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 >

Adeel Munawar¹, Muhammad Nadeem Ali², Awais Qasim³, Byung-Seo Kim^2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 1189-1211, 2025, DOI:10.32604/cmes.2025.071876 - 30 October 2025

Abstract Cyber-physical systems (CPS) represent a sophisticated integration of computational and physical components that power critical applications such as smart manufacturing, healthcare, and autonomous infrastructure. However, their extensive reliance on internet connectivity makes them increasingly susceptible to cyber threats, potentially leading to operational failures and data breaches. Furthermore, CPS faces significant threats related to unauthorized access, improper management, and tampering of the content it generates. In this paper, we propose an intrusion detection system (IDS) optimized for CPS environments using a hybrid approach by combining a nature-inspired feature selection scheme, such as Grey Wolf Optimization (GWO),… More >

Lobna Hajji-Hedfi^1,2,*, Omaima Bargougui^1,3, Abdelhak Rhouma¹, Takwa Wannassi¹, Amira Khlif^1,3, Samar Dali^1,3, Wafa Gamaoun⁴

Phyton-International Journal of Experimental Botany, Vol.94, No.8, pp. 2255-2284, 2025, DOI:10.32604/phyton.2025.066951 - 29 August 2025

Abstract Fungal plant diseases are infections caused by pathogenic fungi that affect crops, ornamental plants, and trees. Symptoms of these diseases can include leaf spots, fruit rot, root rot, and generalized growth retardation. Fungal diseases can result in decreased quality and quantity of crops, which can have a negative economic impact on farmers and producers. Moreover, these diseases can cause environmental damage. Indeed, fungal diseases can directly affect crops by reducing plant growth and yield, as well as altering their quality and nutritional value. Although effective, the use of many chemical products is often harmful to… More >

Jihoon Moon¹, Jiyoung Woo^2,*

CMC-Computers, Materials & Continua, Vol.85, No.1, pp. 1383-1425, 2025, DOI:10.32604/cmc.2025.067784 - 29 August 2025

Abstract Improving early diagnosis of autism spectrum disorder (ASD) in children increasingly relies on predictive models that are reliable and accessible to non-experts. This study aims to develop such models using Python-based tools to improve ASD diagnosis in clinical settings. We performed exploratory data analysis to ensure data quality and identify key patterns in pediatric ASD data. We selected the categorical boosting (CatBoost) algorithm to effectively handle the large number of categorical variables. We used the PyCaret automated machine learning (AutoML) tool to make the models user-friendly for clinicians without extensive machine learning expertise. In addition,… More >

Lekaa K. Abdul Karem¹, Badriah Saad Al-Farhan², Ghada M. G. Eldin³, Samir Kamel⁴, Ahmed M. Khalil^5,*

Journal of Renewable Materials, Vol.13, No.7, pp. 1459-1473, 2025, DOI:10.32604/jrm.2025.02025-0046 - 22 July 2025

Abstract In this study, the casting process is used to fabricate modified polyvinyl alcohol (PVA), starch (S), and carboxymethyl cellulose (CMC) polymer blend films (PVA/S/CMC) loaded with various concentrations of iron-doped carbon quantum dots (Fe-CQDs) and denoted as (PVA/S/CMC@Fe-CQDs). A one-step microwave strategy was employed as a facile method to prepare Fe-CQDs. Through a series of characterization techniques, fourier-transform infrared (FTIR) spectroscopy, x-ray diffraction (XRD) analysis, and scanning electron microscopy (SEM) have been used to show the successful integration of Fe-CQDs into the PVA/S/CMC matrix. Loading the synthesized Fe-CQDs to the polymeric matrix significantly enhanced the… More > Graphic Abstract

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 >

Pooja Bidwai^1,2, Shilpa Gite^1,3, Biswajeet Pradhan^4,*, Abdullah Almari⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 2645-2676, 2025, DOI:10.32604/cmc.2025.061018 - 03 July 2025

Abstract Diabetic Retinopathy (DR) is a critical disorder that affects the retina due to the constant rise in diabetics and remains the major cause of blindness across the world. Early detection and timely treatment are essential to mitigate the effects of DR, such as retinal damage and vision impairment. Several conventional approaches have been proposed to detect DR early and accurately, but they are limited by data imbalance, interpretability, overfitting, convergence time, and other issues. To address these drawbacks and improve DR detection accurately, a distributed Explainable Convolutional Neural network-enabled Light Gradient Boosting Machine (DE-ExLNN) is… More >

Alireza Bahmani^*

Journal on Internet of Things, Vol.7, pp. 1-17, 2025, DOI:10.32604/jiot.2025.060786 - 24 June 2025

Abstract With the development of the Internet of Things (IoT) technology and its widespread integration in various aspects of life, the risks associated with cyberattacks on these systems have increased significantly. Vulnerabilities in IoT devices, stemming from insecure designs and software weaknesses, have made attacks on them more complex and dangerous compared to traditional networks. Conventional intrusion detection systems are not fully capable of identifying and managing these risks in the IoT environment, making research and evaluation of suitable intrusion detection systems for IoT crucial. In this study, deep learning, multi-layer perceptron (MLP), Random Forest (RF),… More >

Shuqin Zhang¹, Zihao Wang^1,*, Xinyu Su²

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 5781-5809, 2025, DOI:10.32604/cmc.2025.062080 - 19 May 2025

Abstract The methods of network attacks have become increasingly sophisticated, rendering traditional cybersecurity defense mechanisms insufficient to address novel and complex threats effectively. In recent years, artificial intelligence has achieved significant progress in the field of network security. However, many challenges and issues remain, particularly regarding the interpretability of deep learning and ensemble learning algorithms. To address the challenge of enhancing the interpretability of network attack prediction models, this paper proposes a method that combines Light Gradient Boosting Machine (LGBM) and SHapley Additive exPlanations (SHAP). LGBM is employed to model anomalous fluctuations in various network indicators,… 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 >