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<p>Ransomware attacks widely happen in cloud services and API’s. However, the existing studies didn’t set the limits and privacy for the API keys, which may lead to data mishandling. Therefore, this paper presents a privacy-preserved API access control policy-based RA detection in cloud VMs using SL<sup>2</sup>STM. Initially, the VM setup is done by VM registration and API gateway privacy preservation. Afterward, cloud user registration and data selection for upload are performed. At this point, the data is secured. Next, the access policy is created by using Saturated Bell-Fuzzy (SB-Fuzzy). Subsequently, the signature is generated by DSA. Afterward, access policy verification is performed. If both access policies are matched, then RA detection is done; otherwise, a notification is sent to the user. The RA detection system is trained based on pre-processing, feature extraction, feature selection, and classification. Here, the SL<sup>2</sup>STM classifier detects whether the cloud VMs are attacked or not. If attacked, then the access policy is created again; otherwise, the data is stored in VMs. The outcomes showcase that the proposed methodology obtained a high accuracy of 98.21%, which outperformed existing methods.</p>

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Privacy preserved api access control policy- based ransomware attack detection in cloud vms using sl2stm

  • 期刊论文

  • 发表日期：

    2025年9月29日

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  • Kalyan Gattupalli,
  • Venkata Surya Bhavana Harish Gollavilli,
  • Harikumar Nagarajan,
  • Poovendran Alagarsundaram,
  • Surendar Rama Sitaraman,
  • Haris M. Khalid

摘要

Ransomware attacks widely happen in cloud services and API’s. However, the existing studies didn’t set the limits and privacy for the API keys, which may lead to data mishandling. Therefore, this paper presents a privacy-preserved API access control policy-based RA detection in cloud VMs using SL2STM. Initially, the VM setup is done by VM registration and API gateway privacy preservation. Afterward, cloud user registration and data selection for upload are performed. At this point, the data is secured. Next, the access policy is created by using Saturated Bell-Fuzzy (SB-Fuzzy). Subsequently, the signature is generated by DSA. Afterward, access policy verification is performed. If both access policies are matched, then RA detection is done; otherwise, a notification is sent to the user. The RA detection system is trained based on pre-processing, feature extraction, feature selection, and classification. Here, the SL2STM classifier detects whether the cloud VMs are attacked or not. If attacked, then the access policy is created again; otherwise, the data is stored in VMs. The outcomes showcase that the proposed methodology obtained a high accuracy of 98.21%, which outperformed existing methods.