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  • Open Access


    Applying Stack Bidirectional LSTM Model to Intrusion Detection

    Ziyong Ran1, Desheng Zheng1, *, Yanling Lai1, Lulu Tian2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 309-320, 2020, DOI:10.32604/cmc.2020.010102

    Abstract Nowadays, Internet has become an indispensable part of daily life and is used in many fields. Due to the large amount of Internet traffic, computers are subject to various security threats, which may cause serious economic losses and even endanger national security. It is hoped that an effective security method can systematically classify intrusion data in order to avoid leakage of important data or misuse of data. As machine learning technology matures, deep learning is widely used in various industries. Combining deep learning with network security and intrusion detection is the current trend. In this paper, the problem of data… More >

  • Open Access


    Performance Anomaly Detection in Web Services: An RNN- Based Approach Using Dynamic Quality of Service Features

    Muhammad Hasnain1, Seung Ryul Jeong2, *, Muhammad Fermi Pasha3, Imran Ghani4

    CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 729-752, 2020, DOI:10.32604/cmc.2020.010394

    Abstract Performance anomaly detection is the process of identifying occurrences that do not conform to expected behavior or correlate with other incidents or events in time series data. Anomaly detection has been applied to areas such as fraud detection, intrusion detection systems, and network systems. In this paper, we propose an anomaly detection framework that uses dynamic features of quality of service that are collected in a simulated setup. Three variants of recurrent neural networks-SimpleRNN, long short term memory, and gated recurrent unit are evaluated. The results reveal that the proposed method effectively detects anomalies in web services with high accuracy.… More >

  • Open Access


    3-Dimensional Bag of Visual Words Framework on Action Recognition

    Shiqi Wang1, Yimin Yang1, *, Ruizhong Wei1, Qingming Jonathan Wu2

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1081-1091, 2020, DOI:10.32604/cmc.2020.09648

    Abstract Human motion recognition plays a crucial role in the video analysis framework. However, a given video may contain a variety of noises, such as an unstable background and redundant actions, that are completely different from the key actions. These noises pose a great challenge to human motion recognition. To solve this problem, we propose a new method based on the 3-Dimensional (3D) Bag of Visual Words (BoVW) framework. Our method includes two parts: The first part is the video action feature extractor, which can identify key actions by analyzing action features. In the video action encoder, by analyzing the action… More >

  • Open Access


    OTT Messages Modeling and Classification Based on Recurrent Neural Networks

    Guangyong Yang1, Jianqiu Zeng1, Mengke Yang2, *, Yifei Wei3, Xiangqing Wang3, Zulfiqar Hussain Pathan4

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 769-785, 2020, DOI:10.32604/cmc.2020.07528

    Abstract A vast amount of information has been produced in recent years, which brings a huge challenge to information management. The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages. In this paper, we propose a nine-rectangle-grid information model according to the information value and privacy, and then present information use policies based on the rough set theory. Recurrent neural networks were employed to classify OTT messages. The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages, ending with a reliable trained classification model.… More >

  • Open Access


    Smartphone User Authentication Based on Holding Position and Touch-Typing Biometrics

    Yu Sun1,2,*, Qiyuan Gao3, Xiaofan Du3, Zhao Gu3

    CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1365-1375, 2019, DOI:10.32604/cmc.2019.06294

    Abstract In this advanced age, when smart phones are the norm, people utilize social networking, online shopping, and even private information storage through smart phones. As a result, identity authentication has become the most critical security activity in this period of the intelligent craze. By analyzing the shortcomings of the existing authentication methods, this paper proposes an identity authentication method based on the behavior of smartphone users. Firstly, the sensor data and touch-screen data of the smart phone users are collected through android programming. Secondly, the eigenvalues of this data are extracted and sent to the server. Thirdly, the Support Vector… More >

  • Open Access


    Ensemble Recurrent Neural Network-Based Residual Useful Life Prognostics of Aircraft Engines

    Jun Wu1,*, Kui Hu1, Yiwei Cheng2, Ji Wang1, Chao Deng2,*, Yuanhan Wang3

    Structural Durability & Health Monitoring, Vol.13, No.3, pp. 317-329, 2019, DOI:10.32604/sdhm.2019.05571

    Abstract Residual useful life (RUL) prediction is a key issue for improving efficiency of aircraft engines and reducing their maintenance cost. Owing to various failure mechanism and operating environment, the application of classical models in RUL prediction of aircraft engines is fairly difficult. In this study, a novel RUL prognostics method based on using ensemble recurrent neural network to process massive sensor data is proposed. First of all, sensor data obtained from the aircraft engines are preprocessed to eliminate singular values, reduce random fluctuation and preserve degradation trend of the raw sensor data. Secondly, three kinds of recurrent neural networks (RNN),… More >

  • Open Access


    Remaining Useful Life Prediction of Rolling Bearings Based on Recurrent Neural Network

    Yimeng Zhai1, Aidong Deng1,*, Jing Li1,2, Qiang Cheng1, Wei Ren3

    Journal on Artificial Intelligence, Vol.1, No.1, pp. 19-27, 2019, DOI:10.32604/jai.2019.05817

    Abstract In order to acquire the degradation state of rolling bearings and achieve predictive maintenance, this paper proposed a novel Remaining Useful Life (RUL) prediction of rolling bearings based on Long Short Term Memory (LSTM) neural net-work. The method is divided into two parts: feature extraction and RUL prediction. Firstly, a large number of features are extracted from the original vibration signal. After correlation analysis, the features that can better reflect the degradation trend of rolling bearings are selected as input of prediction model. In the part of RUL prediction, LSTM that making full use of the network’s memory in time… More >

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