An anomaly detection method based on bqp network

Abstract

The invention belongs to the technical field of deep learning, and specifically relates to a BQP network-based anomaly detection method, comprising the following steps: S1, preset anomaly detection image training data sets; S2, build a BQP network, and set parameters; S3, for each The batches are sent to the training batches of the BQP network, and the features in the image are extracted using the feature extraction network in the BQP network, and the output is the batch feature vector X with a size of B×n; S4, the QP output layer in the BQP network Construct a feature hypersphere in the middle, and the QP output layer outputs the optimal dual variable; S5, through the classification loss function and the consistency loss function, respectively calculate the loss function for the feature vector X output by the feature extraction network and the optimal dual variable of the QP output layer, And optimize the parameters of the BQP network through the backpropagation algorithm; S6, during detection, compare the modulus length of the feature vector output by the feature extraction network with the set threshold to realize anomaly detection.

Description

technical field [0001] The invention belongs to the technical field of deep learning, and in particular relates to an abnormality detection method based on a BQP network. Background technique [0002] With the explosive growth of data in recent years, people's demand for intelligent analysis of data is getting higher and higher. Among them, anomaly detection technology, as a branch of machine learning, has played an important role in intelligent analysis of data. For example, in massive video surveillance data, through computer anomaly detection technology, it is possible to more accurately locate the time period of abnormal events, which greatly reduces labor costs. [0003] Anomaly detection is the determination of samples other than well-defined or identified normal samples, which is a special detection problem. Anomaly detection involves machine learning, data mining, mathematical statistics, information theory and other related knowledge, widely used in intrusion detec...

AI Technical Summary

Problems solved by technology

[0004] The complexity and difficulty of anomaly detection lie in the following aspects: First, different from traditional classification problems, in anomaly detection problems, the number of samples of normal events is far more than the number of samples of abnormal events, and the difference between normal and abnormal training data is There is a serious imbalance in the number of

Secondly, the number of abnormal samples in a training set is limited, but it may contain multiple types. At the same time, the abnormal samples in this training set still cannot exhaustively contain all abnormal situations, so it is difficult to find a suitable model to describe The data

Moreover, due to the noise of the data itself or the noise introduced by the system, it is often difficult to identify noise and anomalies, which brings a lot of uncertainty to the anomaly detection problem.

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