Khaled Soliman^1,*, Mohamed Sobh², Ayman M. Bahaa-Eldin²

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883

Abstract The continuous development of cyberattacks is threatening digital transformation endeavors worldwide and leads to wide losses for various organizations. These dangers have proven that signature-based approaches are insufficient to prevent emerging and polymorphic attacks. Therefore, this paper is proposing a Robust Malicious Executable Detection (RMED) using Host-based Machine Learning Classifier to discover malicious Portable Executable (PE) files in hosts using Windows operating systems through collecting PE headers and applying machine learning mechanisms to detect unknown infected files. The authors have collected a novel reliable dataset containing 116,031 benign files and 179,071 malware samples from diverse sources to ensure the efficiency… More >

Amardeep Singh¹, Hamad Ali Abosaq², Saad Arif³, Zohaib Mushtaq^4,*, Muhammad Irfan⁵, Ghulam Abbas⁶, Arshad Ali⁷, Alanoud Al Mazroa⁸

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 857-873, 2024, DOI:10.32604/cmc.2024.048036

Abstract Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries, especially in light of the growing number of cybersecurity threats. A major and ever-present threat is Ransomware-as-a-Service (RaaS) assaults, which enable even individuals with minimal technical knowledge to conduct ransomware operations. This study provides a new approach for RaaS attack detection which uses an ensemble of deep learning models. For this purpose, the network intrusion detection dataset “UNSW-NB15” from the Intelligent Security Group of the University of New South Wales, Australia is analyzed. In the initial phase, the rectified linear… More >

Anas Bilal¹, Azhar Imran², Talha Imtiaz Baig^3,4, Xiaowen Liu^1,*, Haixia Long¹, Abdulkareem Alzahrani⁵, Muhammad Shafiq⁶

Computer Systems Science and Engineering, Vol.48, No.2, pp. 511-528, 2024, DOI:10.32604/csse.2023.039672

Abstract Artificial Intelligence (AI) is being increasingly used for diagnosing Vision-Threatening Diabetic Retinopathy (VTDR), which is a leading cause of visual impairment and blindness worldwide. However, previous automated VTDR detection methods have mainly relied on manual feature extraction and classification, leading to errors. This paper proposes a novel VTDR detection and classification model that combines different models through majority voting. Our proposed methodology involves preprocessing, data augmentation, feature extraction, and classification stages. We use a hybrid convolutional neural network-singular value decomposition (CNN-SVD) model for feature extraction and selection and an improved SVM-RBF with a Decision Tree (DT) and K-Nearest Neighbor (KNN)… More >

Peng Liao¹, Chaoge Liu², Jie Yin^1,3,*, Zhi Wang², Xiang Cui²

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3255-3285, 2024, DOI:10.32604/cmes.2024.043855

Abstract Digital assets have boomed over the past few years with the emergence of Non-fungible Tokens (NFTs). To be specific, the total trading volume of digital assets reached an astounding $55.5 billion in 2022. Nevertheless, numerous security concerns have been raised by the rapid expansion of the NFT ecosystem. NFT holders are exposed to a plethora of scams and traps, putting their digital assets at risk of being lost. However, academic research on NFT security is scarce, and the security issues have aroused rare attention. In this study, the NFT ecological process is comprehensively explored. This process falls into five different… More >

Ruirui Zhang¹, Xuguang Zhang², Minxin Yang³, Haoran Zhang^4,5,*

International Journal of Mental Health Promotion, Vol.26, No.2, pp. 117-126, 2024, DOI:10.32604/ijmhp.2023.045819

Abstract Contingent self-esteem captures the fragile nature of self-esteem and is often regarded as suboptimal to psychological functioning. Self-compassion is another important self-related concept assumed to promote mental health and well-being. However, research on the relation of self-compassion to contingent self-esteem is lacking. Two studies were conducted to explore the role of self-compassion, either as a personal characteristic or an induced mindset, in influencing the effects of contingent self-esteem on well-being. Study 1 recruited 256 Chinese college students (30.4% male, mean age = 21.72 years) who filled out measures of contingent self-esteem, self-compassion, and well-being. The results found that self-compassion moderated… More >

