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Search Results (102)
  • Open Access

    ARTICLE

    An Anomaly Detection Method of Industrial Data Based on Stacking Integration

    Kunkun Wang1,2, Xianda Liu2,3,4,*

    Journal on Artificial Intelligence, Vol.3, No.1, pp. 9-19, 2021, DOI:10.32604/jai.2021.016706

    Abstract With the development of Internet technology, the computing power of data has increased, and the development of machine learning has become faster and faster. In the industrial production of industrial control systems, quality inspection and safety production of process products have always been our concern. Aiming at the low accuracy of anomaly detection in process data in industrial control system, this paper proposes an anomaly detection method based on stacking integration using the machine learning algorithm. Data are collected from the industrial site and processed by feature engineering. Principal component analysis (PCA) and integrated rule… More >

  • Open Access

    ARTICLE

    Traffic Anomaly Detection Method Based on Improved GRU and EFMS-Kmeans Clustering

    Yonghua Huo1, Yi Cao2, Zhihao Wang1, Yu Yan3, Zhongdi Ge3, Yang Yang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.3, pp. 1053-1091, 2021, DOI:10.32604/cmes.2021.013045

    Abstract In recent years, with the continuous development of information technology and the rapid growth of network scale, network monitoring and management become more and more important. Network traffic is an important part of network state. In order to ensure the normal operation of the network, improve the availability of the network, find network faults in time and deal with network attacks; it is necessary to detect the abnormal traffic in the network. Abnormal traffic detection is of great significance in the actual network management. Therefore, in order to improve the accuracy and efficiency of network… More >

  • Open Access

    ARTICLE

    Anomaly Detection in ICS Datasets with Machine Learning Algorithms

    Sinil Mubarak1, Mohamed Hadi Habaebi1,*, Md Rafiqul Islam1, Farah Diyana Abdul Rahman, Mohammad Tahir2

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 33-46, 2021, DOI:10.32604/csse.2021.014384

    Abstract An Intrusion Detection System (IDS) provides a front-line defense mechanism for the Industrial Control System (ICS) dedicated to keeping the process operations running continuously for 24 hours in a day and 7 days in a week. A well-known ICS is the Supervisory Control and Data Acquisition (SCADA) system. It supervises the physical process from sensor data and performs remote monitoring control and diagnostic functions in critical infrastructures. The ICS cyber threats are growing at an alarming rate on industrial automation applications. Detection techniques with machine learning algorithms on public datasets, suitable for intrusion detection of More >

  • Open Access

    ARTICLE

    A Generative Adversarial Networks for Log Anomaly Detection

    Xiaoyu Duan1, Shi Ying1,*, Wanli Yuan1, Hailong Cheng1, Xiang Yin2

    Computer Systems Science and Engineering, Vol.37, No.1, pp. 135-148, 2021, DOI:10.32604/csse.2021.014030

    Abstract Detecting anomaly logs is a great significance step for guarding system faults. Due to the uncertainty of abnormal log types, lack of real anomaly logs and accurately labeled log datasets. Existing technologies cannot be enough for detecting complex and various log point anomalies by using human-defined rules. We propose a log anomaly detection method based on Generative Adversarial Networks (GAN). This method uses the Encoder-Decoder framework based on Long Short-Term Memory (LSTM) network as the generator, takes the log keywords as the input of the encoder, and the decoder outputs the generated log template. The More >

  • Open Access

    ARTICLE

    Real-Time Anomaly Detection in Packaged Food X-Ray Images Using Supervised Learning

    Kangjik Kim1, Hyunbin Kim1, Junchul Chun1, Mingoo Kang2, Min Hong3,*, Byungseok Min4

    CMC-Computers, Materials & Continua, Vol.67, No.2, pp. 2547-2568, 2021, DOI:10.32604/cmc.2021.014642

    Abstract Physical contamination of food occurs when it comes into contact with foreign objects. Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking. Therefore, a preventive method that can detect and remove foreign objects in advance is required. Several studies have attempted to detect defective products using deep learning networks. Because it is difficult to obtain foreign object-containing food data from industry, most studies on industrial anomaly detection have used unsupervised learning methods. This paper proposes a new method… More >

  • Open Access

    ARTICLE

    Machine Learning Empowered Security Management and Quality of Service Provision in SDN-NFV Environment

