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  • Open Access

    ARTICLE

    Trust Score-Based Malicious Vehicle Detection Scheme in Vehicular Network Environments

    Wenming Wang1,2,3,*, Zhiquan Liu1, Shumin Zhang1, Guijiang Liu1

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 2517-2545, 2024, DOI:10.32604/cmc.2024.055184 - 18 November 2024

    Abstract Advancements in the vehicular network technology enable real-time interconnection, data sharing, and intelligent cooperative driving among vehicles. However, malicious vehicles providing illegal and incorrect information can compromise the interests of vehicle users. Trust mechanisms serve as an effective solution to this issue. In recent years, many researchers have incorporated blockchain technology to manage and incentivize vehicle nodes, incurring significant overhead and storage requirements due to the frequent ingress and egress of vehicles within the area. In this paper, we propose a distributed vehicular network scheme based on trust scores. Specifically, the designed architecture partitions multiple More >

  • Open Access

    ARTICLE

    A Probabilistic Trust Model and Control Algorithm to Protect 6G Networks against Malicious Data Injection Attacks in Edge Computing Environments

    Borja Bordel Sánchez1,*, Ramón Alcarria2, Tomás Robles1

    CMES-Computer Modeling in Engineering & Sciences, Vol.141, No.1, pp. 631-654, 2024, DOI:10.32604/cmes.2024.050349 - 20 August 2024

    Abstract Future 6G communications are envisioned to enable a large catalogue of pioneering applications. These will range from networked Cyber-Physical Systems to edge computing devices, establishing real-time feedback control loops critical for managing Industry 5.0 deployments, digital agriculture systems, and essential infrastructures. The provision of extensive machine-type communications through 6G will render many of these innovative systems autonomous and unsupervised. While full automation will enhance industrial efficiency significantly, it concurrently introduces new cyber risks and vulnerabilities. In particular, unattended systems are highly susceptible to trust issues: malicious nodes and false information can be easily introduced into… More >

  • Open Access

    ARTICLE

    A GAN-EfficientNet-Based Traceability Method for Malicious Code Variant Families

    Li Li*, Qing Zhang, Youran Kong

    CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 801-818, 2024, DOI:10.32604/cmc.2024.051916 - 18 July 2024

    Abstract Due to the diversity and unpredictability of changes in malicious code, studying the traceability of variant families remains challenging. In this paper, we propose a GAN-EfficientNetV2-based method for tracing families of malicious code variants. This method leverages the similarity in layouts and textures between images of malicious code variants from the same source and their original family of malicious code images. The method includes a lightweight classifier and a simulator. The classifier utilizes the enhanced EfficientNetV2 to categorize malicious code images and can be easily deployed on mobile, embedded, and other devices. The simulator utilizes… More >

  • Open Access

    ARTICLE

    Detecting Malicious Uniform Resource Locators Using an Applied Intelligence Framework

    Simona-Vasilica Oprea*, Adela Bâra

    CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 3827-3853, 2024, DOI:10.32604/cmc.2024.051598 - 20 June 2024

    Abstract The potential of text analytics is revealed by Machine Learning (ML) and Natural Language Processing (NLP) techniques. In this paper, we propose an NLP framework that is applied to multiple datasets to detect malicious Uniform Resource Locators (URLs). Three categories of features, both ML and Deep Learning (DL) algorithms and a ranking schema are included in the proposed framework. We apply frequency and prediction-based embeddings, such as hash vectorizer, Term Frequency-Inverse Dense Frequency (TF-IDF) and predictors, word to vector-word2vec (continuous bag of words, skip-gram) from Google, to extract features from text. Further, we apply more… More >

  • Open Access

    ARTICLE

    Suboptimal Feature Selection Techniques for Effective Malicious Traffic Detection on Lightweight Devices

    So-Eun Jeon1, Ye-Sol Oh1, Yeon-Ji Lee1, Il-Gu Lee1,2,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1669-1687, 2024, DOI:10.32604/cmes.2024.047239 - 20 May 2024

    Abstract With the advancement of wireless network technology, vast amounts of traffic have been generated, and malicious traffic attacks that threaten the network environment are becoming increasingly sophisticated. While signature-based detection methods, static analysis, and dynamic analysis techniques have been previously explored for malicious traffic detection, they have limitations in identifying diversified malware traffic patterns. Recent research has been focused on the application of machine learning to detect these patterns. However, applying machine learning to lightweight devices like IoT devices is challenging because of the high computational demands and complexity involved in the learning process. In… More >