Shahriar Md Arman¹, Tao Yang^1,*, Shahadat Shahed², Alanoud Al Mazroa³, Afraa Attiah⁴, Linda Mohaisen⁴

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 2087-2110, 2024, DOI:10.32604/cmc.2024.047870

Abstract The rapid growth of smart technologies and services has intensified the challenges surrounding identity authentication techniques. Biometric credentials are increasingly being used for verification due to their advantages over traditional methods, making it crucial to safeguard the privacy of people’s biometric data in various scenarios. This paper offers an in-depth exploration for privacy-preserving techniques and potential threats to biometric systems. It proposes a noble and thorough taxonomy survey for privacy-preserving techniques, as well as a systematic framework for categorizing the field’s existing literature. We review the state-of-the-art methods and address their advantages and limitations in the context of various biometric… More >

Bin Luo^1,2,3, Liangguo Chen^1,2,3, Shuhua Ruan^1,2,3,*, Yonggang Luo^2,3,*

CMC-Computers, Materials & Continua, Vol.78, No.2, pp. 1731-1754, 2024, DOI:10.32604/cmc.2023.045739

Abstract Considering the stealthiness and persistence of Advanced Persistent Threats (APTs), system audit logs are leveraged in recent studies to construct system entity interaction provenance graphs to unveil threats in a host. Rule-based provenance graph APT detection approaches require elaborate rules and cannot detect unknown attacks, and existing learning-based approaches are limited by the lack of available APT attack samples or generally only perform graph-level anomaly detection, which requires lots of manual efforts to locate attack entities. This paper proposes an APT-exploited process detection approach called ThreatSniffer, which constructs the benign provenance graph from attack-free audit logs, fits normal system entity… More >

Hosam Alhakami^*

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 329-356, 2024, DOI:10.32604/cmc.2023.043439

Abstract The rapid growth of the Internet of Things (IoT) operations has necessitated the incorporation of quantum computing technologies to meet its expanding needs. This integration is motivated by the need to solve the specific issues provided by the expansion of IoT and the potential benefits that quantum computing can offer in this scenario. The combination of IoT and quantum computing creates new privacy and security problems. This study examines the critical need to prevent potential security concerns from quantum computing in IoT applications. We investigate the incorporation of quantum computing approaches within IoT security frameworks, with a focus on developing… More >

Nadir Omer^1,*, Ahmed H. Samak², Ahmed I. Taloba^3,4, Rasha M. Abd El-Aziz^3,5

Computer Systems Science and Engineering, Vol.48, No.1, pp. 77-95, 2024, DOI:10.32604/csse.2023.039265

Abstract Today’s world depends on the Internet to meet all its daily needs. The usage of the Internet is growing rapidly. The world is using the Internet more frequently than ever. The hazards of harmful attacks have also increased due to the growing reliance on the Internet. Hazards to cyber security are actions taken by someone with malicious intent to steal data, destroy computer systems, or disrupt them. Due to rising cyber security concerns, cyber security has emerged as the key component in the fight against all online threats, forgeries, and assaults. A device capable of identifying network irregularities and cyber-attacks… More >

Ming Wan¹, Minglei Hao¹, Jiawei Li¹, Jiangyuan Yao^2,*, Yan Song³

CMC-Computers, Materials & Continua, Vol.77, No.3, pp. 3573-3592, 2023, DOI:10.32604/cmc.2023.044857

Abstract The process control-oriented threat, which can exploit OT (Operational Technology) vulnerabilities to forcibly insert abnormal control commands or status information, has become one of the most devastating cyber attacks in industrial automation control. To effectively detect this threat, this paper proposes one functional pattern-related anomaly detection approach, which skillfully collaborates the BinSeg (Binary Segmentation) algorithm with FSM (Finite State Machine) to identify anomalies between measuring data and control data. By detecting the change points of measuring data, the BinSeg algorithm is introduced to generate some initial sequence segments, which can be further classified and merged into different functional patterns due… More >