MAIDS: Malicious Agent Identification-based Data Security Model for Cloud Environments

TL;DR

This paper introduces MAIDS, a proactive data security model for cloud environments that uses machine learning to identify malicious agents before data breaches occur, enhancing data protection.

Contribution

The paper proposes a novel MAIDS model that employs XGBoost to predict malicious agents proactively, unlike existing methods that only detect breaches after they happen.

Findings

Successfully predicts malicious agents with high accuracy.

Provides a comprehensive security framework for cloud data sharing.

Enhances data protection by preventing unauthorized access.

Abstract

With the vigorous development of cloud computing, most organizations have shifted their data and applications to the cloud environment for storage, computation, and sharing purposes. During storage and data sharing across the participating entities, a malicious agent may gain access to outsourced data from the cloud environment. A malicious agent is an entity that deliberately breaches the data. This information accessed might be misused or revealed to unauthorized parties. Therefore, data protection and prediction of malicious agents have become a demanding task that needs to be addressed appropriately. To deal with this crucial and challenging issue, this paper presents a Malicious Agent Identification-based Data Security (MAIDS) Model which utilizes XGBoost machine learning classification algorithm for securing data allocation and communication among different participating entities in the cloud system. The proposed model explores and computes intended multiple security parameters associated with online data communication or transactions. Correspondingly, a security-focused knowledge database is produced for developing the XGBoost Classifier-based Malicious Agent Prediction (XC-MAP) unit. Unlike the existing approaches, which only identify malicious agents after data leaks, MAIDS proactively identifies malicious agents by examining their eligibility for respective data access. In this way, the model provides a comprehensive solution to safeguard crucial data from both intentional and non-intentional breaches, by granting data to authorized agents only by evaluating the agents behavior and predicting the malicious agent before granting data.