanism, access control mechanism, and data encryption system. The TLSA underlines the need for the protection of sensitive data. This proposed approach starts with Layer 1 data encryption using the Advanced Encryption Standard (AES). For data transfer and storage, this encryption guarantees the data’s… More >

Monirah Al-Ajlan^*, Mourad Ykhlef

CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2053-2076, 2024, DOI:10.32604/cmc.2024.055891 - 18 November 2024

Abstract The ever-growing network traffic threat landscape necessitates adopting accurate and robust intrusion detection systems (IDSs). IDSs have become a research hotspot and have seen remarkable performance improvements. Generative adversarial networks (GANs) have also garnered increasing research interest recently due to their remarkable ability to generate data. This paper investigates the application of (GANs) in (IDS) and explores their current use within this research field. We delve into the adoption of GANs within signature-based, anomaly-based, and hybrid IDSs, focusing on their objectives, methodologies, and advantages. Overall, GANs have been widely employed, mainly focused on solving the More >

Ye-Seul Kil^1,#, Yu-Ran Jeon^1,#, Sun-Jin Lee¹, Il-Gu Lee^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.2, pp. 1473-1493, 2024, DOI:10.32604/cmes.2024.052637 - 27 September 2024

Abstract With the rise of remote work and the digital industry, advanced cyberattacks have become more diverse and complex in terms of attack types and characteristics, rendering them difficult to detect with conventional intrusion detection methods. Signature-based intrusion detection methods can be used to detect attacks; however, they cannot detect new malware. Endpoint detection and response (EDR) tools are attracting attention as a means of detecting attacks on endpoints in real-time to overcome the limitations of signature-based intrusion detection techniques. However, EDR tools are restricted by the continuous generation of unnecessary logs, resulting in poor detection… More >

Chunhui Li^1,*, Hua Jiang²

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4491-4512, 2024, DOI:10.32604/cmc.2024.048431 - 20 June 2024

Abstract In the context of enterprise systems, intrusion detection (ID) emerges as a critical element driving the digital transformation of enterprises. With systems spanning various sectors of enterprises geographically dispersed, the necessity for seamless information exchange has surged significantly. The existing cross-domain solutions are challenged by such issues as insufficient security, high communication overhead, and a lack of effective update mechanisms, rendering them less feasible for prolonged application on resource-limited devices. This study proposes a new cross-domain collaboration scheme based on federated chains to streamline the server-side workload. Within this framework, individual nodes solely engage in… More >

Seyoung Lee¹, Wonsuk Choi¹, Insup Kim², Ganggyu Lee², Dong Hoon Lee^1,*

CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3413-3442, 2023, DOI:10.32604/cmc.2023.039583 - 08 October 2023

Abstract Recently, automotive intrusion detection systems (IDSs) have emerged as promising defense approaches to counter attacks on in-vehicle networks (IVNs). However, the effectiveness of IDSs relies heavily on the quality of the datasets used for training and evaluation. Despite the availability of several datasets for automotive IDSs, there has been a lack of comprehensive analysis focusing on assessing these datasets. This paper aims to address the need for dataset assessment in the context of automotive IDSs. It proposes qualitative and quantitative metrics that are independent of specific automotive IDSs, to evaluate the quality of datasets. These… More >