Process Monitoring System Based on Anomaly Detection Statistics Algorithm

2014 ◽  
Vol 513-517 ◽  
pp. 408-411
Author(s):  
Li Juan Zhang
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Process Monitoring ◽  
System Security ◽  
Detection Algorithm ◽  
Source Information ◽  
Security Threat ◽  
Threat Model ◽  
System Process ◽  
Local Computer

In order to find more efficient and handle the internal network threat with the LAN network threat warning and real-time processing, remote process monitoring and management of computer systems is used in this paper. By getting the system process handle on the local computer, realizes the acquisition of the threat source information of the system. By customization of the application layer protocol AMCP, realizes the efficient information transmission between server and client. In order to enhance the reliability of the system security threat model, the System security threat information is analysised with the anomaly detection algorithm based on statistics. Analysis of the test data shows that: through getting the system process handle on the local computer, the system treating information can be obtained. Through the security threat model based the anomaly detection algorithm based on statistics, network threat is dealed efficiently and real time.

scholarly journals Multivariate-Time-Series-Driven Real-time Anomaly Detection Based on Bayesian Network

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3367 ◽  
Author(s):  
Nan Ding ◽  
Huanbo Gao ◽  
Hongyu Bu ◽  
Haoxuan Ma ◽  
Huaiwei Si
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Bayesian Network ◽  
Real Time ◽  
Multivariate Time Series ◽  
Research Direction ◽  
Detection Algorithm ◽  
The Real ◽  
Hierarchical Temporal Memory ◽  
Sensing Time

Anomaly detection is an important research direction, which takes the real-time information system from different sensors and conditional information sources into consideration. Based on this, we can detect possible anomalies expected of the devices and components. One of the challenges is anomaly detection in multivariate-sensing time-series in this paper. Based on this situation, we propose RADM, a real-time anomaly detection algorithm based on Hierarchical Temporal Memory (HTM) and Bayesian Network (BN). First of all, we use HTM model to evaluate the real-time anomalies of each univariate-sensing time-series. Secondly, a model of anomalous state detection in multivariate-sensing time-series based on Naive Bayesian is designed to analyze the validity of the above time-series. Lastly, considering the real-time monitoring cases of the system states of terminal nodes in Cloud Platform, the effectiveness of the methodology is demonstrated using a simulated example. Extensive simulation results show that using RADM in multivariate-sensing time-series is able to detect more abnormal, and thus can remarkably improve the performance of real-time anomaly detection.

Dynamically Real-time Anomaly Detection Algorithm with Immune Negative Selection

2013 ◽  
Vol 7 (3) ◽  
pp. 1157-1163 ◽  
Author(s):  
Lingxi Peng ◽  
Wenbin Chen ◽  
Dongqing Xie ◽  
Ying Gao ◽  
Chunlin Liang
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Negative Selection ◽  
Detection Algorithm

Big Data Management System Security Threat Model

2019 ◽  
Vol 53 (8) ◽  
pp. 903-913
Author(s):  
M. A. Poltavtseva ◽  
D. P. Zegzhda ◽  
M. O. Kalinin
Keyword(s):  
Big Data ◽  
Data Management ◽  
Management System ◽  
System Security ◽  
Data Management System ◽  
Security Threat ◽  
Threat Model

scholarly journals Performance Comparison of Anomaly Detection Algorithms for Streaming Data

2020 ◽  
Author(s):  
Zirije Hasani ◽  
Jakup Fondaj
Keyword(s):  
Anomaly Detection ◽  
Real Time ◽  
Time Series Data ◽  
Scale Up ◽  
Detection Algorithm ◽  
Streaming Data ◽  
Series Data ◽  
Process Data ◽  
Significant Information ◽  
Detection Algorithms

Abstract Most of the today's world data are streaming, time-series data, where anomalies detection gives significant information of possible critical situations. Yet, detecting anomalies in big streaming data is a difficult task, requiring detectors to acquire and process data in a real-time, as they occur, even before they are stored and instantly alarm on potential threats. Suitable to the need for real-time alarm and unsupervised procedures for massive streaming data anomaly detection, algorithms have to be robust, with low processing time, eventually at the cost of the accuracy. In this work we compare the performance of our proposed anomaly detection algorithm HW-GA[1] with other existing methods as ARIMA [10], Moving Average [11] and Holt Winters [12]. The algorithms are tested and results are visualized in the system R, on the three Numenta datasets, with known anomalies and own e-dnevnik dataset with unknown anomalies. Evaluation is done by comparing achieved results (the algorithm execution time and CPU usage). Our interest is monitoring of the streaming log data that are generating in the national educational network (e-dnevnik) that acquires a massive number of online queries and to detect anomalies in order to scale up performance, prevent network downs, alarm on possible attacks and similar.

Sign in / Sign up

Export Citation Format

Share Document