Auto-Regressive Moving Average Technique Research Articles

Towards Securing OpenFlow Controllers for SDNs using ARMA Models

Control layers are moved away from the forwarding switching layers in Software Defined Networks. SDNs allow more programmability and flexibility to the controllers. OpenFlow is a protocol that gives access to the forwarding plane of a network switch or router over the SDN network. OpenFlow uses a centralized control of network switches and routers in SDN environment. Security is of a major importance for SDN deployment. Transport Layer Security (TLS) is be used to implement security for OpenFlow. This paper proposes a new technique to improve the security of the OpenFlow controller through modifying the TLS implementation. The proposed model is referred to as Secured Feedback model using Autoregressive Moving Average (ARMA) for SDN networks (SFBARMASDN). SFBARMASDN depends on computing the feedback for incoming packets based on ARMA models. Filtering techniques based on ARMA techniques are used to filter the packets and detect malicious packets to be dropped. SFBARMASDN is compared to two reference models. One reference model is based on Bayesian and the other reference model is the standard OpenFlow. Results are very promising. SFBARMASDN has outperformed both the secured standard using Bayesian network for SDN (SSBNSDN) and the standard OpenFlow in different scenarios by an average improvement of 7% and 80% respectively. The processing time overhead for the SFBARMASDN increases by only a percentage of 3% and 5% when compared to the SSBNSDN and the standard OpenFlow respectively.

Feedback ARMA Models versus Bayesian Models towards Securing OpenFlow Controllers for SDNs

In software-defined networking (SDN), the control layers are moved away from the forwarding switching layers. SDN gives more programmability and flexibility to the controllers. OpenFlow is a protocol that gives access to the forwarding plane of a network switch or router over the SDN network. OpenFlow uses a centralized control of network switches and routers in and SDN environment. Security is of major importance for SDN deployment. Transport layer security (TLS) is used to implement security for OpenFlow. This paper proposed a new technique to improve the security of the OpenFlow controller through modifying the TLS implementation. The proposed model is referred to as the secured feedback model using autoregressive moving average (ARMA) for SDN networks (SFBARMASDN). SFBARMASDN depended on computing the feedback for incoming packets based on ARMA models. Filtering techniques based on ARMA techniques were used to filter the packets and detect malicious packets that needed to be dropped. SFBARMASDN was compared to two reference models. One reference model was Bayesian-based and the other reference model was the standard OpenFlow.

Long-term analysis of thunderstorm-related parameters over Visakhapatnam and Machilipatnam, India

In this present work, an attempt has been made to analyze various thunderstorm-related parameters and their influence over the two stations Visakhapatnam (VSK) and Machilipatnam (MTM). The thunderstorm-related parameters used in the present study are convective available potential energy (CAPE), lifted index, K-index, total totals index (TTI), humidity index, convective inhibition, thunderstorm prediction index (TPI), deep convective index (DCI) and updraft vertical velocity. This analysis was carried out using NCEP NCAR reanalysis monthly data for the time period from 1948 to 2012. These parameters have given good guidance for studying the thunderstorm event. We also analyzed IMD thunderstorm occurrence days reported at two stations, i.e., VSK and MTM with NCEP NCAR (daily data) calculated CAPE, TTI, TPI and DCI parameter threshold days in pre-monsoon season for every year during the time period 2010 to 2019. Out of those four parameters, TTI has shown good correlation with the IMD recorded days. So we have attempted the prediction of thunderstorms using artificial neural network (ANN) and auto-regressive moving average (ARMA) techniques for TTI parameter. While using these techniques, we have experimented in three training sets, i.e., 90%, 80% and 70%. Another attempt has been made to assess the skill of ARMA and ANN techniques in forecasting the occurrence of thunderstorm activity at VSK and MTM stations. The present study suggests that ANN has high skill than ARMA. From this study, we can understand that VSK has more chances for thunderstorms than MTM.

