Support Vector Machine, Naïve Baye’s, and Recurrent Neural Network to Detect Data Poisoning Attacks on Dataset

Abstract

Machine learning is a subset of artificial intelligence, that has revolutionalized the world in recent days. The machine learning model will be trained and tested on the collection of huge data, a large portion is for training and others for testing. data has been collected from trusted or untrusted sources, and results will be predicted using different algorithms. People are getting used to these predictions, are these predictions secure, trustful, or guaranteed? Well, that will be decided by the quality of the datasets. Although the datasets can be poisoned easily. Here the proposed work is providing a solution to check the quality of the dataset used for machine learning algorithms. The basis of the research is to detect poisoning attacks in the data set and to determine the most accurate estimate of the detection of poisoning attacks. comparing accuracies of the supervised machine learning algorithms Support Vector Machine(SVM), Naive Baye’s Classifier (NBC), and deep learning algorithms Recurrent Neural Network(RNN).The evaluation report was submitted for the results obtained using the rating report. The classification report considers performance measures the Precision, Accuracy, and F1 score of each algorithm.