YAFA-SOA: A GA-Based Optimizer for Optimizing Security and Cost in Service Compositions
Wafaa Radwan, Yousef Hassouneh, Abdel Salam Sayyad, Nariman Ammar · 2017
This paper studies heuristic search-based optimization of service compositions. We have investigated applying Genetic Algorithms (GA) to optimize service-oriented architectures (SOA) in terms of security goals and cost, we help software Engineers to map the optimized service composition to the business process model based on security and cost. Service composition security risk is measured by implementing the aggregation rules from the local security risk values of the aggregated services in the composition. We adapt the DREAD model for Security risk assessment by suggesting new categorizations for calculating DREAD factors based on a proposed service structure and service attributes. We implemented the YAFA-SOA Optimizer as an extension of an existing GA implementation to solve multi-objective optimization problems for varying number of objectives in the context of SOA. We evaluated the tool in a case study. The study results show that applying multi-objective GA is feasible to find the optimized security and cost in SOA-based systems. We were able to approve that adding security services to the generated composition reduces the risk severity of the generated composition and enhances its security in terms of confidentiality, integrity and availability (CIA). We found that the generated service composition risk severity is less than 0.5, which matches the validation results obtained from a security expert.