## 5 Proposed Model
### Comparison of Traditional PDP in Access Control vs. Proposed PDP-Based ML Technology  
This section outlines how the policy decision point (PDP) functions in conventional access control and explains how this research intends to address the shortcomings of conventional methods by rendering them more flexible, dynamic and secure. As seen in Figure 1, the typical access decision-making technique in policy decision points (PDP) performs logical functions on the access request until it is confirmed to comply with the suggested control policies. The procedure is outlined below. * Policy Enforcement Point (PEP) transmits a request for access to the policy decision point (PDP) for the desired resource. * The PDP resolves the request's attributes after receiving access requests from PEP. This should match the policy associated with the request at the policy administration point (PAP). * As a final response, The Policy Administration Point (PAP) inquiries the pertinent policy set from all policy sets and returns the combination policies to the policy decision point. Then, if the final response is allowed, the user has direct access to the specified resource. Conversely, the user is refused access to the specified resource if the outcome is denied. The classic PDP technique suffers from several issues. One of these is the bottleneck in PDP performance, which has four primary causes:  
* a significant increase in access control rules and the parallel entry of an enormous amount of user requests that exceed the capacity of the policy decision point (PDP),
* Matching the access request attributes from the massive policy administrative point (PAP) to make an access decision This becomes more difficult and time-consuming to manage as the policy's scope expands. * The traditional method determines authorization according to the logical outcome of both the guidelines and the access request. There is a one-to-many relationship between access requests and multiple policies, so an increase in the number of inquiries may result in conflicts between authorization decisions and policies. * Direct communication between the PAP and the PDP may compromise the PAP's privacy and violate policies, resulting in the PDP granting access to unauthorised users. This study presents a distributed, adaptable and dynamic PDP approach to access control based on ML for making effective authorization decisions to address the above  
Figure 1: Traditional policy decision point (PDP) method in access control. mentioned issues. Figure 2 illustrates the proposed method. Policy decision points (PDPs) are trained using current access control policies in order to make an offline authorization decision, which is labelled \"Train Offline Point\" in Figure 2. During permission decisions, there is no interaction between the PDP and the actual policy in the PAP. To protect the privacy and security of PAP, the administration of the policy and the determination of permission are generally autonomous. At the same time, there is no necessity to query associated access control policies. during the permission determination process, which may be delivered and executed independently of the regulations. A light, effective and reliable access control system is provided instead. The suggested model supports multiple permission decision architectures, which are flexible mapping methods that can improve access control decision-making performance in a BYOD environment. The following points describe the foundations upon which the proposed model is constructed. * **Cascade and Distributed Architecture:** a composite permission decision structure sequentially execute sub-permission decision engines. This architecture can be used when companies need cross-domain data access. Policy data from security organisations train decision algorithms. Cross-domain resources are restricted to users [26]. * **Parallel Architecture:** a composite authorization decision structure lets each sub-authority decision engine operate simultaneously and synchronously without dependence.