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  • Open Access

    ARTICLE

    Explainable Classification Model for Android Malware Analysis Using API and Permission-Based Features

    Nida Aslam1,*, Irfan Ullah Khan2, Salma Abdulrahman Bader2, Aisha Alansari3, Lama Abdullah Alaqeel2, Razan Mohammed Khormy2, Zahra Abdultawab AlKubaish2, Tariq Hussain4,*

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3167-3188, 2023, DOI:10.32604/cmc.2023.039721 - 08 October 2023

    Abstract One of the most widely used smartphone operating systems, Android, is vulnerable to cutting-edge malware that employs sophisticated logic. Such malware attacks could lead to the execution of unauthorized acts on the victims’ devices, stealing personal information and causing hardware damage. In previous studies, machine learning (ML) has shown its efficacy in detecting malware events and classifying their types. However, attackers are continuously developing more sophisticated methods to bypass detection. Therefore, up-to-date datasets must be utilized to implement proactive models for detecting malware events in Android mobile devices. Therefore, this study employed ML algorithms to… More >

  • Open Access

    ARTICLE

    Android Malware Detection Using ResNet-50 Stacking

    Lojain Nahhas1, Marwan Albahar1,*, Abdullah Alammari2, Anca Jurcut3

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3997-4014, 2023, DOI:10.32604/cmc.2023.028316 - 31 October 2022

    Abstract There has been an increase in attacks on mobile devices, such as smartphones and tablets, due to their growing popularity. Mobile malware is one of the most dangerous threats, causing both security breaches and financial losses. Mobile malware is likely to continue to evolve and proliferate to carry out a variety of cybercrimes on mobile devices. Mobile malware specifically targets Android operating system as it has grown in popularity. The rapid proliferation of Android malware apps poses a significant security risk to users, making static and manual analysis of malicious files difficult. Therefore, efficient identification… More >

  • Open Access

    ARTICLE

    Behavioral Intrusion Prediction Model on Bayesian Network over Healthcare Infrastructure

    Mohammad Hafiz Mohd Yusof1,*, Abdullah Mohd Zin2, Nurhizam Safie Mohd Satar2

    CMC-Computers, Materials & Continua, Vol.72, No.2, pp. 2445-2466, 2022, DOI:10.32604/cmc.2022.023571 - 29 March 2022

    Abstract Due to polymorphic nature of malware attack, a signature-based analysis is no longer sufficient to solve polymorphic and stealth nature of malware attacks. On the other hand, state-of-the-art methods like deep learning require labelled dataset as a target to train a supervised model. This is unlikely to be the case in production network as the dataset is unstructured and has no label. Hence an unsupervised learning is recommended. Behavioral study is one of the techniques to elicit traffic pattern. However, studies have shown that existing behavioral intrusion detection model had a few issues which had… More >

  • Open Access

    ARTICLE

    A Novel Framework for Windows Malware Detection Using a Deep Learning Approach

    Abdulbasit A. Darem*

    CMC-Computers, Materials & Continua, Vol.72, No.1, pp. 461-479, 2022, DOI:10.32604/cmc.2022.023566 - 24 February 2022

    Abstract Malicious software (malware) is one of the main cyber threats that organizations and Internet users are currently facing. Malware is a software code developed by cybercriminals for damage purposes, such as corrupting the system and data as well as stealing sensitive data. The damage caused by malware is substantially increasing every day. There is a need to detect malware efficiently and automatically and remove threats quickly from the systems. Although there are various approaches to tackle malware problems, their prevalence and stealthiness necessitate an effective method for the detection and prevention of malware attacks. The More >

  • Open Access

    ARTICLE

    MMALE—A Methodology for Malware Analysis in Linux Environments

    José Javier de Vicente Mohino1, Javier Bermejo Higuera1, Juan Ramón Bermejo Higuera1, Juan Antonio Sicilia Montalvo1,*, Manuel Sánchez Rubio1, José Javier Martínez Herraiz2

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 1447-1469, 2021, DOI:10.32604/cmc.2021.014596 - 05 February 2021

    Abstract In a computer environment, an operating system is prone to malware, and even the Linux operating system is not an exception. In recent years, malware has evolved, and attackers have become more qualified compared to a few years ago. Furthermore, Linux-based systems have become more attractive to cybercriminals because of the increasing use of the Linux operating system in web servers and Internet of Things (IoT) devices. Windows is the most employed OS, so most of the research efforts have been focused on its malware protection rather than on other operating systems. As a result,… More >

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