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  • 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

    Ensemble Learning Based on GBDT and CNN for Adoptability Prediction

    Yunfan Ye1, Fang Liu1, *, Shan Zhao2, Wanting Hu3, Zhiyao Liang4

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1361-1372, 2020, DOI:10.32604/cmc.2020.011632

    Abstract By efficiently and accurately predicting the adoptability of pets, shelters and rescuers can be positively guided on improving attraction of pet profiles, reducing animal suffering and euthanization. Previous prediction methods usually only used a single type of content for training. However, many pets contain not only textual content, but also images. To make full use of textual and visual information, this paper proposed a novel method to process pets that contain multimodal information. We employed several CNN (Convolutional Neural Network) based models and other methods to extract features from images and texts to obtain the More >

  • Open Access

    ARTICLE

    Ensemble Strategy for Insider Threat Detection from User Activity Logs

    Shihong Zou1, Huizhong Sun1, *, Guosheng Xu1, Ruijie Quan2

    CMC-Computers, Materials & Continua, Vol.65, No.2, pp. 1321-1334, 2020, DOI:10.32604/cmc.2020.09649

    Abstract In the information era, the core business and confidential information of enterprises/organizations is stored in information systems. However, certain malicious inside network users exist hidden inside the organization; these users intentionally or unintentionally misuse the privileges of the organization to obtain sensitive information from the company. The existing approaches on insider threat detection mostly focus on monitoring, detecting, and preventing any malicious behavior generated by users within an organization’s system while ignoring the imbalanced ground-truth insider threat data impact on security. To this end, to be able to detect insider threats more effectively, a data… 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 >

  • Open Access

    ARTICLE

    Multi-Level Feature-Based Ensemble Model for Target-Related Stance Detection

    Shi Li1, Xinyan Cao1, *, Yiting Nan2

    CMC-Computers, Materials & Continua, Vol.65, No.1, pp. 777-788, 2020, DOI:10.32604/cmc.2020.010870

    Abstract Stance detection is the task of attitude identification toward a standpoint. Previous work of stance detection has focused on feature extraction but ignored the fact that irrelevant features exist as noise during higher-level abstracting. Moreover, because the target is not always mentioned in the text, most methods have ignored target information. In order to solve these problems, we propose a neural network ensemble method that combines the timing dependence bases on long short-term memory (LSTM) and the excellent extracting performance of convolutional neural networks (CNNs). The method can obtain multi-level features that consider both local More >

  • Open Access

    ARTICLE

    KAEA: A Novel Three-Stage Ensemble Model for Software Defect Prediction

    Nana Zhang1, Kun Zhu1, Shi Ying1, *, Xu Wang2

    CMC-Computers, Materials & Continua, Vol.64, No.1, pp. 471-499, 2020, DOI:10.32604/cmc.2020.010117

    Abstract Software defect prediction is a research hotspot in the field of software engineering. However, due to the limitations of current machine learning algorithms, we can’t achieve good effect for defect prediction by only using machine learning algorithms. In previous studies, some researchers used extreme learning machine (ELM) to conduct defect prediction. However, the initial weights and biases of the ELM are determined randomly, which reduces the prediction performance of ELM. Motivated by the idea of search based software engineering, we propose a novel software defect prediction model named KAEA based on kernel principal component analysis… More >

  • Open Access

    ARTICLE

    Geophysical and Production Data History Matching Based on Ensemble Smoother with Multiple Data Assimilation

    Zelong Wang1, 2, 3, *, Xiangui Liu1, 2, 3, Haifa Tang3, Zhikai Lv3, Qunming Liu3

    CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.2, pp. 873-893, 2020, DOI:10.32604/cmes.2020.08993

    Abstract The Ensemble Kalman Filter (EnKF), as the most popular sequential data assimilation algorithm for history matching, has the intrinsic problem of high computational cost and the potential inconsistency of state variables updated at each loop of data assimilation and its corresponding reservoir simulated result. This problem forbids the reservoir engineers to make the best use of the 4D seismic data, which provides valuable information about the fluid change inside the reservoir. Moreover, only matching the production data in the past is not enough to accurately forecast the future, and the development plan based on the… More >

  • Open Access

    ARTICLE

    Ground Nephogram Recognition Algorithm Based on Selective Neural Network Ensemble

    Tao Li1, Xiang Li1, *, Yongjun Ren2, Jinyue Xia3

    CMC-Computers, Materials & Continua, Vol.63, No.2, pp. 621-631, 2020, DOI:10.32604/cmc.2020.06463

    Abstract In view of the low accuracy of traditional ground nephogram recognition model, the authors put forward a k-means algorithm-acquired neural network ensemble method, which takes BP neural network ensemble model as the basis, uses k-means algorithm to choose the individual neural networks with partial diversities for integration, and builds the cloud form classification model. Through simulation experiments on ground nephogram samples, the results show that the algorithm proposed in the article can effectively improve the Classification accuracy of ground nephogram recognition in comparison with applying single BP neural network and traditional BP AdaBoost ensemble algorithm on More >

  • Open Access

    ARTICLE

    Classification and Research of Skin Lesions Based on Machine Learning

    Jian Liu1, Wantao Wang1, Jie Chen2, *, Guozhong Sun3, Alan Yang4

    CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1187-1200, 2020, DOI:10.32604/cmc.2020.05883

    Abstract Classification of skin lesions is a complex identification challenge. Due to the wide variety of skin lesions, doctors need to spend a lot of time and effort to judge the lesion image which zoomed through the dermatoscopy. The diagnosis which the algorithm of identifying pathological images assists doctors gets more and more attention. With the development of deep learning, the field of image recognition has made longterm progress. The effect of recognizing images through convolutional neural network models is better than traditional image recognition technology. In this work, we try to classify seven kinds of More >

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