A new pre-processed DL-DT anomaly detection model in cloud computing

Cloud computing is prone to security issues due to its multi-tenant and volatile environment.Nowadays, intrusion detection systems including anomaly detection play a significant role in maintaining thesecurity of cloud computing systems. Anomaly detection is a prominent task for maintaining the performanceof cloud platform. Among anomaly detection algorithms, machine learning algorithms have received muchattention. In this paper, we propose a new pre-processed deep learning-decision tree model for anomalydetection in cloud environment. The KDD CUP dataset is used for evaluating algorithms. What depicted fromexperiments claims that the proposed model is shown to be a proper algorithm and reached better results suchas ۹۹.۶۳, ۰.۰۰۶ and ۰.۰۵۹ in accuracy, MAE and RMSE respectively.

کلیدواژه ها:

Anomaly Detection ، Cloud Computing ، Deep Learning ، Decision Tree ، Machine Learning