A system and method are disclosed for a non-invasive scheme for application recognition using packet processing. The system and method determine the type of application based on meta-information about the packet flows, rather than on the contents of the packets. An embodiment method includes monitoring and storing, by a processor, direction values, timing values and size values of a sequence of packets for each of a plurality of application protocol types. The direction values are discrete, and the timing and size values are continuous. The method further includes training a hidden Markov model (HMM) for each of the application protocol types using a HMM training algorithm on the direction, timing and size values.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A method for non-invasive application recognition comprising: obtain, by a processor, a plurality of parameters observed for a sequence of packets for each of a plurality of application protocol types, wherein the parameters include a discrete value parameter and continuous value parameters; training a plurality of hidden Markov models (HMMs) corresponding to the application protocol types using training data including the parameters observed for the sequence of packets; obtain a plurality of values for the parameters observed for a new sequence of packets for an unknown application protocol type; applying the values to each of the trained HMMs; computing an estimated likelihood that the unknown application protocol type is a respective application protocol type associated with each one of the trained HMMs; and classifying the unknown application protocol type as one of the application protocol types corresponding to one of the trained HMMs for which a maximum estimated likelihood is computed. 2. The method of claim 1, wherein the HMMs are trained using a HMM training algorithm on the training data comprising, for the sequence of packets for each of the application protocol types, one or more discrete bits for representing the discrete value parameter, and further comprising a vector of continuous variables for representing the continuous value parameters. 3. The method of claim 1, wherein the discrete value parameter indicates a direction of the packets, and wherein the continuous value parameters indicate a timing and a size of the packets. 4. The method of claim 1, wherein each one of the HMMs comprises a finite state machine including probabilities of transitioning between a plurality of states, and an output distribution including probabilities of observing a specific output in a specific state. 5. The method of claim 4, wherein, for each one of the states, the HMMs provide an output divided into a number of discrete bits (d) for representing the discrete value parameter, and a plurality of additional bits (c) that determine Gaussian parameters for representing the continuous value parameters, and wherein the HMMs comprise an output probability distribution matrix (B) comprising a number of columns equal to 2d+c. 6. The method of claim 5, wherein the HMMs calculate a probability (Pr) of a particular output (x) in a particular state (i) as Pr[xd,xc|si]=Σ2cxd<k≦2(x+1)BikN(xc,μk,Σk), where N is a multivariate normal distribution function, μk is a mean of N, and Σk is a variance of N. 7. The method of claim 1, wherein the continuous value parameters are Gaussian distribution parameters including a mean and a variance for determining a Gaussian distribution function for each one of the continuous value parameters. 8. The method of claim 1 further comprising evaluating a Key Quality Indicator (KQI) for the new sequence of packets in accordance with classifying the unknown application protocol type as one of the application protocol types, wherein evaluating the KQI for the packets includes determining at least one of delay and bitrate of the packets. 9. The method of claim 1, wherein the unknown application protocol type is classified without analyzing content of the new sequence of packets. 10. The method of claim 1, wherein the processor is located at a user equipment (UE) or a network end component. 11. A method for non-invasive application recognition comprising: monitoring and storing, by a processor, direction values, timing values and size values of a sequence of packets for each of a plurality of application protocol types, wherein the direction values are discrete, and wherein the timing and size values are continuous; and training a hidden Markov model (HMM) for each of the application protocol types using a HMM training algorithm on the direction, timing and size values. 12. The method of claim 11, wherein each HMM comprises a finite state machine including probabilities of transitioning between states, and an output distribution including probabilities of observing a specific output in a specific state. 13. The method of claim 11, further comprising, after the monitoring, storing and training: monitoring, by the processor, new direction values, new timing values and new size values of a new sequence of packets for an unknown application protocol type; applying the new direction values, timing values and size values to each of the trained HMMs; computing an estimated likelihood that the unknown application protocol type is a respective application protocol type associated with each trained HMMs; and classifying the unknown application protocol type as a specific application protocol type in accordance with a maximum one of the estimated likelihoods. 14. The method of claim 11, wherein the HMM training algorithm comprises a one discrete bit for representing the direction values, and further comprises a predefined number of additional bits the discrete value parameter and further comprising a predefined number of additional bits representing the continuous value parameters. 15. An apparatus for non-invasive application recognition comprising: at least one processor; a non-transitory computer readable storage medium storing programming for execution by the at least one processor, the programming including instructions to: obtain a plurality of parameters observed for a sequence of packets for each of a plurality of application protocol types, wherein the parameters include a discrete value parameter and continuous value parameters; train a plurality of hidden Markov models (HMMs) corresponding to the application protocol types using training data including the parameters; obtain a plurality of values for the parameters observed for a new sequence of packets for an unknown application protocol type; apply the values to each of the trained HMMs; compute an estimated likelihood that the unknown application protocol type is a respective application protocol type associated with each one of the trained HMMs; and classify the unknown application protocol type as one of the application protocol types corresponding to one of the trained HMMs for which a maximum estimated likelihood is computed. 16. The apparatus of claim 15, wherein the HMMs are trained using a HMM training algorithm on the training data comprising, for each sequence of packets for each of the application protocol types, one or more discrete bits for representing the discrete value parameter, and further comprising a vector of continuous variables for representing the continuous value parameters. 17. The apparatus of claim 15, wherein the discrete value parameter indicates a direction of the packets, and wherein the continuous value parameters indicate a timing and a size of the packets. 18. The apparatus of claim 15, wherein each one of the HMMs comprises a finite state machine including probabilities of transitioning between states, and an output distribution including probabilities of observing a specific output in a specific state. 19. The apparatus of claim 15, wherein the continuous value parameters are Gaussian distribution parameters including a mean and a variance for determining a multivariate Gaussian distribution function for the continuous value parameters. 20. The apparatus of claim 15, wherein the apparatus corresponds to a user equipment (UE) or a network end component.