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  • Open Access

    ARTICLE

    An Attention-Based Approach to Enhance the Detection and Classification of Android Malware

    Abdallah Ghourabi*

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2743-2760, 2024, DOI:10.32604/cmc.2024.053163

    Abstract The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware. These malicious applications have become a serious concern to the security of Android systems. To address this problem, researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples. However, most existing studies have focused on the classification task and overlooked the feature selection process, which is crucial to reduce the training time and maintain or improve the classification results. The… More >

  • Open Access

    ARTICLE

    Fine-Tuning Cyber Security Defenses: Evaluating Supervised Machine Learning Classifiers for Windows Malware Detection

    Islam Zada1,*, Mohammed Naif Alatawi2, Syed Muhammad Saqlain1, Abdullah Alshahrani3, Adel Alshamran4, Kanwal Imran5, Hessa Alfraihi6

    CMC-Computers, Materials & Continua, Vol.80, No.2, pp. 2917-2939, 2024, DOI:10.32604/cmc.2024.052835

    Abstract Malware attacks on Windows machines pose significant cybersecurity threats, necessitating effective detection and prevention mechanisms. Supervised machine learning classifiers have emerged as promising tools for malware detection. However, there remains a need for comprehensive studies that compare the performance of different classifiers specifically for Windows malware detection. Addressing this gap can provide valuable insights for enhancing cybersecurity strategies. While numerous studies have explored malware detection using machine learning techniques, there is a lack of systematic comparison of supervised classifiers for Windows malware detection. Understanding the relative effectiveness of these classifiers can inform the selection of… More >

  • Open Access

    ARTICLE

    MoBShield: A Novel XML Approach for Securing Mobile Banking

    Saeed Seraj1, Ali Safaa Sadiq1,*, Omprakash Kaiwartya1, Mohammad Aljaidi2, Alexandros Konios1, Mohammed Ali3, Mohammed Abazeed3

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2123-2149, 2024, DOI:10.32604/cmc.2024.048914

    Abstract Mobile banking security has witnessed significant R&D attention from both financial institutions and academia. This is due to the growing number of mobile baking applications and their reachability and usefulness to society. However, these applications are also attractive prey for cybercriminals, who use a variety of malware to steal personal banking information. Related literature in mobile banking security requires many permissions that are not necessary for the application’s intended security functionality. In this context, this paper presents a novel efficient permission identification approach for securing mobile banking (MoBShield) to detect and prevent malware. A permission-based… More >

  • Open Access

    ARTICLE

    Outsmarting Android Malware with Cutting-Edge Feature Engineering and Machine Learning Techniques

    Ahsan Wajahat1, Jingsha He1, Nafei Zhu1, Tariq Mahmood2,3, Tanzila Saba2, Amjad Rehman Khan2, Faten S. Alamri4,*

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 651-673, 2024, DOI:10.32604/cmc.2024.047530

    Abstract The growing usage of Android smartphones has led to a significant rise in incidents of Android malware and privacy breaches. This escalating security concern necessitates the development of advanced technologies capable of automatically detecting and mitigating malicious activities in Android applications (apps). Such technologies are crucial for safeguarding user data and maintaining the integrity of mobile devices in an increasingly digital world. Current methods employed to detect sensitive data leaks in Android apps are hampered by two major limitations they require substantial computational resources and are prone to a high frequency of false positives. This… More >

  • Open Access

    ARTICLE

    Enhancing PDF Malware Detection through Logistic Model Trees

    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… More >

  • Open Access

    ARTICLE

    Covalent Bond Based Android Malware Detection Using Permission and System Call Pairs

    Rahul Gupta1, Kapil Sharma1,*, R. K. Garg2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 4283-4301, 2024, DOI:10.32604/cmc.2024.046890

    Abstract The prevalence of smartphones is deeply embedded in modern society, impacting various aspects of our lives. Their versatility and functionalities have fundamentally changed how we communicate, work, seek entertainment, and access information. Among the many smartphones available, those operating on the Android platform dominate, being the most widely used type. This widespread adoption of the Android OS has significantly contributed to increased malware attacks targeting the Android ecosystem in recent years. Therefore, there is an urgent need to develop new methods for detecting Android malware. The literature contains numerous works related to Android malware detection.… More >

  • Open Access

    ARTICLE

    An Empirical Study on the Effectiveness of Adversarial Examples in Malware Detection

    Younghoon Ban, Myeonghyun Kim, Haehyun Cho*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.3, pp. 3535-3563, 2024, DOI:10.32604/cmes.2023.046658

    Abstract Antivirus vendors and the research community employ Machine Learning (ML) or Deep Learning (DL)-based static analysis techniques for efficient identification of new threats, given the continual emergence of novel malware variants. On the other hand, numerous researchers have reported that Adversarial Examples (AEs), generated by manipulating previously detected malware, can successfully evade ML/DL-based classifiers. Commercial antivirus systems, in particular, have been identified as vulnerable to such AEs. This paper firstly focuses on conducting black-box attacks to circumvent ML/DL-based malware classifiers. Our attack method utilizes seven different perturbations, including Overlay Append, Section Append, and Break Checksum,… More >

  • Open Access

    ARTICLE

    Hybrid Malware Variant Detection Model with Extreme Gradient Boosting and Artificial Neural Network Classifiers

    Asma A. Alhashmi1, Abdulbasit A. Darem1,*, Sultan M. Alanazi1, Abdullah M. Alashjaee2, Bader Aldughayfiq3, Fuad A. Ghaleb4,5, Shouki A. Ebad1, Majed A. Alanazi1

    CMC-Computers, Materials & Continua, Vol.76, No.3, pp. 3483-3498, 2023, DOI:10.32604/cmc.2023.041038

    Abstract In an era marked by escalating cybersecurity threats, our study addresses the challenge of malware variant detection, a significant concern for a multitude of sectors including petroleum and mining organizations. This paper presents an innovative Application Programmable Interface (API)-based hybrid model designed to enhance the detection performance of malware variants. This model integrates eXtreme Gradient Boosting (XGBoost) and an Artificial Neural Network (ANN) classifier, offering a potent response to the sophisticated evasion and obfuscation techniques frequently deployed by malware authors. The model’s design capitalizes on the benefits of both static and dynamic analysis to extract… More >

  • 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

    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

    Comparative Analysis of Machine Learning Models for PDF Malware Detection: Evaluating Different Training and Testing Criteria

    Bilal Khan1, Muhammad Arshad2, Sarwar Shah Khan3,4,*

    Journal of Cyber Security, Vol.5, pp. 1-11, 2023, DOI:10.32604/jcs.2023.042501

    Abstract The proliferation of maliciously coded documents as file transfers increase has led to a rise in sophisticated attacks. Portable Document Format (PDF) files have emerged as a major attack vector for malware due to their adaptability and wide usage. Detecting malware in PDF files is challenging due to its ability to include various harmful elements such as embedded scripts, exploits, and malicious URLs. This paper presents a comparative analysis of machine learning (ML) techniques, including Naive Bayes (NB), K-Nearest Neighbor (KNN), Average One Dependency Estimator (A1DE), Random Forest (RF), and Support Vector Machine (SVM) for More >

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