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 >

Sang-Hoon Choi¹, Ki-Woong Park^2,*

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4021-4039, 2025, DOI:10.32604/cmc.2025.061267 - 06 March 2025

Abstract The proliferation of Internet of Things (IoT) devices has established edge computing as a critical paradigm for real-time data analysis and low-latency processing. Nevertheless, the distributed nature of edge computing presents substantial security challenges, rendering it a prominent target for sophisticated malware attacks. Existing signature-based and behavior-based detection methods are ineffective against the swiftly evolving nature of malware threats and are constrained by the availability of resources. This paper suggests the Genetic Encoding for Novel Optimization of Malware Evaluation (GENOME) framework, a novel solution that is intended to improve the performance of malware detection and… More >

Amel Ksibi^1,*, Mohammed Zakariah², Latifah Almuqren¹, Ala Saleh Alluhaidan¹

CMC-Computers, Materials & Continua, Vol.82, No.3, pp. 4093-4116, 2025, DOI:10.32604/cmc.2025.059615 - 06 March 2025

Abstract The analysis of Android malware shows that this threat is constantly increasing and is a real threat to mobile devices since traditional approaches, such as signature-based detection, are no longer effective due to the continuously advancing level of sophistication. To resolve this problem, efficient and flexible malware detection tools are needed. This work examines the possibility of employing deep CNNs to detect Android malware by transforming network traffic into image data representations. Moreover, the dataset used in this study is the CIC-AndMal2017, which contains 20,000 instances of network traffic across five distinct malware categories: a.… More >

Eliel Martins¹, Javier Bermejo Higuera^2,*, Ricardo Sant’Ana¹, Juan Ramón Bermejo Higuera², Juan Antonio Sicilia Montalvo², Diego Piedrahita Castillo³

CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3031-3067, 2025, DOI:10.32604/cmes.2025.061080 - 03 March 2025

Abstract The growing threat of malware, particularly in the Portable Executable (PE) format, demands more effective methods for detection and classification. Machine learning-based approaches exhibit their potential but often neglect semantic segmentation of malware files that can improve classification performance. This research applies deep learning to malware detection, using Convolutional Neural Network (CNN) architectures adapted to work with semantically extracted data to classify malware into malware families. Starting from the Malconv model, this study introduces modifications to adapt it to multi-classification tasks and improve its performance. It proposes a new innovative method that focuses on byte More >

Ki-Pyoung Ma¹, Dong-Ju Ryu², Sang-Joon Lee^3,*

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4485-4502, 2024, DOI:10.32604/cmc.2024.059116 - 19 December 2024

Abstract With the advancements in artificial intelligence (AI) technology, attackers are increasingly using sophisticated techniques, including ChatGPT. Endpoint Detection & Response (EDR) is a system that detects and responds to strange activities or security threats occurring on computers or endpoint devices within an organization. Unlike traditional antivirus software, EDR is more about responding to a threat after it has already occurred than blocking it. This study aims to overcome challenges in security control, such as increased log size, emerging security threats, and technical demands faced by control staff. Previous studies have focused on AI detection models,… More >

Manh Vu Minh^*, Cho Do Xuan

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4371-4396, 2024, DOI:10.32604/cmc.2024.058168 - 19 December 2024

Abstract Detecting malware on mobile devices using the Android operating system has become a critical challenge in the field of cybersecurity, in the context of the rapid increase in the number of malware variants and the frequency of attacks targeting Android devices. In this paper, we propose a novel intelligent computational method to enhance the effectiveness of Android malware detection models. The proposed method combines two main techniques: (1) constructing a malware behavior profile and (2) extracting features from the malware behavior profile using graph neural networks. Specifically, to effectively construct an Android malware behavior profile,… More >

Maryam Mahsal Khan¹, Attaullah Buriro², Tahir Ahmad^3,*, Subhan Ullah⁴

CMC-Computers, Materials & Continua, Vol.81, No.3, pp. 4691-4705, 2024, DOI:10.32604/cmc.2024.057648 - 19 December 2024

Abstract With the ever-increasing continuous adoption of Industrial Internet of Things (IoT) technologies, security concerns have grown exponentially, especially regarding securing critical infrastructures. This is primarily due to the potential for backdoors to provide unauthorized access, disrupt operations, and compromise sensitive data. Backdoors pose a significant threat to the integrity and security of Industrial IoT setups by exploiting vulnerabilities and bypassing standard authentication processes. Hence its detection becomes of paramount importance. This paper not only investigates the capabilities of Machine Learning (ML) models in identifying backdoor malware but also evaluates the impact of balancing the dataset More >

Boadu Nkrumah^1,*, Michal Asante¹, Gaddafi Adbdul-Salam¹, Wofa K. Adu-Gyamfi²

Journal of Cyber Security, Vol.6, pp. 131-153, 2024, DOI:10.32604/jcs.2024.053954 - 17 December 2024

Abstract The changing nature of malware poses a cybersecurity threat, resulting in significant financial losses each year. However, traditional antivirus tools for detecting malware based on signatures are ineffective against disguised variations as they have low levels of accuracy. This study introduces Data Efficient Image Transformer-Malware Classifier (DeiT-MC), a system for classifying malware that utilizes Data-Efficient Image Transformers. DeiT-MC treats malware samples as visual data and integrates a newly developed Hybrid GridBay Optimizer (HGBO) for hyperparameter optimization and better model performance under varying malware scenarios. With HGBO, DeiT-MC outperforms the state-of-the-art techniques with a strong accuracy More >