Moumita Pramanik

TL;DR: An improved version of the random sampling mechanism called Supervised Relative Random Sampling has been proposed to generate a balanced sample from a high-class imbalanced dataset at the detector’s pre-processing stage, and an improved multi-class feature selection mechanism has been designed and developed as a filter component to generate the IDS datasets’ ideal outstanding features for efficient intrusion detection.

TL;DR: The current literature status in the field of network intrusion detection is analyzed, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps and a robust classifier is proposed as the ideal classifier for designing IDSs.

TL;DR: Among the three proposed detection schemes the Forest by Penalizing Attributes (ForestPA) proved to be a promising Parkinson’s disease detector with a little number of decision trees in the forest to score the highest detection accuracy.

TL;DR: In this article , a hybrid of Decision Table and Naive Bayes models were combined to train and detect intrusions, which achieved an accuracy of 96.8% using five features of CICIDS2017, which is higher than the accuracy of methods discussed in the literatures.

TL;DR: In this paper, a machine learning approach for Parkinson's disease detection is presented, where multiple acoustic signal features of Parkinson's and control subjects are ascertained through correlated feature selection, Fisher score feature selection and mutual information-based feature selection.