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  • Open Access

    ARTICLE

    State-Based Control Feature Extraction for Effective Anomaly Detection in Process Industries

    Ming Wan1, Jinfang Li1, Jiangyuan Yao2, *, Rongbing Wang1, 3, Hao Luo1

    CMC-Computers, Materials & Continua, Vol.63, No.3, pp. 1415-1431, 2020, DOI:10.32604/cmc.2020.09692

    Abstract In process industries, the characteristics of industrial activities focus on the integrality and continuity of production process, which can contribute to excavating the appropriate features for industrial anomaly detection. From this perspective, this paper proposes a novel state-based control feature extraction approach, which regards the finite control operations as different states. Furthermore, the procedure of state transition can adequately express the change of successive control operations, and the statistical information between different states can be used to calculate the feature values. Additionally, OCSVM (One Class Support Vector Machine) and BPNN (BP Neural Network), which are optimized by PSO (Particle Swarm… More >

  • Open Access

    ARTICLE

    Unsupervised Anomaly Detection via DBSCAN for KPIs Jitters in Network Managements

    Haiwen Chen1, Guang Yu1, Fang Liu2, Zhiping Cai1, *, Anfeng Liu3, Shuhui Chen1, Hongbin Huang1, Chak Fong Cheang4

    CMC-Computers, Materials & Continua, Vol.62, No.2, pp. 917-927, 2020, DOI:10.32604/cmc.2020.05981

    Abstract For many Internet companies, a huge amount of KPIs (e.g., server CPU usage, network usage, business monitoring data) will be generated every day. How to closely monitor various KPIs, and then quickly and accurately detect anomalies in such huge data for troubleshooting and recovering business is a great challenge, especially for unlabeled data. The generated KPIs can be detected by supervised learning with labeled data, but the current problem is that most KPIs are unlabeled. That is a time-consuming and laborious work to label anomaly for company engineers. Build an unsupervised model to detect unlabeled data is an urgent need… More >

  • Open Access

    ARTICLE

    A Convolution-Based System for Malicious URLs Detection

    Chaochao Luo1, Shen Su2, *, Yanbin Sun2, Qingji Tan3, Meng Han4, Zhihong Tian2, *

    CMC-Computers, Materials & Continua, Vol.62, No.1, pp. 399-411, 2020, DOI:10.32604/cmc.2020.06507

    Abstract Since the web service is essential in daily lives, cyber security becomes more and more important in this digital world. Malicious Uniform Resource Locator (URL) is a common and serious threat to cybersecurity. It hosts unsolicited content and lure unsuspecting users to become victim of scams, such as theft of private information, monetary loss, and malware installation. Thus, it is imperative to detect such threats. However, traditional approaches for malicious URLs detection that based on the blacklists are easy to be bypassed and lack the ability to detect newly generated malicious URLs. In this paper, we propose a novel malicious… More >

  • Open Access

    ARTICLE

    A Novel Probabilistic Hybrid Model to Detect Anomaly in Smart Homes

    Sasan Saqaeeyan1, Hamid Haj Seyyed Javadi1,2,*, Hossein Amirkhani1,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.121, No.3, pp. 815-834, 2019, DOI:10.32604/cmes.2019.07848

    Abstract Anomaly detection in smart homes provides support to enhance the health and safety of people who live alone. Compared to the previous studies done on this topic, less attention has been given to hybrid methods. This paper presents a two-steps hybrid probabilistic anomaly detection model in the smart home. First, it employs various algorithms with different characteristics to detect anomalies from sensory data. Then, it aggregates their results using a Bayesian network. In this Bayesian network, abnormal events are detected through calculating the probability of abnormality given anomaly detection results of base methods. Experimental evaluation of a real dataset indicates… More >

  • Open Access

    ARTICLE

    Long Short Term Memory Networks Based Anomaly Detection for KPIs

    Haiqi Zhu1, Fanzhi Meng2,*, Seungmin Rho3, Mohan Li4,*, Jianyu Wang1, Shaohui Liu1, Feng Jiang1

    CMC-Computers, Materials & Continua, Vol.61, No.2, pp. 829-847, 2019, DOI:10.32604/cmc.2019.06115

    Abstract In real-world many internet-based service companies need to closely monitor large amounts of data in order to ensure stable operation of their business. However, anomaly detection for these data with various patterns and data quality has been a great challenge, especially without labels. In this paper, we adopt an anomaly detection algorithm based on Long Short-Term Memory (LSTM) Network in terms of reconstructing KPIs and predicting KPIs. They use the reconstruction error and prediction error respectively as the criteria for judging anomalies, and we test our method with real data from a company in the insurance industry and achieved good… More >

