Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (101)
  • Open Access

    ARTICLE

    Fine-Grained Multivariate Time Series Anomaly Detection in IoT

    Shiming He1,4, Meng Guo1, Bo Yang1, Osama Alfarraj2, Amr Tolba2, Pradip Kumar Sharma3, Xi’ai Yan4,*

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5027-5047, 2023, DOI:10.32604/cmc.2023.038551

    Abstract Sensors produce a large amount of multivariate time series data to record the states of Internet of Things (IoT) systems. Multivariate time series timestamp anomaly detection (TSAD) can identify timestamps of attacks and malfunctions. However, it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis, a process referred to as fine-grained anomaly detection (FGAD). Although further FGAD can be extended based on TSAD methods, existing works do not provide a quantitative evaluation, and the performance is unknown. Therefore, to tackle the FGAD problem, this paper first verifies that the TSAD methods achieve low… More >

  • Open Access

    ARTICLE

    RRCNN: Request Response-Based Convolutional Neural Network for ICS Network Traffic Anomaly Detection

    Yan Du1,2, Shibin Zhang1,2,*, Guogen Wan1,2, Daohua Zhou3, Jiazhong Lu1,2, Yuanyuan Huang1,2, Xiaoman Cheng4, Yi Zhang4, Peilin He5

    CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5743-5759, 2023, DOI:10.32604/cmc.2023.035919

    Abstract Nowadays, industrial control system (ICS) has begun to integrate with the Internet. While the Internet has brought convenience to ICS, it has also brought severe security concerns. Traditional ICS network traffic anomaly detection methods rely on statistical features manually extracted using the experience of network security experts. They are not aimed at the original network data, nor can they capture the potential characteristics of network packets. Therefore, the following improvements were made in this study: (1) A dataset that can be used to evaluate anomaly detection algorithms is produced, which provides raw network data. (2) A request response-based convolutional neural… More >

  • Open Access

    ARTICLE

    Intelligent Intrusion Detection for Industrial Internet of Things Using Clustering Techniques

    Noura Alenezi, Ahamed Aljuhani*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 2899-2915, 2023, DOI:10.32604/csse.2023.036657

    Abstract The rapid growth of the Internet of Things (IoT) in the industrial sector has given rise to a new term: the Industrial Internet of Things (IIoT). The IIoT is a collection of devices, apps, and services that connect physical and virtual worlds to create smart, cost-effective, and scalable systems. Although the IIoT has been implemented and incorporated into a wide range of industrial control systems, maintaining its security and privacy remains a significant concern. In the IIoT contexts, an intrusion detection system (IDS) can be an effective security solution for ensuring data confidentiality, integrity, and availability. In this paper, we… More >

  • 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 due to the aforementioned convergence.… More >

  • Open Access

    ARTICLE

    An Efficient Attention-Based Strategy for Anomaly Detection in Surveillance Video

    Sareer Ul Amin1, Yongjun Kim2, Irfan Sami3, Sangoh Park1,*, Sanghyun Seo4,*

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3939-3958, 2023, DOI:10.32604/csse.2023.034805

    Abstract In the present technological world, surveillance cameras generate an immense amount of video data from various sources, making its scrutiny tough for computer vision specialists. It is difficult to search for anomalous events manually in these massive video records since they happen infrequently and with a low probability in real-world monitoring systems. Therefore, intelligent surveillance is a requirement of the modern day, as it enables the automatic identification of normal and aberrant behavior using artificial intelligence and computer vision technologies. In this article, we introduce an efficient Attention-based deep-learning approach for anomaly detection in surveillance video (ADSV). At the input… More >

  • Open Access

    ARTICLE

    Anomaly Detection and Classification in Streaming PMU Data in Smart Grids

    A. L. Amutha1, R. Annie Uthra1,*, J. Preetha Roselyn2, R. Golda Brunet3

    Computer Systems Science and Engineering, Vol.46, No.3, pp. 3387-3401, 2023, DOI:10.32604/csse.2023.029904

