Feature Selection has been a significant preprocessing procedure for classification in the area of Supervised Machine Learning. It is mostly applied when the attribute set is very large. The large set of attributes often tend to misguide the classifier. Extensive research has been performed to increase the efficacy of the predictor by finding the optimal set of features. The feature subset should be such that it enhances the classification accuracy by the removal of redundant features. We propose a new feature selection mechanism, an amalgamation of the filter and the wrapper techniques by taking into consideration the benefits of both the methods. Our hybrid model is based on a two phase process where we rank the features and then choose the best subset of features based on the ranking. We validated our model with various datasets, using multiple evaluation metrics. Furthermore, we have also compared and analyzed our results with previous works. The proposed model outperformed many existent algorithms and has given us good results.

Feature selection has emerged as a craft, using which we boost the performance of our learning model. Feature or Attribute Selection is a data preprocessing technique, where only the most informative features are considered and given to the predictor. This reduces the computational overhead and improves the correctness of the classifier. Attribute Selection is commonly carried out by applying some filter or by using the performance of the learning model to gauge the quality of the attribute subset. Metric Ranked Feature Inclusion and Accuracy Ranked Feature Inclusion are the two novel hybrid feature selection methods we introduce in this paper. These algorithms follow a two-stage procedure, the first of which is feature ranking, followed by feature subset selection. They differ in the way they rank the features but follow the same subset selection technique. Multiple experiments have been conducted to assess our models. We compare our results with numerous works of the past and validate our models using 12 datasets. From the results, we infer that our algorithms perform better than many existent models.

Industrial Automation and Control Systems (IACS) are broadly utilized in critical infrastructures for monitoring and controlling the industrial processes remotely. The real-time transmissions in such systems provoke security breaches. Many security breaches have been reported impacting society severely. Hence, it is essential to achieve secure communication between the devices for creating a secure environment. For this to be effective, the keys used for secure communication must be protected against unauthorized disclosure, misuse, alteration or loss, which can be taken care of by a Key Management Infrastructure. In this paper, by considering the generic industrial automation network, a comprehensive key management infrastructure (CKMI) is designed for IACS. To design such an infrastructure, the proposed scheme employs ECDH, matrix method, and polynomial crypto mechanisms. The proposed design handles all the standard key management operations, viz. key generation, device registration, key establishment, key storage, device addition, key revocation, key update, key recovery, key archival, and key de-registration and destruction. The design supports secure communication between the same and different levels of IACS devices. The proposed design can be applied for major industrial automation networks to handle the key management operations. The performance analysis and implementation results highlight the benefits of the proposed design.