Evaluation of Trust Algorithm - A Trust-Based Adaptive Access Control Model for Wireless Sensor

The user behaviour trust algorithm is evaluated based on numerical analysis in MATLAB [83] to check and show how the total trust value of users vary given different users’ be-

7.5 Evaluation of Trust Algorithm 81

Fig. 7.2 Flow Chart of the Trust Model

haviour information or attributes. The trust algorithm is calculated with random variables4 that represent the trust value of three different attributes such as trust value for location, users’ role and time range. Random variables are useful when solving and complex problems related to probability (whether the users can be trusted or not trusted). The trust algorithm can be seen as follows:

Calculate Total Trust (Ttotal_{, T}cur_{, T}pre_{, T}Lo_{, T}Ro_{, T}Ti_{, α, β )}

Uin f o_{= Current User Information}

Ttotal = Total Trust Value

82 A Simple User Behaviour Trust Model Tpre_{= Previous Trust Value}

Tcur= Current Trust Value TLo= Trust Value for Location TRo= Trust Value for User’s Role TTi= Trust Value for Time Range

α and β = Constant Weighting Factor (0 ≤ α, β ≤ 1) For TLo_, TLou= randi(4,1,10); For TRo_, TRo_{= randi(4,1,10);} For TTi_, TTi= randi(4,1,10);

For Current Trust (Tcur_),

Tcur(n) =√3TLo_{∗ T}Ro_{∗ T}Ti

For Total Trust (Ttotal_),

if Tpre_{(n) = NA,}

Tcur_{(n) = T}total _(n)

Return Ttotal_(n);

else Tpre_{(n) = T}total_(n-1);

Ttotal_{(n) = ( α * T}cur_{(n)) + ( β * T}pre_(n))

Return Ttotal(n);

Figure 7.3 shows the numerical analysis of trust algorithm based on users’ behaviour pattern in MATLAB. The green line presents the previous trust value of the user and the red line represents the total trust value of the users. The blue line represents the current behaviour trust of the user; the black lines represent the trust value of three different attributes such as location; user’s role; and user’s time range. These attributes were simulated by using the “randi” [83] function based on uniformly distributed pseudo-random integers to generate the random integers. This function generates different variables that are used as the defined trust value for location of user, value for location of targeted object, value for user’s role and value for time range, for the current trust evaluation. “randi(4,1,10)” represents the numerical number between 4 to 1 for the transactions (10). Therefore, it shows that the current trust value of a user varies based on the dynamic changes of his or her behaviour information. Overall, it shows that the trust value of users can be evaluated and calculated based on highly dynamic characteristics of their behaviour information. Additionally, Fig-

7.6 Conclusion 83 ure 7.3 demonstrates that the total trust value of a user does not only rely on the current trust value that evaluate based on the users’ behaviour information from recent transaction but also depends on the previous trust values.

Fig. 7.3 Behaviour Trust Evaluation

7.6 Conclusion

This chapter discussed a simple user behaviour trust model with figures and diagrams. The proposed model is developed and designed based on the geometric mean and weighted run- ning average to calculate the trust value of the user. The results obtained from the evaluation of trust algorithms based on numerical simulation in MATLAB show that the trust value of the users can vary based on the current users’ behaviour information and the previous trust value. The proposed model is developed to cooperate with access control engines and to

84 A Simple User Behaviour Trust Model be used as one of the policy evaluation criteria for making access decisions effectively and dynamically. The proposed model is designed based on the user behaviour information (such as location, role and time) that can be easily obtained from any data access request. This means that, there is no essential requirement regarding the behaviour information or attributes and it can be easily adapted in current access control engines. Additionally, the introducing of trust model in access control engines can help to address the conflict between data privacy and data availability because only the trusted users can get the restricted access in emergency and unanticipated situations. Therefore, we extend the adaptive access control model with prevention and detection mechanism from chapter 6 with the proposed behaviour trust model to create the Trust-Based Adaptive Access Control Model (T BA2_C)

Chapter 8 T BA

2

C_{: A Trust-Based Adaptive Access}

Control Model

8.1 Introduction

In the healthcare industry, patients are expected to be treated in reasonable time and any loss in data availability can result in further decline in the patient’s condition or can even lead to death. Therefore, the availability of data is usually more important than security concerns. The overwhelming priority is to take care of the patient, but the privacy and confidentiality of that patient’s medical records cannot be neglected. In current healthcare applications, there are many problems concerning security policy violations in the real world environ- ment. Additionally, we cannot assume that all the users are trustworthy enough to give a flexible access in both defined and emergency situations because there is no facility to detect abnormal data access from authorised users in current Wireless Sensor Network (WSN) access control models. Some WSN access control models address data availability issue for emergency situations but the privacy of the patients’ information has been neglected.

To address the above issues, Trust-Based Adaptive Access Control (T BA2_{C) is proposed}

which is an extended version of the model proposed in chapter 6 incorporating the simple user behaviour trust model from chapter 7. T BA2C is aimed at protecting the privacy of the users’ information and the privacy of the patients’ information allowing only trusted users to have a restricted access in emergency and unanticipated situations. In addition, the trust value is used as an extra condition in the authorisation policy to detect abnormal data access from authorised users. Therefore, T BA2_{C is an emerging concept that builds on the con-}

86 T BA2_{C: A Trust-Based Adaptive Access Control Model}

and detection mechanism, and the possibility-with-override concept to provide a flexible policy that is not too permissive nor too strict in the access control engine and to adjust the access decisions effectively based on the user behaviour trust values.

Firstly, the development details of T BA2_{C model are explained, followed by the simula-}

tion test scenario with its threat model. Additionally, a medical application is developed to evaluate and verify the proposed T BA2_{C model. Finally, this chapter concludes with further}

suggestions.

In document A Trust-Based Adaptive Access Control Model for Wireless Sensor Networks (Page 102-108)