  • Open Access

    ARTICLE

    A New Malicious Code Classification Method for the Security of Financial Software

    Xiaonan Li1,2, Qiang Wang1, Conglai Fan2,3, Wei Zhan1, Mingliang Zhang4,*

    Computer Systems Science and Engineering, Vol.48, No.3, pp. 773-792, 2024, DOI:10.32604/csse.2024.039849 - 20 May 2024

    Abstract The field of finance heavily relies on cybersecurity to safeguard its systems and clients from harmful software. The identification of malevolent code within financial software is vital for protecting both the financial system and individual clients. Nevertheless, present detection models encounter limitations in their ability to identify malevolent code and its variations, all while encompassing a multitude of parameters. To overcome these obstacles, we introduce a lean model for classifying families of malevolent code, formulated on Ghost-DenseNet-SE. This model integrates the Ghost module, DenseNet, and the squeeze-and-excitation (SE) channel domain attention mechanism. It substitutes the… More >

  • Open Access

    ARTICLE

    Malware Attacks Detection in IoT Using Recurrent Neural Network (RNN)

    Abeer Abdullah Alsadhan1, Abdullah A. Al-Atawi2, Hanen karamti3, Abid Jameel4, Islam Zada5, Tan N. Nguyen6,*

    Intelligent Automation & Soft Computing, Vol.39, No.2, pp. 135-155, 2024, DOI:10.32604/iasc.2023.041130 - 21 May 2024

    Abstract IoT (Internet of Things) devices are being used more and more in a variety of businesses and for a variety of tasks, such as environmental data collection in both civilian and military situations. They are a desirable attack target for malware intended to infect specific IoT devices due to their growing use in a variety of applications and their increasing computational and processing power. In this study, we investigate the possibility of detecting IoT malware using recurrent neural networks (RNNs). RNN is used in the proposed method to investigate the execution operation codes of ARM-based More >

  • Open Access

    ARTICLE

    SCIRD: Revealing Infection of Malicious Software in Edge Computing-Enabled IoT Networks

    Jiehao Ye, Wen Cheng, Xiaolong Liu, Wenyi Zhu, Xuan’ang Wu, Shigen Shen*

    CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2743-2769, 2024, DOI:10.32604/cmc.2024.049985 - 15 May 2024

    Abstract The Internet of Things (IoT) has characteristics such as node mobility, node heterogeneity, link heterogeneity, and topology heterogeneity. In the face of the IoT characteristics and the explosive growth of IoT nodes, which brings about large-scale data processing requirements, edge computing architecture has become an emerging network architecture to support IoT applications due to its ability to provide powerful computing capabilities and good service functions. However, the defense mechanism of Edge Computing-enabled IoT Nodes (ECIoTNs) is still weak due to their limited resources, so that they are susceptible to malicious software spread, which can compromise… More >

  • Open Access

    ARTICLE

    Robust Malicious Executable Detection Using Host-Based Machine Learning Classifier

    Khaled Soliman1,*, Mohamed Sobh2, Ayman M. Bahaa-Eldin2

    CMC-Computers, Materials & Continua, Vol.79, No.1, pp. 1419-1439, 2024, DOI:10.32604/cmc.2024.048883 - 25 April 2024

    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… More >

  • Open Access

    ARTICLE

    BSTFNet: An Encrypted Malicious Traffic Classification Method Integrating Global Semantic and Spatiotemporal Features

    Hong Huang1, Xingxing Zhang1,*, Ye Lu1, Ze Li1, Shaohua Zhou2

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3929-3951, 2024, DOI:10.32604/cmc.2024.047918 - 26 March 2024

    Abstract While encryption technology safeguards the security of network communications, malicious traffic also uses encryption protocols to obscure its malicious behavior. To address the issues of traditional machine learning methods relying on expert experience and the insufficient representation capabilities of existing deep learning methods for encrypted malicious traffic, we propose an encrypted malicious traffic classification method that integrates global semantic features with local spatiotemporal features, called BERT-based Spatio-Temporal Features Network (BSTFNet). At the packet-level granularity, the model captures the global semantic features of packets through the attention mechanism of the Bidirectional Encoder Representations from Transformers (BERT)… More >

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