Isam Bahaa Aldallal¹, Abdullahi Abdu Ibrahim^1,*, Saadaldeen Rashid Ahmed^2,3

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.075212 - 10 February 2026

Abstract The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems (IDS) capable of addressing dynamic security threats under constrained resource environments. This paper proposes a hybrid IDS for IoT networks, integrating Support Vector Machine (SVM) and Genetic Algorithm (GA) for feature selection and parameter optimization. The GA reduces the feature set from 41 to 7, achieving a 30% reduction in overhead while maintaining an attack detection rate of 98.79%. Evaluated on the NSL-KDD dataset, the system demonstrates an accuracy of 97.36%, a recall of 98.42%, and an F1-score of 96.67%, with a low false More >

Kirubavathi Ganapathiyappan¹, Chahana Ravikumar¹, Raghul Alagunachimuthu Ranganayaki¹, Ayman Altameem², Ateeq Ur Rehman^3,*, Ahmad Almogren^4,*

CMC-Computers, Materials & Continua, Vol.87, No.1, 2026, DOI:10.32604/cmc.2025.072840 - 10 February 2026

Abstract Android smartphones have become an integral part of our daily lives, becoming targets for ransomware attacks. Such attacks encrypt user information and ask for payment to recover it. Conventional detection mechanisms, such as signature-based and heuristic techniques, often fail to detect new and polymorphic ransomware samples. To address this challenge, we employed various ensemble classifiers, such as Random Forest, Gradient Boosting, Bagging, and AutoML models. We aimed to showcase how AutoML can automate processes such as model selection, feature engineering, and hyperparameter optimization, to minimize manual effort while ensuring or enhancing performance compared to traditional… More >

Misbah Anwer^1,*, Ghufran Ahmed¹, Maha Abdelhaq², Raed Alsaqour³, Shahid Hussain⁴, Adnan Akhunzada^5,*

CMC-Computers, Materials & Continua, Vol.86, No.1, pp. 1-15, 2026, DOI:10.32604/cmc.2025.068673 - 10 November 2025

Abstract The exponential growth of the Internet of Things (IoT) has introduced significant security challenges, with zero-day attacks emerging as one of the most critical and challenging threats. Traditional Machine Learning (ML) and Deep Learning (DL) techniques have demonstrated promising early detection capabilities. However, their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints, high computational costs, and the costly time-intensive process of data labeling. To address these challenges, this study proposes a Federated Learning (FL) framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in… 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 >

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 >

Hanqing Sun¹, Xue Li^2,*, Qiyuan Fan³, Puming Wang³

CMC-Computers, Materials & Continua, Vol.84, No.1, pp. 1659-1679, 2025, DOI:10.32604/cmc.2025.061426 - 09 June 2025

Abstract The era of big data brings new challenges for information network systems (INS), simultaneously offering unprecedented opportunities for advancing intelligent intrusion detection systems. In this work, we propose a data-driven intrusion detection system for Distributed Denial of Service (DDoS) attack detection. The system focuses on intrusion detection from a big data perceptive. As intelligent information processing methods, big data and artificial intelligence have been widely used in information systems. The INS system is an important information system in cyberspace. In advanced INS systems, the network architectures have become more complex. And the smart devices in… More >

Haydar Abdulameer Marhoon^1,2,*, Rafid Sagban^3,4, Atheer Y. Oudah^1,5, Saadaldeen Rashid Ahmed^6,7

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4181-4218, 2025, DOI:10.32604/cmc.2025.058822 - 06 March 2025

Abstract In order to address the critical security challenges inherent to Wireless Sensor Networks (WSNs), this paper presents a groundbreaking barrier-based machine learning technique. Vital applications like military operations, healthcare monitoring, and environmental surveillance increasingly deploy WSNs, recognizing the critical importance of effective intrusion detection in protecting sensitive data and maintaining operational integrity. The proposed method innovatively partitions the network into logical segments or virtual barriers, allowing for targeted monitoring and data collection that aligns with specific traffic patterns. This approach not only improves the diversit. There are more types of data in the training set,… More >

Ammar Odeh^*, Anas Abu Taleb

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4149-4169, 2024, DOI:10.32604/cmc.2024.058052 - 19 December 2024

Abstract The proliferation of Internet of Things (IoT) technology has exponentially increased the number of devices interconnected over networks, thereby escalating the potential vectors for cybersecurity threats. In response, this study rigorously applies and evaluates deep learning models—namely Convolutional Neural Networks (CNN), Autoencoders, and Long Short-Term Memory (LSTM) networks—to engineer an advanced Intrusion Detection System (IDS) specifically designed for IoT environments. Utilizing the comprehensive UNSW-NB15 dataset, which encompasses 49 distinct features representing varied network traffic characteristics, our methodology focused on meticulous data preprocessing including cleaning, normalization, and strategic feature selection to enhance model performance. A robust… More >

Fatma S. Alrayes¹, Mohammed Zakariah², Syed Umar Amin^3,*, Zafar Iqbal Khan³, Jehad Saad Alqurni⁴

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4319-4347, 2024, DOI:10.32604/cmc.2024.050586 - 20 June 2024

Abstract Intrusion detection systems (IDS) are essential in the field of cybersecurity because they protect networks from a wide range of online threats. The goal of this research is to meet the urgent need for small-footprint, highly-adaptable Network Intrusion Detection Systems (NIDS) that can identify anomalies. The NSL-KDD dataset is used in the study; it is a sizable collection comprising 43 variables with the label’s “attack” and “level.” It proposes a novel approach to intrusion detection based on the combination of channel attention and convolutional neural networks (CNN). Furthermore, this dataset makes it easier to conduct… 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 >