    Abstract The invention of Phasor Measurement Units (PMUs) produce synchronized phasor measurements with high resolution real time monitoring and control of power system in smart grids that make possible. PMUs are used in transmitting data to Phasor Data Concentrators (PDC) placed in control centers for monitoring purpose. A primary concern of system operators in control centers is maintaining safe and efficient operation of the power grid. This can be achieved by continuous monitoring of the PMU data that contains both normal and abnormal data. The normal data indicates the normal behavior of the grid whereas the abnormal data indicates fault or… More >

  • Open Access

    ARTICLE

    Analysis of Social Media Impact on Stock Price Movements Using Machine Learning Anomaly Detection

    Richard Cruz1, Johnson Kinyua1,*, Charles Mutigwe2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3405-3423, 2023, DOI:10.32604/iasc.2023.035906

    Abstract The massive increase in the volume of data generated by individuals on social media microblog platforms such as Twitter and Reddit every day offers researchers unique opportunities to analyze financial markets from new perspectives. The meme stock mania of 2021 brought together stock traders and investors that were also active on social media. This mania was in good part driven by retail investors’ discussions on investment strategies that occurred on social media platforms such as Reddit during the COVID-19 lockdowns. The stock trades by these retail investors were then executed using services like Robinhood. In this paper, machine learning models… More >

  • Open Access

    ARTICLE

    Ensemble Voting-Based Anomaly Detection for a Smart Grid Communication Infrastructure

    Hend Alshede1,2,*, Laila Nassef1, Nahed Alowidi1, Etimad Fadel1

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3257-3278, 2023, DOI:10.32604/iasc.2023.035874

    Abstract Advanced Metering Infrastructure (AMI) is the metering network of the smart grid that enables bidirectional communications between each consumer’s premises and the provider’s control center. The massive amount of data collected supports the real-time decision-making required for diverse applications. The communication infrastructure relies on different network types, including the Internet. This makes the infrastructure vulnerable to various attacks, which could compromise security or have devastating effects. However, traditional machine learning solutions cannot adapt to the increasing complexity and diversity of attacks. The objective of this paper is to develop an Anomaly Detection System (ADS) based on deep learning using the… More >

  • Open Access

    ARTICLE

    Segmentation Based Real Time Anomaly Detection and Tracking Model for Pedestrian Walkways

    B. Sophia1,*, D. Chitra2

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 2491-2504, 2023, DOI:10.32604/iasc.2023.029799

    Abstract Presently, video surveillance is commonly employed to ensure security in public places such as traffic signals, malls, railway stations, etc. A major challenge in video surveillance is the identification of anomalies that exist in it such as crimes, thefts, and so on. Besides, the anomaly detection in pedestrian walkways has gained significant attention among the computer vision communities to enhance pedestrian safety. The recent advances of Deep Learning (DL) models have received considerable attention in different processes such as object detection, image classification, etc. In this aspect, this article designs a new Panoptic Feature Pyramid Network based Anomaly Detection and… More >

  • Open Access

    ARTICLE

    Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

    Feisha Hu1, Qi Wang1,*, Haijian Shao1,2, Shang Gao1, Hualong Yu1

    CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2405-2424, 2023, DOI:10.32604/cmes.2023.026732

    Abstract Unmanned Aerial Vehicles (UAVs) are widely used and meet many demands in military and civilian fields. With the continuous enrichment and extensive expansion of application scenarios, the safety of UAVs is constantly being challenged. To address this challenge, we propose algorithms to detect anomalous data collected from drones to improve drone safety. We deployed a one-class kernel extreme learning machine (OCKELM) to detect anomalies in drone data. By default, OCKELM uses the radial basis (RBF) kernel function as the kernel function of the model. To improve the performance of OCKELM, we choose a Triangular Global Alignment Kernel (TGAK) instead of… More > Graphic Abstract

    Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Displaying 21-30 on page 3 of 101. Per Page