Intelligent and Mined-knowledge Driven Consultant Services for User Behavior Recovery in Large Distributed Systems

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An increasing number of organizations are integrating their public services to become more competitive in providing broader and more easily accessible services to the public. The resulting federated organization must relax its internal regulations and access control policies to provide compatibility of services that collectively form a trust model of service usage. Such trust models jeopardize the public’s precious data assets. The complex and changing nature of usage patterns and the high costs of violating a trust model by accessing sensitive private data, create serious threats to the integrity of distributed information systems that are the backbone of public services. The proposed research program aims at providing a new generation of intelligent decision support services, namely “Consultant-as-a-Service” that effectively assists and guides a broad range of users that can be categorized as: i) customers of organizations who are not familiar with the large variety of organization’s services to the public; and ii) system administrators of large distributed systems who are overwhelmed by the number of system users whose behavior in using the system services must be monitored and controlled. Most large organizations are operated by outdated legacy systems that are not compatible with advanced technology for secure communication, and protocols for user authentication and authorization. Therefore, the system vulnerability problem is becoming more serious and may drastically impair the quality of service provisioning. This issue may cause vast economical costs for the organizations whose private data assets have been compromised. For example, the current state of distributed medical imaging systems and their central repositories in most provinces is based on a trust model, which makes it vulnerable. Such cases have been the main motivation for the approach in this proposal. The proposed consultant service is knowledge driven and closely assists the security administrators to compose detailed and complex patterns of usage and provides solutions that can answer to a variety of user behavior questions, which will save valuable time and effort that must be invested for such discoveries (if possible at all). Example queries are as follow. What is common among users who accessed the system around the traffic rush hour? Which policy rules are sensitive to the current state of system usage? What is the frequent behavior pattern of a specific role or user in the system? Is there any behavior similarity among users in specific and separated locations, and in what aspect they are similar? Etc. Furthermore, there are huge possibilities to identify anomaly behavior of some users by close investigation of the identified common behavioral patterns, in which the existence of some actions cannot be justified by the related policy rules. The proposed research will train three Master’s students who will become expert in different skills such as: applying data mining techniques on different data assets; working on advanced decision support techniques; challenging with high complexity of optimization search algorithms and constrain pattern matching; and designing pattern languages. Two PhD students will be trained in this research, which will provide theoretical and practical solutions to novel user behavioral modeling and designing intelligent decision services. All research students will have access to an advanced cloud based data centre that provides an experimental distributed medical imaging system to design, develop and evaluate their research works.