  • Open Access

    ARTICLE

    A Hybrid Model for Anomalies Detection in AMI System Combining K-means Clustering and Deep Neural Network

    Assia Maamar1,*, Khelifa Benahmed2

    CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 15-39, 2019, DOI:10.32604/cmc.2019.06497

    Abstract Recently, the radical digital transformation has deeply affected the traditional electricity grid and transformed it into an intelligent network (smart grid). This mutation is based on the progressive development of advanced technologies: advanced metering infrastructure (AMI) and smart meter which play a crucial role in the development of smart grid. AMI technologies have a promising potential in terms of improvement in energy efficiency, better demand management, and reduction in electricity costs. However the possibility of hacking smart meters and electricity theft is still among the most significant challenges facing electricity companies. In this regard, we propose a hybrid approach to… More >

  • Open Access

    ARTICLE

    YATA: Yet Another Proposal for Traffic Analysis and Anomaly Detection

    Yu Wang1,2,*, Yan Cao2, Liancheng Zhang2, Hongtao Zhang3, Roxana Ohriniuc4, Guodong Wang5, Ruosi Cheng6

    CMC-Computers, Materials & Continua, Vol.60, No.3, pp. 1171-1187, 2019, DOI:10.32604/cmc.2019.05575

    Abstract Network traffic anomaly detection has gained considerable attention over the years in many areas of great importance. Traditional methods used for detecting anomalies produce quantitative results derived from multi-source information. This makes it difficult for administrators to comprehend and deal with the underlying situations. This study proposes another method to yet determine traffic anomaly (YATA), based on the cloud model. YATA adopts forward and backward cloud transformation algorithms to fuse the quantitative value of acquisitions into the qualitative concept of anomaly degree. This method achieves rapid and direct perspective of network traffic. Experimental results with standard dataset indicate that using… More >

  • Open Access

    ARTICLE

    Key Process Protection of High Dimensional Process Data in Complex Production

    He Shi1,2,3,4, Wenli Shang1,2,3,4,*, Chunyu Chen1,2,3,4, Jianming Zhao1,2,3,4, Long Yin1, 2, 3, 4

    CMC-Computers, Materials & Continua, Vol.60, No.2, pp. 645-658, 2019, DOI:10.32604/cmc.2019.05648

    Abstract In order to solve the problem of locating and protecting key processes and detecting outliers efficiently in complex industrial processes. An anomaly detection system which is based on the two-layer model fusion frame is designed in this paper. The key process is located by using the random forest model firstly, then the process data feature selection, dimension reduction and noise reduction are processed. Finally, the validity of the model is verified by simulation experiments. It is shown that this method can effectively reduce the prediction accuracy variance and improve the generalization ability of the traditional anomaly detection model from the… More >

  • Open Access

    ARTICLE

    Using Imbalanced Triangle Synthetic Data for Machine Learning Anomaly Detection

    Menghua Luo1,2, Ke Wang1, Zhiping Cai1,*, Anfeng Liu3, Yangyang Li4, Chak Fong Cheang5

    CMC-Computers, Materials & Continua, Vol.58, No.1, pp. 15-26, 2019, DOI:10.32604/cmc.2019.03708

    Abstract The extreme imbalanced data problem is the core issue in anomaly detection. The amount of abnormal data is so small that we cannot get adequate information to analyze it. The mainstream methods focus on taking fully advantages of the normal data, of which the discrimination method is that the data not belonging to normal data distribution is the anomaly. From the view of data science, we concentrate on the abnormal data and generate artificial abnormal samples by machine learning method. In this kind of technologies, Synthetic Minority Over-sampling Technique and its improved algorithms are representative milestones, which generate synthetic examples… More >

  • Open Access

    ARTICLE

    Event-Based Anomaly Detection for Non-Public Industrial Communication Protocols in SDN-Based Control Systems

    Ming Wan1, Jiangyuan Yao2,*, Yuan Jing1, Xi Jin3,4

    CMC-Computers, Materials & Continua, Vol.55, No.3, pp. 447-463, 2018, DOI: 10.3970/cmc.2018.02195

    Abstract As the main communication mediums in industrial control networks, industrial communication protocols are always vulnerable to extreme exploitations, and it is very difficult to take protective measures due to their serious privacy. Based on the SDN (Software Defined Network) technology, this paper proposes a novel event-based anomaly detection approach to identify misbehaviors using non-public industrial communication protocols, and this approach can be installed in SDN switches as a security software appliance in SDN-based control systems. Furthermore, aiming at the unknown protocol specification and message format, this approach first restructures the industrial communication sessions and merges the payloads from industrial communication… More >

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