Kirubavathi Ganapathiyappan¹, Heba G. Mohamed², Abhishek Yadav¹, Guru Akshya Chinnaswamy¹, Ateeq Ur Rehman^3,*, Habib Hamam^4,5,6,7

CMC-Computers, Materials & Continua, Vol.86, No.2, pp. 1-18, 2026, DOI:10.32604/cmc.2025.069951 - 09 December 2025

Abstract The escalating complexity of modern malware continues to undermine the effectiveness of traditional signature-based detection techniques, which are often unable to adapt to rapidly evolving attack patterns. To address these challenges, this study proposes X-MalNet, a lightweight Convolutional Neural Network (CNN) framework designed for static malware classification through image-based representations of binary executables. By converting malware binaries into grayscale images, the model extracts distinctive structural and texture-level features that signify malicious intent, thereby eliminating the dependence on manual feature engineering or dynamic behavioral analysis. Built upon a modified AlexNet architecture, X-MalNet employs transfer learning to… More >

Boyan Cui^1,2, Huijuan Wang^1,*, Yongjun Qi^1,*, Hongce Chen¹, Quanbo Yuan^1,3, Dongran Liu¹, Xuehua Zhou¹

CMC-Computers, Materials & Continua, Vol.85, No.3, pp. 5531-5553, 2025, DOI:10.32604/cmc.2025.070101 - 23 October 2025

Abstract The proliferation of malware and the emergence of adversarial samples pose severe threats to global cybersecurity, demanding robust detection mechanisms. Traditional malware detection methods suffer from limited feature extraction capabilities, while existing Vision Transformer (ViT)-based approaches face high computational complexity due to global self-attention, hindering their efficiency in handling large-scale image data. To address these issues, this paper proposes a novel hybrid enhanced Vision Transformer architecture, HERL-ViT, tailored for malware detection. The detection framework involves five phases: malware image visualization, image segmentation with patch embedding, regional-local attention-based feature extraction, enhanced feature transformation, and classification. Methodologically,… More >

Madini O. Alassafi, Syed Hamid Hasan^*

CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.2, pp. 2627-2645, 2025, DOI:10.32604/cmes.2025.064926 - 31 August 2025

Abstract Over the past few years, Malware attacks have become more and more widespread, posing threats to digital assets throughout the world. Although numerous methods have been developed to detect malicious attacks, these malware detection techniques need to be more efficient in detecting new and progressively sophisticated variants of malware. Therefore, the development of more advanced and accurate techniques is necessary for malware detection. This paper introduces a comprehensive Dual-Channel Attention Deep Bidirectional Long Short-Term Memory (DCA-DBiLSTM) model for malware detection and risk mitigation. The Dual Channel Attention (DCA) mechanism improves the model’s capability to concentrate… More >

Muhammad Adil^1,*, Mona M. Jamjoom², Zahid Ullah³

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4363-4380, 2025, DOI:10.32604/cmc.2025.066551 - 30 July 2025

Abstract In today’s digital world, the Internet of Things (IoT) plays an important role in both local and global economies due to its widespread adoption in different applications. This technology has the potential to offer several advantages over conventional technologies in the near future. However, the potential growth of this technology also attracts attention from hackers, which introduces new challenges for the research community that range from hardware and software security to user privacy and authentication. Therefore, we focus on a particular security concern that is associated with malware detection. The literature presents many countermeasures, but… More >

Brij B. Gupta^1,2,3,4,*, Akshat Gaurav⁵, Wadee Alhalabi⁶, Varsha Arya^7,8, Shavi Bansal^9,10, Ching-Hsien Hsu¹

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 4755-4772, 2025, DOI:10.32604/cmc.2025.064053 - 30 July 2025

