Cloud Zero-Day Attack Detection Using Hidden Markov Model with Transductive Learning

Abstract

In cloud security, detecting attack software is considered an essential task. Among several attack types, a zero-day attack is considered as most problematic because the antivirus cannot able to remove it. The existing attack detection model uses stored data about attack characteristics, which fails to detect zero-attack where an altered attack is implemented for an antivirus system to detect the attack. To detect and prevent zero-day attacks, this paper proposed a model stated as Hidden Markov Model Transductive Deep Learning (HMM_TDL), which generates hyper alerts when an attack is implemented. Also, the HMM_TDL assigns labels to data in the network and periodically updates the database (DB). Initially, the HMM model detects the attacks with hyper alerts in the database. In the next stage, transductive deep learning incorporates k-medoids for clustering attacks and assign labels. Finally, the trust value of the original data is computed and computed in the database based on the value network able to classify attacks and data. The developed HMM_TDL is trained with consideration of two datasets such as NSL-KDD and CIDD. The comparative analysis of HMM_TDL exhibits a higher accuracy value of 95% than existing attack classification techniques.