Method, device, and computer program product for abnormality detection

#This application claims the priority, under 35 U.S.C. § 119, of Chinese Patent Application No. 202010790880.2, filed Aug. 7, 2020, which is incorporated by reference in its entirety.

#The present disclosure generally relates to the field of artificial intelligence (AI), and more particularly, to a method, an apparatus, a device, and a computer program product for abnormality detection.

#Currently, many applications are deployed in public computing environments, such as in public clouds. Users may initiate requests to applications via client terminals to obtain corresponding services. Such deployment may provide services to more users. However, in public computing environments, applications may face various attacks, which may lead to data theft, data leakage, malicious data deletion or modification, etc. Injection hijacking and token hijacking are common attack means. Therefore, application security is a very important issue.

#The above and other objectives, features, and advantages of the embodiments of the present disclosure will become readily understandable by reading the following detailed description with reference to the accompanying drawings. In the accompanying drawings, several embodiments of the present disclosure are shown by way of example instead of limitation, where:

#FIG. 1 shows an example environment in which embodiments of the present disclosure can be implemented;

#FIG. 2 shows a block diagram of a system for abnormality detection according to some embodiments of the present disclosure;

#Hereinafter, preferred implementations of the present disclosure will be described in more detail with reference to the accompanying drawings. Although the preferred implementations of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure may be implemented in various forms and should not be limited by the implementations set forth herein. Rather, these implementations are provided so that the present disclosure will be more thorough and complete, and the scope of the present disclosure will be fully conveyed to those skilled in the art.

#As used herein, the term “include” and variations thereof mean open-ended inclusion, that is, “including but not limited to.” Unless specifically stated, the term “or” indicates “and/or.” The term “based on” means “based at least in part on.” The terms “one example implementation” and “one implementation” mean “at least one example implementation.” The term “another implementation” means “at least one further implementation.” The terms “first,” “second,” and the like may refer to different or identical objects. Other explicit and implicit definitions may also be included below.

#As used herein, the term “model” may learn a correlation between corresponding inputs and outputs from training data, so that after training is completed, a corresponding output may be generated for a given input. Therefore, the trained model may be considered as being able to characterize this correlation between inputs and outputs.