Muhammad Waleed Ayub Ghouri¹, Guofeng Wang², Muhammad Ali Hussain³, Zhisheng Li^1,*, Tachia Chin¹

International Journal of Mental Health Promotion, Vol.26, No.4, pp. 279-292, 2024, DOI:10.32604/ijmhp.2024.044759

Abstract Crises in the past have caused devastating, long-lasting impacts on the global economy. The after-effects always bring some dynamic and rigorous challenges for businesses and governments. Such challenges have always been a point of discussion for scholars. The recent COVID-19 pandemic emaciated the global economy, leaving everyone mired in uncertainty, fear, and psychological impairments. One of the headwind features utilized by consumers during pandemic was panic buying (PB), which must be explored in various contexts for policymakers and practitioners. To address this gap, this study deployed a moderated mediation mechanism, integrating the health belief model (HBM) and competitive arousal theory… More >

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 >

Shafi Ullah¹, Sibghat Ullah Bazai^1,*, Mohammad Imran², Qazi Mudassar Ilyas^3,*, Abid Mehmood⁴, Muhammad Asim Saleem⁵, Muhmmad Aasim Rafique³, Arsalan Haider⁶, Ilyas Khan⁷, Sajid Iqbal³, Yonis Gulzar⁴, Kauser Hameed³

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 93-115, 2024, DOI:10.32604/cmc.2024.048796

Abstract Machine-to-machine (M2M) communication plays a fundamental role in autonomous IoT (Internet of Things)-based infrastructure, a vital part of the fourth industrial revolution. Machine-type communication devices (MTCDs) regularly share extensive data without human intervention while making all types of decisions. These decisions may involve controlling sensitive ventilation systems maintaining uniform temperature, live heartbeat monitoring, and several different alert systems. Many of these devices simultaneously share data to form an automated system. The data shared between machine-type communication devices (MTCDs) is prone to risk due to limited computational power, internal memory, and energy capacity. Therefore, securing the data and devices becomes challenging… 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 >

Yan Xiang^1,2, Daofeng Li^1,2,*, Xinyi Meng^1,2, Chengfeng Dong^1,2, Guanglin Qin^1,2

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1005-1023, 2024, DOI:10.32604/cmc.2024.047143

Abstract The rapid expansion of Internet of Things (IoT) devices across various sectors is driven by steadily increasing demands for interconnected and smart technologies. Nevertheless, the surge in the number of IoT device has caught the attention of cyber hackers, as it provides them with expanded avenues to access valuable data. This has resulted in a myriad of security challenges, including information leakage, malware propagation, and financial loss, among others. Consequently, developing an intrusion detection system to identify both active and potential intrusion traffic in IoT networks is of paramount importance. In this paper, we propose ResNeSt-biGRU, a practical intrusion detection… More >

Naglaa F. Soliman¹, Fatma E. Fadl-Allah², Walid El-Shafai^3,4,*, Mahmoud I. Aly², Maali Alabdulhafith¹, Fathi E. Abd El-Samie¹

CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 201-241, 2024, DOI:10.32604/cmc.2024.046757

Abstract The efficient transmission of images, which plays a large role in wireless communication systems, poses a significant challenge in the growth of multimedia technology. High-quality images require well-tuned communication standards. The Single Carrier Frequency Division Multiple Access (SC-FDMA) is adopted for broadband wireless communications, because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio (PAPR). Data transmission through open-channel networks requires much concentration on security, reliability, and integrity. The data need a space away from unauthorized access, modification, or deletion. These requirements are to be fulfilled by digital image watermarking and encryption. This paper is mainly… More >

Hongjun Zhang^1,2, Hao Zhang², Yu Lei³, Hao Ye¹, Peng Li^1,*, Desheng Shi¹

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4109-4128, 2024, DOI:10.32604/cmc.2024.048355

Abstract The study delves into the expanding role of network platforms in our daily lives, encompassing various mediums like blogs, forums, online chats, and prominent social media platforms such as Facebook, Twitter, and Instagram. While these platforms offer avenues for self-expression and community support, they concurrently harbor negative impacts, fostering antisocial behaviors like phishing, impersonation, hate speech, cyberbullying, cyberstalking, cyberterrorism, fake news propagation, spamming, and fraud. Notably, individuals also leverage these platforms to connect with authorities and seek aid during disasters. The overarching objective of this research is to address the dual nature of network platforms by proposing innovative methodologies aimed… More >

Muhammad Binsawad^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3645-3663, 2024, DOI:10.32604/cmc.2024.048183

Abstract Malware is an ever-present and dynamic threat to networks and computer systems in cybersecurity, and because of its complexity and evasiveness, it is challenging to identify using traditional signature-based detection approaches. The study article discusses the growing danger to cybersecurity that malware hidden in PDF files poses, highlighting the shortcomings of conventional detection techniques and the difficulties presented by adversarial methodologies. The article presents a new method that improves PDF virus detection by using document analysis and a Logistic Model Tree. Using a dataset from the Canadian Institute for Cybersecurity, a comparative analysis is carried out with well-known machine learning… More >

Talal Albalawi, P. Ganeshkumar^*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3511-3528, 2024, DOI:10.32604/cmc.2024.046396

Abstract The Internet of Things (IoT) is a growing technology that allows the sharing of data with other devices across wireless networks. Specifically, IoT systems are vulnerable to cyberattacks due to its opennes The proposed work intends to implement a new security framework for detecting the most specific and harmful intrusions in IoT networks. In this framework, a Covariance Linear Learning Embedding Selection (CL2ES) methodology is used at first to extract the features highly associated with the IoT intrusions. Then, the Kernel Distributed Bayes Classifier (KDBC) is created to forecast attacks based on the probability distribution value precisely. In addition, a… More >

Navaneetha Krishnan Muthunambu¹, Senthil Prabakaran², Balasubramanian Prabhu Kavin³, Kishore Senthil Siruvangur⁴, Kavitha Chinnadurai¹, Jehad Ali^5,*

CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3089-3127, 2024, DOI:10.32604/cmc.2023.043172

Abstract The extensive utilization of the Internet in everyday life can be attributed to the substantial accessibility of online services and the growing significance of the data transmitted via the Internet. Regrettably, this development has expanded the potential targets that hackers might exploit. Without adequate safeguards, data transmitted on the internet is significantly more susceptible to unauthorized access, theft, or alteration. The identification of unauthorised access attempts is a critical component of cybersecurity as it aids in the detection and prevention of malicious attacks. This research paper introduces a novel intrusion detection framework that utilizes Recurrent Neural Networks (RNN) integrated with… More >