Saeed Ullah¹, Junsheng Wu^1,*, Mian Muhammad Kamal², Heba G. Mohamed³, Muhammad Sheraz⁴, Teong Chee Chuah^4,*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2647-2681, 2025, DOI:10.32604/cmes.2025.068849 - 31 August 2025

Abstract The Internet of Things (IoT) ecosystem faces growing security challenges because it is projected to have 76.88 billion devices by 2025 and $1.4 trillion market value by 2027, operating in distributed networks with resource limitations and diverse system architectures. The current conventional intrusion detection systems (IDS) face scalability problems and trust-related issues, but blockchain-based solutions face limitations because of their low transaction throughput (Bitcoin: 7 TPS (Transactions Per Second), Ethereum: 15–30 TPS) and high latency. The research introduces MBID (Multi-Tier Blockchain Intrusion Detection) as a groundbreaking Multi-Tier Blockchain Intrusion Detection System with AI-Enhanced Detection, which… More >

Saad Awadh Alanazi¹, Fahad Ahmad^2,3,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 769-800, 2025, DOI:10.32604/cmc.2025.066753 - 29 August 2025

Abstract This study presents a comprehensive and secure architectural framework for the Internet of Medical Things (IoMT), integrating the foundational principles of the Confidentiality, Integrity, and Availability (CIA) triad along with authentication mechanisms. Leveraging advanced Machine Learning (ML) and Deep Learning (DL) techniques, the proposed system is designed to safeguard Patient-Generated Health Data (PGHD) across interconnected medical devices. Given the increasing complexity and scale of cyber threats in IoMT environments, the integration of Intrusion Detection and Prevention Systems (IDPS) with intelligent analytics is critical. Our methodology employs both standalone and hybrid ML & DL models to… More >

Kuncham Sreenivasa Rao¹, Rajitha Kotoju², B. Ramana Reddy³, Taher Al-Shehari⁴, Nasser A. Alsadhan⁵, Subhav Singh^6,7,8, Shitharth Selvarajan^9,10,11,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1899-1916, 2025, DOI:10.32604/cmc.2025.064728 - 29 August 2025

Abstract Protecting Supervisory Control and Data Acquisition-Industrial Internet of Things (SCADA-IIoT) systems against intruders has become essential since industrial control systems now oversee critical infrastructure, and cyber attackers more frequently target these systems. Due to their connection of physical assets with digital networks, SCADA-IIoT systems face substantial risks from multiple attack types, including Distributed Denial of Service (DDoS), spoofing, and more advanced intrusion methods. Previous research in this field faces challenges due to insufficient solutions, as current intrusion detection systems lack the necessary accuracy, scalability, and adaptability needed for IIoT environments. This paper introduces CyberFortis, a… 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 >

Fatma S. Alrayes¹, Syed Umar Amin^2,*, Nada Ali Hakami², Mohammed K. Alzaylaee³, Tariq Kashmeery⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 581-614, 2025, DOI:10.32604/cmes.2025.065188 - 31 July 2025

Abstract The growing incidence of cyberattacks necessitates a robust and effective Intrusion Detection Systems (IDS) for enhanced network security. While conventional IDSs can be unsuitable for detecting different and emerging attacks, there is a demand for better techniques to improve detection reliability. This study introduces a new method, the Deep Adaptive Multi-Layer Attention Network (DAMLAN), to boost the result of intrusion detection on network data. Due to its multi-scale attention mechanisms and graph features, DAMLAN aims to address both known and unknown intrusions. The real-world NSL-KDD dataset, a popular choice among IDS researchers, is used to… More >

Tony Thomas^*, Ravi Prakash, Soumya Pal

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4055-4104, 2025, DOI:10.32604/cmc.2025.064903 - 30 July 2025

Abstract The Internet of Medical Things (IoMT) is transforming healthcare by enabling real-time data collection, analysis, and personalized treatment through interconnected devices such as sensors and wearables. The integration of Digital Twins (DTs), the virtual replicas of physical components and processes, has also been found to be a game changer for the ever-evolving IoMT. However, these advancements in the healthcare domain come with significant cybersecurity challenges, exposing it to malicious attacks and several security threats. Intrusion Detection Systems (IDSs) serve as a critical defense mechanism, yet traditional IDS approaches often struggle with the complexity and scale… More >

Yuan Feng¹, Yajie Si², Jianwei Zhang^3,4,*, Zengyu Cai^5,*, Hongying Zhao⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4431-4449, 2025, DOI:10.32604/cmc.2025.064696 - 30 July 2025

Abstract With the rapid development of the industrial Internet, the network security environment has become increasingly complex and variable. Intrusion detection, a core technology for ensuring the security of industrial control systems, faces the challenge of unbalanced data samples, particularly the low detection rates for minority class attack samples. Therefore, this paper proposes a data enhancement method for intrusion detection in the industrial Internet based on a Self-Attention Wasserstein Generative Adversarial Network (SA-WGAN) to address the low detection rates of minority class attack samples in unbalanced intrusion detection scenarios. The proposed method integrates a self-attention mechanism… More >

Atena Shiranzaei^1,*, Emad Alizadeh², Mahdi Rabbani³, Sajjad Bagheri Baba Ahmadi^4,*, Mohsen Tajgardan⁵

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5381-5402, 2025, DOI:10.32604/cmc.2025.064414 - 30 July 2025

Abstract The sinkhole attack is one of the most damaging threats in the Internet of Things (IoT). It deceptively attracts neighboring nodes and initiates malicious activity, often disrupting the network when combined with other attacks. This study proposes a novel approach, named NADSA, to detect and isolate sinkhole attacks. NADSA is based on the RPL protocol and consists of two detection phases. In the first phase, the minimum possible hop count between the sender and receiver is calculated and compared with the sender’s reported hop count. The second phase utilizes the number of DIO messages to More >

Xiaogang Yuan^*, Huan Pei, Yanlin Wu

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5555-5575, 2025, DOI:10.32604/cmc.2025.063537 - 30 July 2025

Abstract In the complex environment of Wireless Sensor Networks (WSNs), various malicious attacks have emerged, among which internal attacks pose particularly severe security risks. These attacks seriously threaten network stability, data transmission reliability, and overall performance. To effectively address this issue and significantly improve intrusion detection speed, accuracy, and resistance to malicious attacks, this research designs a Three-level Intrusion Detection Model based on Dynamic Trust Evaluation (TIDM-DTE). This study conducts a detailed analysis of how different attack types impact node trust and establishes node models for data trust, communication trust, and energy consumption trust by focusing… More >

Manzheng Yuan^1,2, Kai Yang^2,*

Journal of Cyber Security, Vol.7, pp. 197-219, 2025, DOI:10.32604/jcs.2025.066089 - 07 July 2025

Abstract Currently, network intrusion detection systems (NIDS) face significant challenges in feature redundancy and high computational complexity, which hinder the improvement of detection performance and significantly reduce operational efficiency. To address these issues, this paper proposes an innovative weighted feature selection method combining mutual information and Extreme Gradient Boosting (XGBoost). This method aims to leverage their strengths to identify crucial feature subsets for intrusion detection accurately. Specifically, it first calculates the mutual information scores between features and target variables to evaluate individual discriminatory capabilities of features and uses XGBoost to obtain feature importance scores reflecting their… More >