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D.E. Savitsky,M.E. Dunaev,K.S. Zaytsev
Pág. 70 - 76
The purpose of this work is to study methods for detecting anomalies in the processing of data streams in distributed streams in real time. To do this, the authors carried out a modification of the K-Means algorithm, called K-Means in real time, and carr...
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?.?. Yudova,Olga R. Laponina
Pág. 61 - 68
This article is devoted to the analysis of the possibility of detecting attacks on web applications using machine learning algorithms. Supervised learning is considered. A sample of HTTP DATASET CSIC 2010 is used as a data set. The dataset was automatica...
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Xiaosong Zhao, Lei Zhang, Yixin Cao, Kai Jin and Yupeng Hou
Anomaly detection problems in industrial control systems (ICSs) are always tackled by a network traffic monitoring scheme. However, traffic-based anomaly detection systems may be deceived by anomalous behaviors that mimic normal system activities and fai...
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Igor Chirkov,Maxim Dunaev
Pág. 36 - 42
The problem of software failures in the operation of complex corporate software systems is economically significant and, unfortunately, inevitable. Therefore, to solve this problem, it is necessary to predict failures in a timely manner, based on informa...
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Yufeng Yu, Dingsheng Wan, Qun Zhao and Huan Liu
Anomalous patterns are common phenomena in time series datasets. The presence of anomalous patterns in hydrological data may represent some anomalous hydrometeorological events that are significantly different from others and induce a bias in the decisio...
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