MULTI-HYPERVISOR-BASED AUTHORIZATION AND DOS ATTACK MITIGATION FRAMEWORK USING LC-WTRNN TECHNIQUE

doi:10.1016/j.iot.2025.101843

MULTI-HYPERVISOR-BASED AUTHORIZATION AND DOS ATTACK MITIGATION FRAMEWORK USING LC-WTRNN TECHNIQUE

2025-12-03

0

PRE

AI

K

Kalyan Gattupalli

P

Poovendran Alagarsundaram

H

Harikumar Nagarajan

V

Venkata Surya Bhavana Harish Gollavilli

S

Surendar Rama Sitaraman

R

R. Pushpakumar

DOI：10.1016/j.iot.2025.101843

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Hypervisors allow the management of several Virtual Machines (VMs) on a single device but are highly susceptible to DoS attacks, which deprive resources and disrupt cloud services. Techniques currently in use fail to establish proper authorization between multi-hypervisors, thereby exposing VMs to security threats. To ameliorate this situation, we developed a LeCun Wave Tanh Recurrent Neural Network (LC-WTRNN)-based multi-hypervisor authorization framework integrated with Hamming Code Quantum Cryptography (HC-QC), Kullback-Leibler De-Swinging K-Anonymity (KLDS-KAnonymity), and the Hell Bhatt Tiger Hashing Algorithm (HB-THA). Thereby, the system efficiently detects DoS attacks, secures VM registration, and ensures data integrity. With experimental results on the CICDDoS2019 dataset, it is seen that its method achieves an accuracy of 98.62%, a recall value of 98.45%, and a specificity of 98.65% on average, outperforming traditional RNN, DBN, RBM, and DNN methods by 5.3%. Additionally, the newly proposed framework contributes to a 56.1% reduction in the time needed for anonymization while providing 8.5% better encryption security and 44.5% less tree generation time against the traditional methods. These results thus validate LC-WTRNN as a scalable and secure solution to mitigating DoS attacks in cloud environments.

MULTI-HYPERVISOR-BASED AUTHORIZATION AND DOS ATTACK MITIGATION FRAMEWORK USING LC-WTRNN TECHNIQUE

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