Statistical Analysis-Driven Prediction Model for Malicious Entity Detection in Cloud Environment

Ishu Gupta

doi:10.1109/tnsm.2026.3704450

Statistical Analysis-Driven Prediction Model for Malicious Entity Detection in Cloud Environment

2026-01-01

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DOI：10.1109/tnsm.2026.3704450

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Data sharing across distinct entities, including clouds, has become a necessity to enhance the performance of enterprises; however, it leads to data protection challenges. In this paper, a novel model aimed at data protection is presented when multiple untrusted parties are involved in the system. The proposed model enables secure data sharing and effective data distribution among the involved entities while minimizing the risk associated with data exposure. It enables the identification of malicious entities responsible for data leakage with high confidence. To this end, an efficient distribution strategy based on object and user selection, incorporating an operative access control mechanism, is proposed. Furthermore, algorithms are designed for the selection of data to be distributed among users. Experimental results demonstrate that the proposed model achieves significant improvements of 31%, 97%, and 64% in success rate, detection rate, and assessment rate, respectively, compared to prior works. Moreover, it reduces data leakage by up to 75% and lowers the error rate by up to 83% for malicious entity detection, while simultaneously enhancing detection performance and capability by up to 32% and 40%, respectively, over existing approaches.

Keywords:

Modeling

Signal detection

Clouds

Algorithms

Lead

Probability

Resource management

Cloud computing

Measurement

Federated learning

data protection

distribution strategy

data allocation

malicious entity

information security

Statistical Analysis-Driven Prediction Model for Malicious Entity Detection in Cloud Environment

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