Gradient Boosting Feature Selection with Machine Learning Classifier for Prediction of Intrusion Image

The novel approach to intrusion image prediction explained in this paper uses both Gradient Boosting Feature Selection and Machine Learning Classifiers. Detection of intruders in images is a very essential in security systems to ensure detection of intrusion to prevent unauthorized access. To address this issue, the proposed method uses Gradient Boosting to rank the features of the image data and improve the effectiveness of the next ML classifier. The Artificial Neural Network technique is used for classification and prediction model. The analysis of simulation results proved the efficiency of proposed approach to enhance the accuracy and reliability of the intrusion prediction models.