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  • 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 - 08 October 2023

    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 - 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

    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 - 21 August 2023

    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 >

  • Open Access

    ARTICLE

    Learning-Based Artificial Algae Algorithm with Optimal Machine Learning Enabled Malware Detection

    Khaled M. Alalayah1, Fatma S. Alrayes2, Mohamed K. Nour3, Khadija M. Alaidarous1, Ibrahim M. Alwayle1, Heba Mohsen4, Ibrahim Abdulrab Ahmed5, Mesfer Al Duhayyim6,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3103-3119, 2023, DOI:10.32604/csse.2023.034034 - 03 April 2023

    Abstract Malware is a ‘malicious software program that performs multiple cyberattacks on the Internet, involving fraud, scams, nation-state cyberwar, and cybercrime. Such malicious software programs come under different classifications, namely Trojans, viruses, spyware, worms, ransomware, Rootkit, botnet malware, etc. Ransomware is a kind of malware that holds the victim’s data hostage by encrypting the information on the user’s computer to make it inaccessible to users and only decrypting it; then, the user pays a ransom procedure of a sum of money. To prevent detection, various forms of ransomware utilize more than one mechanism in their attack… More >

  • Open Access

    ARTICLE

    Graph Convolutional Neural Network Based Malware Detection in IoT-Cloud Environment

    Faisal S. Alsubaei1, Haya Mesfer Alshahrani2, Khaled Tarmissi3, Abdelwahed Motwakel4,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2897-2914, 2023, DOI:10.32604/iasc.2023.034907 - 15 March 2023

    Abstract Cybersecurity has become the most significant research area in the domain of the Internet of Things (IoT) owing to the ever-increasing number of cyberattacks. The rapid penetration of Android platforms in mobile devices has made the detection of malware attacks a challenging process. Furthermore, Android malware is increasing on a daily basis. So, precise malware detection analytical techniques need a large number of hardware resources that are significantly resource-limited for mobile devices. In this research article, an optimal Graph Convolutional Neural Network-based Malware Detection and classification (OGCNN-MDC) model is introduced for an IoT-cloud environment. The… More >

  • Open Access

    ARTICLE

    Clustering-Aided Supervised Malware Detection with Specialized Classifiers and Early Consensus

    Murat Dener*, Sercan Gulburun

    CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 1235-1251, 2023, DOI:10.32604/cmc.2023.036357 - 06 February 2023

    Abstract One of the most common types of threats to the digital world is malicious software. It is of great importance to detect and prevent existing and new malware before it damages information assets. Machine learning approaches are used effectively for this purpose. In this study, we present a model in which supervised and unsupervised learning algorithms are used together. Clustering is used to enhance the prediction performance of the supervised classifiers. The aim of the proposed model is to make predictions in the shortest possible time with high accuracy and f1 score. In the first… More >

  • Open Access

    ARTICLE

    Byte-Level Function-Associated Method for Malware Detection

    Jingwei Hao*, Senlin Luo, Limin Pan

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 719-734, 2023, DOI:10.32604/csse.2023.033923 - 20 January 2023

    Abstract The byte stream is widely used in malware detection due to its independence of reverse engineering. However, existing methods based on the byte stream implement an indiscriminate feature extraction strategy, which ignores the byte function difference in different segments and fails to achieve targeted feature extraction for various byte semantic representation modes, resulting in byte semantic confusion. To address this issue, an enhanced adversarial byte function associated method for malware backdoor attack is proposed in this paper by categorizing various function bytes into three functions involving structure, code, and data. The Minhash algorithm, grayscale mapping, More >

  • Open Access

    ARTICLE

    Malware Detection in Android IoT Systems Using Deep Learning

    Muhammad Waqar1, Sabeeh Fareed1, Ajung Kim2,*, Saif Ur Rehman Malik3, Muhammad Imran1, Muhammad Usman Yaseen1

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 4399-4415, 2023, DOI:10.32604/cmc.2023.032984 - 31 October 2022

    Abstract The Android Operating System (AOS) has been evolving since its inception and it has become one of the most widely used operating system for the Internet of Things (IoT). Due to the high popularity and reliability of AOS for IoT, it is a target of many cyber-attacks which can cause compromise of privacy, financial loss, data integrity, unauthorized access, denial of services and so on. The Android-based IoT (AIoT) devices are extremely vulnerable to various malwares due to the open nature and high acceptance of Android in the market. Recently, several detection preventive malwares are More >

  • Open Access

    ARTICLE

    Optimal Bottleneck-Driven Deep Belief Network Enabled Malware Classification on IoT-Cloud Environment

    Mohammed Maray1, Hamed Alqahtani2, Saud S. Alotaibi3, Fatma S. Alrayes4, Nuha Alshuqayran5, Mrim M. Alnfiai6, Amal S. Mehanna7, Mesfer Al Duhayyim8,*

    CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 3101-3115, 2023, DOI:10.32604/cmc.2023.032969 - 31 October 2022

    Abstract Cloud Computing (CC) is the most promising and advanced technology to store data and offer online services in an effective manner. When such fast evolving technologies are used in the protection of computer-based systems from cyberattacks, it brings several advantages compared to conventional data protection methods. Some of the computer-based systems that effectively protect the data include Cyber-Physical Systems (CPS), Internet of Things (IoT), mobile devices, desktop and laptop computer, and critical systems. Malicious software (malware) is nothing but a type of software that targets the computer-based systems so as to launch cyber-attacks and threaten… 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 >

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