Projects

PhD

Empirical study in cyber deception: computational trust and cloud neutrality

Description: Agents wants to interact with a system they trust, upon needed. This trust develops over time, mostly depends on past behaviors in forms of interactions. The future usage of the same system, hence, depends upon this trust value. It is hereby necessary to define a computational way to define trust for such system e.g. a computing resource that will comprehend the system’s behavior. This trust can be useful in self-assessment by the computing resource itself or in decision making by the consumer. It is, hence, necessary to define such trust evaluation model on different aspects of such computing resource’s usage: stability in Quality of Service, security strength. The result shows that these models has the capability of representing behavior of these aspects’ of the corresponding system.

Problem Statement:

X represents consumers and Y represents Service Providers. Y's service can be SAAS, IAAS or data hosting for X.

One problem is measuring the security strength of a server. Trust model has a good application here as security can be considered one type of behavior of Y.

One Trust evaluation can be focused on statistical models (distribution) for cyber attacks. Another trust model evaluation can be focused on the ability to defend a cyber-attack.

Another problem associated with this is the unstable QoS of Cloud services (SAAS and IAAS) like Y towards X. This unstable behavior of Y is periodic, it can be Y's intentional and unintentional behavior. For both international and unintentional behavior proposed solution is Con man trust algorithm for Y’s instability detection. Y’s behavior is not expected periodically: intentional, solution is trust measured from cloud neutrality.

PhD candidacy completed on 21st November 2016.

Former Projects not associated with PhD dissertation: CHAPS Software Project for North Dakota Cattle Association (May 2015 to November 2015)

MS