Sequence abnormity detection method of telecommunication phone fraud based on sliding time window aggregation

Abstract

The invention discloses the sequence abnormity detection method of a telecommunication phone fraud based on sliding time window aggregation and belongs to the data mining, machine learning and business intelligence field. The method comprises the following steps of firstly, constructing a training user data set, and recalling all the call records of a called user to form a call sequence of each called user; using a cos similarity function, calculating a sequence structure similarity, counting a characteristic similarity and carrying out linearly combination to obtain an additivity similarity;then, through a K-Means clustering model, acquiring a K-class user so as to form an independent sequence training data set, and forming K training sets through a sliding time window; and finally, training an iForest model on each training set to obtain K abnormal detection models. Each called user identifies an abnormity through a corresponding abnormity detection model. When a maximum value is higher than a threshold h, the called user is a high-risk called user. The K-Means model and the abnormity detection model are updated every fixed period of time. In the invention, a data sparsity problem is alleviated and an abnormal characteristic based on a group is discovered.

Description

technical field [0001] The invention relates to a sequence anomaly detection method of telecommunications fraud calls based on sliding time window aggregation, and belongs to the fields of data mining, machine learning and business intelligence. Background technique [0002] In recent years, telecom fraud cases have occurred frequently in our country, threatening the property safety of the people and the stability of the society. Therefore, how to use methods such as classification and anomaly detection in data mining to realize the identification and detection of high-risk callees has important practical significance for the supervision department and the whole society. [0003] Since such anomalies are difficult to capture from static cross-sectional data, they can only be considered from the perspective of sequences. However, for the problem of anomaly detection in telecommunications call sequences, we first need to face the problem of data sparsity: especially on the in...

AI Technical Summary

Problems solved by technology

[0003] Since such anomalies are difficult to capture from static cross-sectional data, they can only be considered from the perspective of sequences

However, for the problem of anomaly detection in telecommunications call sequences, we first need to face the problem of data sparseness: especially on the international call side, the average number of calls for most calls within a month is less than 5 times

Such short call sequences are difficult to directly process using traditional methods

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