**3. An intelligent framework for ABACsh**

The framework is designed based on knowledge-agent and it employs rule-based expert system method. This intelligent system is not based on machine learning which will have a percentage of correct answers. This system is based on the available rules; therefore, it is not a type of uncertain approach. The system must guarantee an access decision.

The purpose of this **ABACsh** framework is to prove that AI architecture can contribute in supporting a dynamic access control. In regard to guaranteeing behavior, the followed mechanism in this chapter is based on knowledge available. If there is a shortage in knowledge, the access decision will be denied. There are other AI categories related to uncertain knowledge such as probabilistic reasoning, However, uncertain reasoning is out of this research scope.

#### **3.1 AI scope for the proposed framework**

According to [32–34], artificial intelligence systems are designed to think and act. They can be categorized into four types based on the intention of the system: Thinking Humanly, Acting Humanly, Thinking Rationally and Acting Rationally. The category of Thinking Rationally leads to an evolved need for the logic field in artificial intelligence. Involving logic in an intelligent system faces two substantial obstacles. The first one is the difficulty of presenting informal-knowledge using a formal logical notation though the certainty level is less than 100%. The second is that solving problems theoretically is different from solving them practically when the machine capacity is taken into consideration.

The category of Acting Rationally initiates the development of a computer agent. Prior to computer science, the term agent was used in different fields.

**39**

**Figure 1.**

*AI scope for the proposed framework.*

*An Intelligent Access Control Model*

*DOI: http://dx.doi.org/10.5772/intechopen.95459*

an agent implementation within a physical system.

possible states instead of hard-coded all predicted states.

be the most appropriate logic to be used in AI as discussed by [36].

**3.2 Logical-based agent architecture**

Therefore, there are various definitions of agent. However, it can be defined as an entity that acts within an environment by sensing its surroundings to update its knowledge and acts upon that to meet specific goals [35]. The agent function represents an abstract mathematical description, whereas the agent program represents

Problem-solving through an intelligent agent involves four stages. Firstly, the agent formulates its goal. Secondly, it formulates the problem based on five steps: initial state, possible actions, transition model that describes what each action does, goal test and path cost. Thirdly, it searches for a solution by looking for a sequence of actions that leads to the goal. Fourthly, in the execution stage, the solution found is implemented. However, the problem-solving agent is inflexible as each possible state should be hard-coded. Therefore, the complexity of the search stage grows exponentially in relation to the number of states in addition to its inability to infer unobserved information. Therefore, there is a need for logic to reason about the

Knowledge-based reasoning is a step in overcoming problem-solving agent limitations. The logic provides a natural language for describing and reasoning about the system. The knowledge-based system is given facts about the external world, and it is asked queries about that world. The rule-based expert system is a popular method that is used to build knowledge-based systems. The rules are used to represent knowledge in the format of IF-THEN. The Inference engine is the reasoning component whereby the system concludes by linking the rules given in the knowledge base with facts supplied from the database. The explanation facilities allow the user to interact with the expert system to get justifications regarding the results produced by the inference engine. Therefore; the AI scope for the proposed intelligent-framework for **ABACsh** is illustrated in **Figure 1**. Modal logic is found to

Intelligence security is a fertile approach, as most existing security paradigms suffer from reactive and fragmented approaches [37]. In a frequently changing infrastructure, deploying an agent-based mechanism will be an advantage [38].

### *An Intelligent Access Control Model DOI: http://dx.doi.org/10.5772/intechopen.95459*

*Quality Control - Intelligent Manufacturing, Robust Design and Charts*

**3. An intelligent framework for ABACsh**

uncertain reasoning is out of this research scope.

the machine capacity is taken into consideration.

**3.1 AI scope for the proposed framework**

guarantee an access decision.

