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Abstract

With the continuous progress of network technology, some abnormal data are often confused in network data flow, which affects network security. In order to grasp the abnormal degree of abnormal data in networks and detect the similarity of abnormal data, an optimized genetic data mining algorithm is used to mine abnormal data in network, obtain the initial population of abnormal data mining and optimize genetic operation. On this basis, the network data type and the number of network data types are adaptively adjusted to obtain the optimal abnormal data mining results. Based on Euclidean distance, the similarity value of abnormal data in network is calculated, and the greater the similarity value is, the greater the similarity of abnormal data is and vice versa. The experimental results show that the average standard deviation of detection error and energy consumption of the proposed method are 0.00865 and 398J, respectively. This method is a reliable and energy-saving method for similarity detection of abnormal data in network, which provides an effective basis for grasping the anomaly degree of network data.

Keywords

Data mining abnormal data in network population optimized genetics similarity detection

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References

Assadhan ,

Zeb ,

Al-Muhtadi and

Alshebeili , Anomaly detection based on lrd behavior analysis of decomposed control and data planes network traffic using soss and farima models, IEEE Access 1(1) (2007).

Bandyopadhyay ,

Wolfson ,

D.M.

Vock , et al., Data mining for censored time-to-event data: A bayesian network model for predicting cardiovascular risk from electronic health record data, Data Mining and Knowledge Discovery 29(4) (2015), 1033–1069.

Deng ,

Tan ,

He , et al., An immune network classification algorithm based on dynamic neighborhood recognition and its performance analysis, Journal of Electronics and Information 37(5) (2015), 1167–1172.

Dong and

Peng , Anomalous data mining algorithms simulation in complex network data stream, Computer Simulation 33(1) (2016), 434–437.

Kaur and

Singh , A survey of data mining and social network analysis based anomaly detection techniques, Egyptian Informatics Journal 17(2) (2016), 199–216.

Li ,

Guo and

Yang , Fault detection method based on time series data mining, Data Acquisition and Processing 31(4) (2016), 782–790.

K. Naveen

Kumar ,

Surendranath and

Mahesh Kumar , Electrical appliances control and automation using Wi-Fi, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(10) (2017).

Li ,

Zhang and

Chen , Layered computation of sentence similarity for user query intention, Computer Science 42(1) (2015), 227–231.

Lin ,

Hao ,

Tang , et al., Research on short-term load forecasting method of buildings based on data mining, Power System Protection and Control 44(7) (2016), 83–89.

10.

Luo ,

Ding ,

Fu , et al., Identification of abnormal P2P network loan based on “multi-level classification” method, Journal of Management Engineering 31(3) (2017), 201–209.

11.

Ma ,

Wan ,

Ma , et al., Abnormal traffic identification method based on bag-of-words model clustering, Computer Engineering 43(5) (2017), 204–209.

12.

Peng ,

Yang ,

Xu , et al., Outlier detection method based on ranking and clustering in dual-type heterogeneous networks, Journal of Electronics 46(2) (2018), 281–288.

13.

Suryakumar and Dr.

Ramadevi , An algorithmic study of link prediction in social network sites, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(8) (2017).

14.

Qiu ,

He ,

Yang , et al., Review of key technologies for early warning and prediction of abnormal trajectory data, Journal of Systems Simulation 29(11) (2017), 2608–2617.

15.

Wang ,

Wang , et al., Research on incomplete data filling method based on tuple similarity, Computer Science 44(2) (2017), 98–102.

16.

Wang ,

Zhang and

Chen , A collaborative filtering algorithm based on Dice-Euclidean similarity calculation, Application Research of Computers 32(10) (2015), 2891–2895.

17.

Wang ,

Cheng and

Ma , Summary of threat behavior detection technology based on alert association, Journal of National Defense University of Science and Technology 39(5) (2017), 128–138.

18.

Gayathri and Dr.

A.S.

Thanamani , A survey on datamining based intrusion detection system, International Journal of Innovative Research in Computer and Communication Engineering (IJIRCCE) 5(9) (2017).

19.

Xiong ,

Liu and

Li , Research on the combination model of network intrusion classification and diagnosis based on neighborhood rough sets, Practice and Understanding of Mathematics 47(10) (2017), 145–151.

20.

Yin and

Sun , Parallel implementation improved k-means applied for image retrieval and anomaly detection, Multimedia Tools & Applications 76(16) (2016), 1–17.

21.

Zhang ,

Chen and

Li , A method for calculating the similarity of Chinese text in PST_LDA, Computer Applied Research 33(2) (2016), 375–377.

22.

Paul ,

Ahmad ,

M.M.

Rathore and

Jabbar , Smartbuddy: Defining human behaviors using big data analytics in social internet of things, IEEE Wireless Communications 23(5) (2016), 68–74.

23.

Zhou and

Xiong , Network state anomaly detection based on data mining, Journal of Jilin University: Science Edition 55(5) (2017), 1269–1273.

Similarity detection method of abnormal data in network based on data mining

Abstract

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