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  • Open Access

    ARTICLE

    Network Intrusion Detection in Internet of Blended Environment Using Ensemble of Heterogeneous Autoencoders (E-HAE)

    Lelisa Adeba Jilcha1, Deuk-Hun Kim2, Julian Jang-Jaccard3, Jin Kwak4,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3261-3284, 2023, DOI:10.32604/csse.2023.037615

    Abstract Contemporary attackers, mainly motivated by financial gain, consistently devise sophisticated penetration techniques to access important information or data. The growing use of Internet of Things (IoT) technology in the contemporary convergence environment to connect to corporate networks and cloud-based applications only worsens this situation, as it facilitates multiple new attack vectors to emerge effortlessly. As such, existing intrusion detection systems suffer from performance degradation mainly because of insufficient considerations and poorly modeled detection systems. To address this problem, we designed a blended threat detection approach, considering the possible impact and dimensionality of new attack surfaces… More >

  • Open Access

    ARTICLE

    Hyperparameter Tuned Deep Hybrid Denoising Autoencoder Breast Cancer Classification on Digital Mammograms

    Manar Ahmed Hamza*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2879-2895, 2023, DOI:10.32604/iasc.2023.034719

    Abstract Breast Cancer (BC) is considered the most commonly scrutinized cancer in women worldwide, affecting one in eight women in a lifetime. Mammography screening becomes one such standard method that is helpful in identifying suspicious masses’ malignancy of BC at an initial level. However, the prior identification of masses in mammograms was still challenging for extremely dense and dense breast categories and needs an effective and automatic mechanisms for helping radiotherapists in diagnosis. Deep learning (DL) techniques were broadly utilized for medical imaging applications, particularly breast mass classification. The advancements in the DL field paved the… More >

  • Open Access

    ARTICLE

    Gender Identification Using Marginalised Stacked Denoising Autoencoders on Twitter Data

    Badriyya B. Al-onazi1, Mohamed K. Nour2, Hassan Alshamrani3, Mesfer Al Duhayyim4,*, Heba Mohsen5, Amgad Atta Abdelmageed6, Gouse Pasha Mohammed6, Abu Sarwar Zamani6

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2529-2544, 2023, DOI:10.32604/iasc.2023.034623

    Abstract Gender analysis of Twitter could reveal significant socio-cultural differences between female and male users. Efforts had been made to analyze and automatically infer gender formerly for more commonly spoken languages’ content, but, as we now know that limited work is being undertaken for Arabic. Most of the research works are done mainly for English and least amount of effort for non-English language. The study for Arabic demographic inference like gender is relatively uncommon for social networking users, especially for Twitter. Therefore, this study aims to design an optimal marginalized stacked denoising autoencoder for gender identification… More >

  • Open Access

    ARTICLE

    A Novel Deep Learning Representation for Industrial Control System Data

    Bowen Zhang1,2,3, Yanbo Shi4, Jianming Zhao1,2,3,*, Tianyu Wang1,2,3, Kaidi Wang5

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2703-2717, 2023, DOI:10.32604/iasc.2023.033762

    Abstract Feature extraction plays an important role in constructing artificial intelligence (AI) models of industrial control systems (ICSs). Three challenges in this field are learning effective representation from high-dimensional features, data heterogeneity, and data noise due to the diversity of data dimensions, formats and noise of sensors, controllers and actuators. Hence, a novel unsupervised learning autoencoder model is proposed for ICS data in this paper. Although traditional methods only capture the linear correlations of ICS features, our deep industrial representation learning model (DIRL) based on a convolutional neural network can mine high-order features, thus solving the… More >

  • Open Access

    ARTICLE

    Augmenting Android Malware Using Conditional Variational Autoencoder for the Malware Family Classification

    Younghoon Ban, Jeong Hyun Yi, Haehyun Cho*

    Computer Systems Science and Engineering, Vol.46, No.2, pp. 2215-2230, 2023, DOI:10.32604/csse.2023.036555

    Abstract Android malware has evolved in various forms such as adware that continuously exposes advertisements, banking malware designed to access users’ online banking accounts, and Short Message Service (SMS) malware that uses a Command & Control (C&C) server to send malicious SMS, intercept SMS, and steal data. By using many malicious strategies, the number of malware is steadily increasing. Increasing Android malware threats numerous users, and thus, it is necessary to detect malware quickly and accurately. Each malware has distinguishable characteristics based on its actions. Therefore, security researchers have tried to categorize malware based on their… More >

