Cloud security based attack detection using transductive learning integrated with Hidden Markov Model

doi:10.1016/j.patrec.2022.02.012

Cloud security based attack detection using transductive learning integrated with Hidden Markov Model

2022-05-01

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DOI：10.1016/j.patrec.2022.02.012

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In recent years, organizations and enterprises put huge attention on their network security. The attackers were able to influence vulnerabilities for the configuration of the network through the network. Zero day (0-day) is defined as vulnerable software or application that is either defined by the vendor or not patched by any vendor of organization. When zero-day attack is identified within the network there is no proper mechanism when observed. To mitigate challenges related to the zero-day attack, this paper presented HMM_TDL, a deep learning model for detection and prevention of attack in the cloud platform. The presented model is carried out in three phases like at first, Hidden Markov Model (HMM) is incorporated for the detection of attacks. With the derived HMM model, hyper alerts are transmitted to the database for attack prevention. In the second stage, a transductive deep learning model with k-medoids clustering is adopted for attack identification. With k-medoids clustering, soft labels are assigned for attack and data and update to the database. In the last phase, with computed HMM_TDL database is updated with computed trust value for attack prevention within the cloud. (c) 2022 Published by Elsevier B.V.

Keywords:

Zero-day attack security

Transductive deep learning

Hidden Markov Model (HMM)

Soft labels

K-medoids clustering

Cloud security based attack detection using transductive learning integrated with Hidden Markov Model

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