India is accounting for almost 20 percent of total milk production in the world and 70 percent of this share is coming from small, marginal farmers and landless people of the country residing in rural areas and this shows that dairy industry has an important role in social and economic development in India. Dairy is growing with a positive rate as per capita availability has reached to 375 (gms/day) in 2017-18 from 178 (gms/day) in 1990-91. In this study, time series data (2001-02 to 2015-16) on milk production and different milching species population of Chhattisgarh have been used to find out the suitable forecasting models for milk production and population of these mulching animals of Chhattisgarh. To meet the objective of study different Autoregressive Integrated Moving Average (ARIMA) models have been tried and among all ARIMA (0,2,0) model has been found more suitable for production of milk in India and Chhattisgarh both. Availability of milk is forecasted suitably by ARIMA (0,2,1) and ARIMA(0,1,1) for India and Chhattisgarh respectively. Similarly different ARIMA models have been fitted for population of different species animals. By this study milk production is expected to reach 219.73 MMT and 1.599 MMT by 2022-23 in India and Chhattisgarh respectively.

Sugarcane plays an essential role in the economy of the India. During 2018, 79.9% of total sugarcane production of India was used in the manufacture of white sugar, 11.29% was used for jaggery production, and 8.80% was used as seed and feed materials. 840.16 Mt sugarcane was exported in the year 2019. Prediction of production level is basic to effective decision-making for policymakers. The objective of this study is thus to find the suitable models of forecasting for sugarcane production. India and major sugarcane producing states, namely Andhra Pradesh, Karnataka, Maharashtra, Tamil Nadu and Uttar Pradesh were selected. Sugarcane production data from 1950 to 2015 were used for training and 2016 to 2018 was used to test the model. ARIMA method was used to model the production process. Order selection was done using AIC. RMSE, MAPE and Theils' U statistic were used to test the accuracy of the models fitted to the data. ARCH process was found for Karnataka, Tamil Nadu and Uttar Pradesh. Autocorrelation was not present in all the data series analyzed. Forecast accuracy on MAPE criteria ranged from 0.046 to 0.197 percent.

Software-defined Networking (SDN) enables advanced network applications by separating a network into a data plane that forwards packets and a control plane that computes and installs forwarding rules into the data plane. Many SDN applications rely on dynamic rule installation, where the control plane processes the first few packets of each traffic flow and then installs a dynamically computed rule into the data plane to forward the remaining packets. Control plane processing adds delay, as the switch must forward each packet and meta-information to a (often centralized) control server and wait for a response specifying how to handle the packet. The amount of delay the control plane imposes depends on its load, and the applications and protocols it runs. In this work, we develop a non-intrusive timing attack that exploits this property to learn about a SDN network's configuration. The attack analyzes the amount of delay added to timing pings that are specially crafted to invoke the control plane, while transmitting other packets that may invoke the control plane, depending on the network's configuration. We show, in a testbed with physical OpenFlow switches and controllers, that an attacker can probe the network at a low rate for short periods of time to learn a bevy of sensitive information about networks with > 99% accuracy, including host communication patterns, ACL entries, and network monitoring settings. We also implement and test a practical defense: a timeout proxy, which normalizes control plane delay by providing configurable default responses to control plane requests that take too long. The proxy can be deployed on unmodified OpenFlow switches. It reduced the attack accuracy to below 50% in experiments, and can be configured to have minimal impact on non-attack traffic.Our work. In this paper, we develop a more sophisticated timingbased side channel attack that can be launched by an adversary with access to only a single machine on the target network. Using the attack, the adversary can learn many more details about a network configuration than reported in prior work, without initiating connections to other hosts in a network. Much of the revealed information would be considered highly sensitive, including host communication records, network access control configurations, and

Due to the impact of Corona virus (COVID-19) pandemic that exists today, all countries, national and international organizations are in a continuous effort to find efficient and accurate statistical models for forecasting the future pattern of COVID infection. Accurate forecasting should help governments to take decisive decisions to master the pandemic spread.  In this article, we explored the COVID-19 database of India between 17th March to 1st July 2020, then we estimated two nonlinear time series models: Artificial Neural Network (ANN) and Fuzzy Time Series (FTS) by comparing them with ARIMA model. In terms of model adequacy, the FTS model out performs the ANN for the new cases and new deaths time series in India. We observed a short-term virus spread trend according to three forecasting models.Such findings help in more efficient preparation for the Indian health system.