  • Open Access

    ARTICLE

    A Lightweight Deep Autoencoder Scheme for Cyberattack Detection in the Internet of Things

    Maha Sabir1, Jawad Ahmad2,*, Daniyal Alghazzawi1

    Computer Systems Science and Engineering, Vol.46, No.1, pp. 57-72, 2023, DOI:10.32604/csse.2023.034277

    Abstract The Internet of things (IoT) is an emerging paradigm that integrates devices and services to collect real-time data from surroundings and process the information at a very high speed to make a decision. Despite several advantages, the resource-constrained and heterogeneous nature of IoT networks makes them a favorite target for cybercriminals. A single successful attempt of network intrusion can compromise the complete IoT network which can lead to unauthorized access to the valuable information of consumers and industries. To overcome the security challenges of IoT networks, this article proposes a lightweight deep autoencoder (DAE) based… More >

  • Open Access

    ARTICLE

    A Convolutional Autoencoder Based Fault Detection Method for Metro Railway Turnout

    Chen Chen1,2, Xingqiu Li2,3,*, Kai Huang4, Zhongwei Xu1, Meng Mei1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 471-485, 2023, DOI:10.32604/cmes.2023.024033

    Abstract Railway turnout is one of the critical equipment of Switch & Crossing (S&C) Systems in railway, related to the train’s safety and operation efficiency. With the advancement of intelligent sensors, data-driven fault detection technology for railway turnout has become an important research topic. However, little research in the literature has investigated the capability of data-driven fault detection technology for metro railway turnout. This paper presents a convolutional autoencoder-based fault detection method for the metro railway turnout considering human field inspection scenarios. First, the one-dimensional original time-series signal is converted into a two-dimensional image by data More >

  • Open Access

    ARTICLE

    Optimal Deep Learning Driven Intrusion Detection in SDN-Enabled IoT Environment

    Mohammed Maray1, Haya Mesfer Alshahrani2, Khalid A. Alissa3, Najm Alotaibi4, Abdulbaset Gaddah5, Ali Meree1,6, Mahmoud Othman7, Manar Ahmed Hamza8,*

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6587-6604, 2023, DOI:10.32604/cmc.2023.034176

    Abstract In recent years, wireless networks are widely used in different domains. This phenomenon has increased the number of Internet of Things (IoT) devices and their applications. Though IoT has numerous advantages, the commonly-used IoT devices are exposed to cyber-attacks periodically. This scenario necessitates real-time automated detection and the mitigation of different types of attacks in high-traffic networks. The Software-Defined Networking (SDN) technique and the Machine Learning (ML)-based intrusion detection technique are effective tools that can quickly respond to different types of attacks in the IoT networks. The Intrusion Detection System (IDS) models can be employed… More >

  • Open Access

    ARTICLE

    Optimizing Big Data Retrieval and Job Scheduling Using Deep Learning Approaches

    Bao Rong Chang1, Hsiu-Fen Tsai2,*, Yu-Chieh Lin1

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.2, pp. 783-815, 2023, DOI:10.32604/cmes.2022.020128

    Abstract Big data analytics in business intelligence do not provide effective data retrieval methods and job scheduling that will cause execution inefficiency and low system throughput. This paper aims to enhance the capability of data retrieval and job scheduling to speed up the operation of big data analytics to overcome inefficiency and low throughput problems. First, integrating stacked sparse autoencoder and Elasticsearch indexing explored fast data searching and distributed indexing, which reduces the search scope of the database and dramatically speeds up data searching. Next, exploiting a deep neural network to predict the approximate execution time More >

  • Open Access

    ARTICLE

    Spoofing Face Detection Using Novel Edge-Net Autoencoder for Security

    Amal H. Alharbi1, S. Karthick2, K. Venkatachalam3, Mohamed Abouhawwash4,5, Doaa Sami Khafaga1,*

    Intelligent Automation & Soft Computing, Vol.35, No.3, pp. 2773-2787, 2023, DOI:10.32604/iasc.2023.030763

    Abstract Recent security applications in mobile technologies and computer systems use face recognition for high-end security. Despite numerous security techniques, face recognition is considered a high-security control. Developers fuse and carry out face identification as an access authority into these applications. Still, face identification authentication is sensitive to attacks with a 2-D photo image or captured video to access the system as an authorized user. In the existing spoofing detection algorithm, there was some loss in the recreation of images. This research proposes an unobtrusive technique to detect face spoofing attacks that apply a single frame… More >

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