Methods and systems for anomaly detection
This disclosure relates generally to anomaly detection, and more particularly to system and method for detecting anomalies. In one embodiment, the method includes executing at least one thread associated with the application. Executing the at least one thread results in invoking one or more methods associated with the at least one thread. During the execution metrics associated with the one or more methods are captured. The metrics are systematically arranged in a data structure to represent a plurality of thread-method pairs and the metrics corresponding to each of the plurality of thread-method pairs. One or more anomalies associated with the one or more methods are identified from the data structure based on a detection of at least one predetermined condition in the data structure. An anomaly of the one or more anomalies includes one of un-exited anomaly, an exception anomaly and a user-defined anomaly.
1. A processor-implemented method for anomaly detection in an application, the method comprising
systematically executing at least one thread associated with the application, wherein executing the at least one thread results in invoking one or more methods associated with the at least one thread, wherein the at least one thread is systematically executed upon establishing a monitoring session with a server that runs the application;
periodically capturing, during the execution, metrics associated with the one or more methods during the monitoring session, wherein the metrics are generated during systematic execution of the application in runtime environment, wherein the metrics are periodically captured using a default auto-generated standard configuration;
systematically arranging the metrics in a data structure to represent a plurality of thread-method pairs and the metrics corresponding to each of the plurality of thread-method pairs; and
identifying, from the data structure, one or more anomalies associated with the one or more methods based on a detection of at least one predetermined condition associated with the metrics in the data structure, wherein an anomaly of the one or more anomalies comprises one of unexited anomaly, an exception anomaly and a user-defined anomaly.
2. The method of claim 1 , wherein the metrics associated with the one or more methods comprises method name, method invocation timestamp, method exit timestamp, method stack-trace, exception name, exception cause, and exception timestamp.
3. The method of claim 1 , wherein identifying the un-exited anomaly in the one or more methods comprises detecting absence of the method exit timestamp in the metrics associated with the one or more methods.
4. The method of claim 1 , wherein identifying the exception anomaly in the one or more methods comprises detecting exception name, exception cause, and exception timestamp in the metrics associated with the one or more methods.
5. The method of claim 1 , wherein the at least one predetermined condition is defined by a user to identify the user-defined anomaly in the method.
6. The method of claim 1 , wherein the application is a distributed application having multiple layers, the distributed application comprising one or more of at least one web interface, at least one database component and at least one application interface.
7. A system for anomaly detection in an application comprising:
at least one memory; and
one or more hardware processors, the at least one memory coupled to the one or more hardware processors wherein the one or more hardware processors are capable of executing programmed instructions stored in the at least one memory to:
systematically execute at least one thread associated with the application, wherein executing the at least one thread results in invoking one or more methods associated with the at least one thread, wherein the at least one thread is systematically executed upon establishing a monitoring session with a server that runs the application;
periodically capture, during the execution, metrics associated with the one or more methods during the monitoring session, wherein the metrics are generated during systematic execution of the application in runtime environment, wherein the metrics are periodically captured using a default auto-generated standard configuration;
systematically arrange the metrics in a data structure to represent a plurality of thread-method pairs and the metrics corresponding to each of the plurality of thread-method pairs; and
identify, from the data structure, one or more anomalies associated with the one or more methods based on a detection of at least one predetermined condition associated with the metrics in the data structure, wherein an anomaly of the one or more anomalies comprises one of un-exited anomaly and exception anomaly and a user-defined anomaly.
8. The system of claim 7 , wherein the metrics associated with the one or more methods comprises method name, method invocation timestamp, method exit timestamp, method stack-trace, exception name, exception cause, and exception timestamp.
9. The system of claim 7 , wherein the at least one processor is capable of executing programmed instructions to identify the un-exited anomaly in the one or more methods comprises detecting absence of the method exit timestamp in the metrics associated with the one or more methods.
10. The system of claim 7 , wherein at least one processor is capable of executing programmed instructions to identify the exception anomaly in the one or more methods comprises detecting exception name, exception cause, and exception timestamp in the metrics associated with the one or more methods.
11. The system of claim 7 , wherein the at least one predetermined condition is defined by a user to identify the user-defined anomaly in the method.
12. The system of claim 7 , wherein the application is a distributed application having multiple layers, the multiple layers comprising one or more of at least one web interface, at least one database component and at least one application interface.
13. A non-transitory computer-readable medium having embodied thereon a computer program for executing a method for anomaly detection, the method comprising:
systematically executing at least one thread associated with the application, wherein executing the at least one thread results in invoking one or more methods associated with the at least one thread, wherein the at least one thread is systematically executed upon establishing a monitoring session with a server that runs the application;
periodically capturing, during the execution, metrics associated with the one or more methods during the monitoring session, wherein the metrics are generated during systematic execution of the application in runtime environment, wherein the metrics are periodically captured using a default auto-generated standard configuration;
systematically arranging the metrics in a data structure to represent a plurality of thread-method pairs and the metrics corresponding to each of the plurality of thread-method pairs; and
identifying, from the data structure, one or more anomalies associated with the one or more methods based on a detection of at least one predetermined condition associated with the metrics in the data structure, wherein an anomaly of the one or more anomalies comprises one of unexited anomaly, an exception anomaly and a user-defined anomaly.