Home / Journals / JCS / Vol.2, No.4, 2020
  • 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 clustering is used to obtain… More >

  • Open Access

    ARTICLE

    Excellent Practical Byzantine Fault Tolerance

    Huanrong Tang, Yaojing Sun, Jianquan Ouyang*
    Journal of Cyber Security, Vol.2, No.4, pp. 167-182, 2020, DOI:10.32604/jcs.2020.011341
    Abstract With the rapid development of blockchain technology, more and more people are paying attention to the consensus mechanism of blockchain. Practical Byzantine Fault Tolerance (PBFT), as the first efficient consensus algorithm solving the Byzantine Generals Problem, plays an important role. But PBFT also has its problems. First, it runs in a completely closed environment, and any node can't join or exit without rebooting the system. Second, the communication complexity in the network is as high as O(n2), which makes the algorithm only applicable to small-scale networks. For these problems, this paper proposes an Optimized consensus algorithm, Excellent Practical Byzantine Fault… More >

  • Open Access

    ARTICLE

    A Two-Stage Highly Robust Text Steganalysis Model

    Enlu Li1, Zhangjie Fu1,2,3,*, Siyu Chen1, Junfu Chen1
    Journal of Cyber Security, Vol.2, No.4, pp. 183-190, 2020, DOI:10.32604/jcs.2020.015010
    Abstract With the development of natural language processing, deep learning, and other technologies, text steganography is rapidly developing. However, adversarial attack methods have emerged that gives text steganography the ability to actively spoof steganalysis. If terrorists use the text steganography method to spread terrorist messages, it will greatly disturb social stability. Steganalysis methods, especially those for resisting adversarial attacks, need to be further improved. In this paper, we propose a two-stage highly robust model for text steganalysis. The proposed method analyzes and extracts anomalous features at both intra-sentential and inter-sentential levels. In the first phase, every sentence is first transformed into… More >

  • Open Access

    ARTICLE

    Improvement of the Firework Algorithm for Classification Problems

    Yu Xue, Sow Alpha Amadou*, Yan Zhao
    Journal of Cyber Security, Vol.2, No.4, pp. 191-196, 2020, DOI:10.32604/jcs.2020.014045
    Abstract Attracted numerous analysts’ consideration, classification is one of the primary issues in Machine learning. Numerous evolutionary algorithms (EAs) were utilized to improve their global search ability. In the previous years, many scientists have attempted to tackle this issue, yet regardless of the endeavors, there are still a few inadequacies. Based on solving the classification problem, this paper introduces a new optimization classification model, which can be applied to the majority of evolutionary computing (EC) techniques. Firework algorithm (FWA) is one of the EC methods, Although the Firework algorithm (FWA) is a proficient algorithm for solving complex optimization issue. The proficient… More >

  • Open Access

    REVIEW

    An Overview of Face Manipulation Detection

    Xingwang Ju*
    Journal of Cyber Security, Vol.2, No.4, pp. 197-207, 2020, DOI:10.32604/jcs.2020.014310
    Abstract Due to the power of editing tools, new types of fake faces are being created and synthesized, which has attracted great attention on social media. It is reasonable to acknowledge that one human cannot distinguish whether the face is manipulated from the real faces. Therefore, the detection of face manipulation becomes a critical issue in digital media forensics. This paper provides an overview of recent deep learning detection models for face manipulation. Some public dataset used for face manipulation detection is introduced. On this basis, the challenges for the research and the potential future directions are analyzed and discussed. More >

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