Therefore, the proposed SoD is operation-object orientated that defines a rulesset reflecting the forbidden operations on the set of objects under a specific situation of a collection of entities attributes. Entities include the object, the subject, the environment, and the system context. Moreover, formal logic facilitates SoD rule creation, even by non-expert security administrators. Since the proposed system is attribute-based, it is not necessary to update different locations if a new action restriction is added, deleted, or modified. Object-attributes and operations. We can discern from the above that it is more appropriate to enhance SoD by implementing a form of HSoD which will be suitable to be enforced in a dynamic access control policy environment such as ABAC. With RBAC, the centric entity involved in the SoD principle design is the role set. In contrast, ABAC cannot consider a role in the form of an attribute as it can lead to a chaos [30]. Therefore, the focus of this paper regarding formally defining SoD within ABAC will be on attributes and attribute-relations, with no aim to define an application-oriented SoD. Thus, we aim to identify a logical based design for SoD within the ABAC policy model. The proposed work is based on formal logic; exception cases are not encouraged in a formal logic as exceptions make regulations non-monotonic and introduce conflict between proven conclusions [31]. Therefore, the proposed SoD is operation-object orientated that defines a rules-set rejecting the forbidden operations on the set of objects under a specific situation of a collection of entities attributes. Entities include the object, the subject, the environment, and the system context. Moreover, formal logic facilitates SoD rule creation, even by non-expert security administrators. Since the proposed system is attribute-based, it is not necessary to update different locations if a new action restriction is added, deleted, or modified.

The framework is designed based on knowledge-agent and it employs rule-based

The purpose of this **ABACsh** framework is to prove that AI architecture can contribute in supporting a dynamic access control. In regard to guaranteeing behavior, the followed mechanism in this chapter is based on knowledge available. If there is a shortage in knowledge, the access decision will be denied. There are other AI categories related to uncertain knowledge such as probabilistic reasoning, However,

According to [32–34], artificial intelligence systems are designed to think and act. They can be categorized into four types based on the intention of the system: Thinking Humanly, Acting Humanly, Thinking Rationally and Acting Rationally. The category of Thinking Rationally leads to an evolved need for the logic field in artificial intelligence. Involving logic in an intelligent system faces two substantial obstacles. The first one is the difficulty of presenting informal-knowledge using a formal logical notation though the certainty level is less than 100%. The second is that solving problems theoretically is different from solving them practically when

The category of Acting Rationally initiates the development of a computer agent. Prior to computer science, the term agent was used in different fields.

expert system method. This intelligent system is not based on machine learning which will have a percentage of correct answers. This system is based on the available rules; therefore, it is not a type of uncertain approach. The system must

**38**

Therefore, there are various definitions of agent. However, it can be defined as an entity that acts within an environment by sensing its surroundings to update its knowledge and acts upon that to meet specific goals [35]. The agent function represents an abstract mathematical description, whereas the agent program represents an agent implementation within a physical system.

Problem-solving through an intelligent agent involves four stages. Firstly, the agent formulates its goal. Secondly, it formulates the problem based on five steps: initial state, possible actions, transition model that describes what each action does, goal test and path cost. Thirdly, it searches for a solution by looking for a sequence of actions that leads to the goal. Fourthly, in the execution stage, the solution found is implemented. However, the problem-solving agent is inflexible as each possible state should be hard-coded. Therefore, the complexity of the search stage grows exponentially in relation to the number of states in addition to its inability to infer unobserved information. Therefore, there is a need for logic to reason about the possible states instead of hard-coded all predicted states.

Knowledge-based reasoning is a step in overcoming problem-solving agent limitations. The logic provides a natural language for describing and reasoning about the system. The knowledge-based system is given facts about the external world, and it is asked queries about that world. The rule-based expert system is a popular method that is used to build knowledge-based systems. The rules are used to represent knowledge in the format of IF-THEN. The Inference engine is the reasoning component whereby the system concludes by linking the rules given in the knowledge base with facts supplied from the database. The explanation facilities allow the user to interact with the expert system to get justifications regarding the results produced by the inference engine. Therefore; the AI scope for the proposed intelligent-framework for **ABACsh** is illustrated in **Figure 1**. Modal logic is found to be the most appropriate logic to be used in AI as discussed by [36].