    Shumaila Shahzadi1, Fahad Ahmad1,*, Asma Basharat1, Madallah Alruwaili2, Saad Alanazi2, Mamoona Humayun2, Muhammad Rizwan1, Shahid Naseem3

    CMC-Computers, Materials & Continua, Vol.66, No.3, pp. 2723-2749, 2021, DOI:10.32604/cmc.2021.014594

    Abstract With the rising demand for data access, network service providers face the challenge of growing their capital and operating costs while at the same time enhancing network capacity and meeting the increased demand for access. To increase efficacy of Software Defined Network (SDN) and Network Function Virtualization (NFV) framework, we need to eradicate network security configuration errors that may create vulnerabilities to affect overall efficiency, reduce network performance, and increase maintenance cost. The existing frameworks lack in security, and computer systems face few abnormalities, which prompts the need for different recognition and mitigation methods to… More >

  • Open Access

    ARTICLE

    Oversampling Methods Combined Clustering and Data Cleaning for Imbalanced Network Data

    Yang Yang1,*, Qian Zhao1, Linna Ruan2, Zhipeng Gao1, Yonghua Huo3, Xuesong Qiu1

    Intelligent Automation & Soft Computing, Vol.26, No.5, pp. 1139-1155, 2020, DOI:10.32604/iasc.2020.011705

    Abstract In network anomaly detection, network traffic data are often imbalanced, that is, certain classes of network traffic data have a large sample data volume while other classes have few, resulting in reduced overall network traffic anomaly detection on a minority class of samples. For imbalanced data, researchers have proposed the use of oversampling techniques to balance data sets; in particular, an oversampling method called the SMOTE provides a simple and effective solution for balancing data sets. However, current oversampling methods suffer from the generation of noisy samples and poor information quality. Hence, this study proposes More >

  • Open Access

    ARTICLE

    Large-Scale KPI Anomaly Detection Based on Ensemble Learning and Clustering

    Ji Qian1, Fang Liu2,*, Donghui Li3, Xin Jin4, Feng Li4

    Journal of Cyber Security, Vol.2, No.4, pp. 157-166, 2020, DOI:10.32604/jcs.2020.011169

    Abstract Anomaly detection using KPI (Key Performance Indicator) is critical for Internet-based services to maintain high service availability. However, given the velocity, volume, and diversified nature of monitoring data, it is difficult to obtain enough labelled data to build an accurate anomaly detection model for using supervised machine leaning methods. In this paper, we propose an automatic and generic transfer learning strategy: Detecting anomalies on a new KPI by using pretrained model on existing selected labelled KPI. Our approach, called KADT (KPI Anomaly Detection based on Transfer Learning), integrates KPI clustering and model pretrained techniques. KPI More >

  • Open Access

    ARTICLE

    A Stacking-Based Deep Neural Network Approach for Effective Network Anomaly Detection

    Lewis Nkenyereye1, Bayu Adhi Tama2, Sunghoon Lim3,*

    CMC-Computers, Materials & Continua, Vol.66, No.2, pp. 2217-2227, 2021, DOI:10.32604/cmc.2020.012432

    Abstract An anomaly-based intrusion detection system (A-IDS) provides a critical aspect in a modern computing infrastructure since new types of attacks can be discovered. It prevalently utilizes several machine learning algorithms (ML) for detecting and classifying network traffic. To date, lots of algorithms have been proposed to improve the detection performance of A-IDS, either using individual or ensemble learners. In particular, ensemble learners have shown remarkable performance over individual learners in many applications, including in cybersecurity domain. However, most existing works still suffer from unsatisfactory results due to improper ensemble design. The aim of this study More >

  • Open Access

    ARTICLE

    The Design and Implementation of a Multidimensional and Hierarchical Web Anomaly Detection System

    Jianfeng Guan*, Jiawei Li, Zhongbai Jiang

    Intelligent Automation & Soft Computing, Vol.25, No.1, pp. 131-141, 2019, DOI:10.31209/2018.100000050

    Abstract The traditional web anomaly detection systems face the challenges derived from the constantly evolving of the web malicious attacks, which therefore result in high false positive rate, poor adaptability, easy over-fitting, and high time complexity. Due to these limitations, we need a new anomaly detection system to satisfy the requirements of enterprise-level anomaly detection. There are lots of anomaly detection systems designed for different application domains. However, as for web anomaly detection, it has to describe the network accessing behaviours characters from as many dimensions as possible to improve the performance. In this paper we… More >

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