Predictive time‐series modeling using artificial neural networks for Linac beam symmetry: an empirical study

Over half of cancer patients receive radiotherapy (RT) as partial or full cancer treatment. Daily quality assurance (QA) of RT in cancer treatment closely monitors the performance of the medical linear accelerator (Linac) and is critical for continuous improvement of patient safety and quality of care. Cumulative longitudinal QA measurements are valuable for understanding the behavior of the Linac and allow physicists to identify trends in the output and take preventive actions. In this study, artificial neural networks (ANNs) and autoregressive moving average (ARMA) time-series prediction modeling techniques were both applied to 5-year daily Linac QA data. Verification tests and other evaluations were then performed for all models. Preliminary results showed that ANN time-series predictive modeling has more advantages over ARMA techniques for accurate and effective applicability in the dosimetry and QA field.

Modelling and analysis of turbulent datasets using Auto Regressive Moving Average processes

We introduce a novel way to extract information from turbulent datasets by applying an Auto Regressive Moving Average (ARMA) statistical analysis. Such analysis goes well beyond the analysis of the mean flow and of the fluctuations and links the behavior of the recorded time series to a discrete version of a stochastic differential equation which is able to describe the correlation structure in the dataset. We introduce a new index Υ that measures the difference between the resulting analysis and the Obukhov model of turbulence, the simplest stochastic model reproducing both Richardson law and the Kolmogorov spectrum. We test the method on datasets measured in a von Kármán swirling flow experiment. We found that the ARMA analysis is well correlated with spatial structures of the flow, and can discriminate between two different flows with comparable mean velocities, obtained by changing the forcing. Moreover, we show that the Υ is highest in regions where shear layer vortices are present, thereby establishing a link between deviations from the Kolmogorov model and coherent structures. These deviations are consistent with the ones observed by computing the Hurst exponents for the same time series. We show that some salient features of the analysis are preserved when considering global instead of local observables. Finally, we analyze flow configurations with multistability features where the ARMA technique is efficient in discriminating different stability branches of the system.

Autoregressive (AR) and autoregressive moving average (ARMA) spectral estimation techniques for faster TLM analysis of microwave structures

Autoregressive (AR) and autoregressive moving average (ARMA) techniques have been successfully implemented in conjunction with the transmission line matrix (TLM) method for efficient time-domain analysis of microwave structures. The AR technique can be used to compute the full time-domain response from a relatively short segment of the early TLM response. It was found that the least-square technique of estimating the AR parameters requires a shorter time record than solving Yule-Walker equations through the Levinson-Durbin algorithm. The ARMA technique can be used to compute the scattering parameters of microwave structures without using the discrete Fourier transform. A recursive least square covariance ladder algorithm has been used for ARMA modeling. Both AR and ARMA models have been validated by applying them to waveguide and suspended substrate stripline filters. With these techniques, the speed of the computationally intensive TLM algorithm can be increased up to five times. >

The use of time-series analysis to forecast bont tick (Amblyomma hebraeum) infestations in Zimbabwe.

Studying the dynamics of tick infestations on cattle is an essential step in developing optimal strategies for tick control. Successful strategic tick control requires accurate predictions of when tick infestations will reach predetermined threshold levels. In the case of Amblyomma hebraeum, earlier work has shown that there is no consistent pattern of seasonal activity. This means that a statistical model for predicting A. hebraeum infestations cannot reliably use climatic factors as the only independent variables. An alternative method is to apply time-series, or auto-regressive moving-average (ARMA), analysis which uses only the past population patterns to predict future trends. This technique was applied to a data set consisting of 108 weekly tick counts of A. hebraeum (adult males, standard females, flat females and standard nymphs), conducted at an experimental station in southeastern Zimbabwe. The ability of the ARMA models to fit and predict actual tick infestations was judged using two sets of criteria. The first set focused on the goodness-of-fit, and used the adjusted R2 values, Q statistic and the Akaike Information Criteria. The second set of criteria measured the forecasting accuracy of an estimated equation, and consisted of regressing a 9-period forecast against an actual out-of-sample data set not used in the estimation process. The root mean square error of the forecast was also considered when comparing several models for the same data set. Using these criteria, the models estimated using the ARMA technique were judged to both fit and forecast with sufficient accuracy to warrant their use in strategic tick control. Although the success of using ARMA to forecast A. hebraeum is partly due to the non-seasonal behavior of the species, the results presented here suggest that it is worthwhile exploring the use of ARMA techniques to model the dynamics of other tick species. Where independent variables exert considerable influence on the dynamics of a tick species, these variables can be incorporated into an ARMA-style model.