One of the major security issues in Cloud computing is to detect malicious activities at the network layer. In this paper, we propose a framework integrating network intrusion detection system (NIDS) in the Cloud. Our NIDS module consists of Snort and signature apriori algorithm. It generates new rules from captured packets. These new rules are appended in the Snort configuration file to improve efficiency of Snort. It aims to detect known attacks and derivative of known attacks in Cloud by monitoring network traffic, while ensuring low false positive rate with reasonable computational cost. We also recommend the positioning of NIDS in Cloud. We present experimental setup and discuss the design goals expected from proposed framework

The consumer’s short-term load forecasting plays an essential role in microgrid energy distribution. However, the load forecasting at consumer level is more challenging than at substation level due to high volatility and uncertainty in energy consumption. In literature, many machine learning-based forecasting models have been explored. However, there is a need of developing a robust and accurate model to handle highly inconsistent energy consumption. In this article, we propose a robust and accurate model for consumer’s short-term load forecasting, which uses feasible techniques such as random forest, support vector regressor, and long short-term memory as base predictors to handle varying traits of energy consumption. For the final decision-making on forecasting result of these predictors, it assigns weights to each predictor dynamically as per the forecasting efficacy. The proposed model is tested on different consumer’s varying traits of energy consumption. The experimental results show that the proposed model achieves forecasting error reduction by 3.46 and 2.53 in terms of average RMSE and MAE, respectively, in comparison with the existing models. It is robust and accurate even in presence of highly volatile and uncertain load patterns, and thus, it can better fit for microgrid energy management.