Anomaly Detection via Rain Optimization Algorithm and Stacked Autoencoder Hoeffding Tree

Abstract

Networks of Computer are vulnerable to cyberattacks which could affect the mission critical data accessibility, confidentiality, integrity. Anomaly detection became the most basic environment of study because of extend usage range like unusual network traffic manner detection, detection of disease in MRI images, detection of fraud in transactions of credit card. In a lot of real-life anomaly issues of detection, we meet the heterogeneous data including various features’ kinds such as categorical and continuous features. Data heterogeneity makes that actually hard for data examples’ comparison. In addition, data manners might shift over time in flowing areas. At last, that is difficult foe getting data tags as we get a lot of data every day for classification manually. Autoencoders are a feed-forward neural network kind which could be ordered for performing anomaly detection through learning the stochastic input ‘normal’ instances’ representation and abnormal instances’ diagnosis through controlling error of reconstruction in comparison with the predetermined anomaly threshold. This paper concentrates on developing IDS effectiveness by applying proposed Stacked Autoencoder Hoeffding Tree approach (SAE-HT) applying Rain Optimization Algorithm (ROA) for selection of feature. Experiments on the dataset NSL-KDD illustrate that our model multi-classification possesses great performance. In comparison to the other mechanisms of ML in accuracy case, our model performs better than such mechanisms.