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  • 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 out with well-known machine learning… 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. As far as our understanding… 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, capitalizing on the ambiguities present… More >

  • Open Access

    ARTICLE

    An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

    Syeda Wajiha Zahra1, Muhammad Nadeem2, Ali Arshad3,*, Saman Riaz3, Muhammad Abu Bakr4, Ashit Kumar Dutta5, Zaid Alzaid6, Badr Almutairi7, Sultan Almotairi8

    CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 589-616, 2024, DOI:10.32604/cmc.2023.042386

    Abstract Various organizations store data online rather than on physical servers. As the number of user’s data stored in cloud servers increases, the attack rate to access data from cloud servers also increases. Different researchers worked on different algorithms to protect cloud data from replay attacks. None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack. This study presents the development of a TKN (Text, Key and Name) cryptographic algorithm aimed at protecting data from replay attacks. The program employs distinct ways to encrypt plain text [P], a user-defined Key [K], and a Secret Code… 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 API-based features, providing a holistic… 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 classify Android applications into malware… More >

  • Open Access

    ARTICLE

    MBB-IoT: Construction and Evaluation of IoT DDoS Traffic Dataset from a New Perspective

    Yi Qing1, Xiangyu Liu2, Yanhui Du2,*

    CMC-Computers, Materials & Continua, Vol.76, No.2, pp. 2095-2119, 2023, DOI:10.32604/cmc.2023.039980

    Abstract Distributed Denial of Service (DDoS) attacks have always been a major concern in the security field. With the release of malware source codes such as BASHLITE and Mirai, Internet of Things (IoT) devices have become the new source of DDoS attacks against many Internet applications. Although there are many datasets in the field of IoT intrusion detection, such as Bot-IoT, Constrained Application Protocol–Denial of Service (CoAP-DoS), and LATAM-DDoS-IoT (some of the names of DDoS datasets), which mainly focus on DDoS attacks, the datasets describing new IoT DDoS attack scenarios are extremely rare, and only N-BaIoT and IoT-23 datasets used IoT… 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 PDF malware detection. The study… 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

    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 flow in conjunction with Machine… More >

  • Open Access

    ARTICLE

    Concept Drift Analysis and Malware Attack Detection System Using Secure Adaptive Windowing

    Emad Alsuwat1,*, Suhare Solaiman1, Hatim Alsuwat2

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 3743-3759, 2023, DOI:10.32604/cmc.2023.035126

    Abstract Concept drift is a main security issue that has to be resolved since it presents a significant barrier to the deployment of machine learning (ML) models. Due to attackers’ (and/or benign equivalents’) dynamic behavior changes, testing data distribution frequently diverges from original training data over time, resulting in substantial model failures. Due to their dispersed and dynamic nature, distributed denial-of-service attacks pose a danger to cybersecurity, resulting in attacks with serious consequences for users and businesses. This paper proposes a novel design for concept drift analysis and detection of malware attacks like Distributed Denial of Service (DDOS) in the network.… More >

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