Abstract Detecting cyber attacks in networks connected to the Internet of Things (IoT) is of utmost importance because of the growing vulnerabilities in the smart environment. Conventional models, such as Naive Bayes and support vector machine (SVM), as well as ensemble methods, such as Gradient Boosting and eXtreme gradient boosting (XGBoost), are often plagued by high computational costs, which makes it challenging for them to perform real-time detection. In this regard, we suggested an attack detection approach that integrates Visual Geometry Group 16 (VGG16), Artificial Rabbits Optimizer (ARO), and Random Forest Model to increase detection accuracy… More >

Farah Mohammad^1,2,*, Saad Al-Ahmadi^1,3, Jalal Al-Muhtadi^1,3

CMC-Computers, Materials & Continua, Vol.84, No.3, pp. 5995-6014, 2025, DOI:10.32604/cmc.2025.063046 - 30 July 2025

Abstract Malware continues to pose a significant threat to cybersecurity, with new advanced infections that go beyond traditional detection. Limitations in existing systems include high false-positive rates, slow system response times, and inability to respond quickly to new malware forms. To overcome these challenges, this paper proposes OMD-RAS: Implementing Malware Detection in an Optimized Way through Real-Time and Adaptive Security as an extensive approach, hoping to get good results towards better malware threat detection and remediation. The significant steps in the model are data collection followed by comprehensive preprocessing consisting of feature engineering and normalization. Static… More >

Atif Raza Zaidi¹, Tahir Abbas^1,*, Ali Daud^2,*, Omar Alghushairy³, Hussain Dawood⁴, Nadeem Sarwar⁵

CMC-Computers, Materials & Continua, Vol.84, No.2, pp. 3281-3304, 2025, DOI:10.32604/cmc.2025.063646 - 03 July 2025

Abstract Safeguarding against malware requires precise machine-learning algorithms to classify harmful apps. The Drebin dataset of 15,036 samples and 215 features yielded significant and reliable results for two hybrid models, CNN + XGBoost and KNN + XGBoost. To address the class imbalance issue, SMOTE (Synthetic Minority Over-sampling Technique) was used to preprocess the dataset, creating synthetic samples of the minority class (malware) to balance the training set. XGBoost was then used to choose the most essential features for separating malware from benign programs. The models were trained and tested using 6-fold cross-validation, measuring accuracy, precision, recall,… More >

Taher Alzahrani^*

CMC-Computers, Materials & Continua, Vol.83, No.3, pp. 4575-4606, 2025, DOI:10.32604/cmc.2025.063448 - 19 May 2025

Abstract The rapid evolution of malware presents a critical cybersecurity challenge, rendering traditional signature-based detection methods ineffective against novel variants. This growing threat affects individuals, organizations, and governments, highlighting the urgent need for robust malware detection mechanisms. Conventional machine learning-based approaches rely on static and dynamic malware analysis and often struggle to detect previously unseen threats due to their dependency on predefined signatures. Although machine learning algorithms (MLAs) offer promising detection capabilities, their reliance on extensive feature engineering limits real-time applicability. Deep learning techniques mitigate this issue by automating feature extraction but may introduce computational overhead,… More >

Waleed Khalid Al-Ghanem¹, Emad Ul Haq Qazi^2,*, Tanveer Zia^2,3, Muhammad Hamza Faheem², Muhammad Imran⁴, Iftikhar Ahmad⁵

CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1009-1027, 2025, DOI:10.32604/cmes.2025.058352 - 11 April 2025

Abstract In the current digital era, new technologies are becoming an essential part of our lives. Consequently, the number of malicious software or malware attacks is rapidly growing. There is no doubt, the majority of malware attacks can be detected by most antivirus programs. However, such types of antivirus programs are one step behind malicious software. Due to these dilemmas, deep learning become popular in the detection and classification of malicious data. Therefore, researchers have significantly focused on finding solutions for malware attacks by analyzing malicious samples with the help of different techniques and models. In More >

Huiyao Dong¹, Igor Kotenko^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4263-4285, 2025, DOI:10.32604/cmc.2025.062439 - 06 March 2025

Abstract The growing complexity of cyber threats requires innovative machine learning techniques, and image-based malware classification opens up new possibilities. Meanwhile, existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection, and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges. This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis. The… More >