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In this paper, a Quality of Service-sensitive customer behavior model graph (QoS-CBMG) is proposed for use in service quality adaptation in e-commerce systems. Success in achieving customer satisfaction and maximizing profit in e-commerce is highly dependent on the QoS provided. However, providing high-level QoS for all customers in all Web sessions is often deemed costly and inefficient. Therefore, a QoS-sensitive model for formulating QoS-aware offers to customers is required. The paper aims to respond to this necessity.

Process mining is adopted as the knowledge extraction technique for developing a QoS-CBMG. If it is assumed that user navigation on a website is a process, then clickstreams during one user’s navigations can be considered process steps.

The application of both QoS-CBMG (the new model) and CBMG (the classic version) to the same real data set demonstrated that the proposed method outperforms CBMG due to its reduction of average absolute error in the measurement scale. This finding also verifies the assumption that customer behavior is sensitive to the level of QoS.

From a theoretical viewpoint, the obtained QoS-CBMG facilitates the adaption in e-commerce systems, which leads to conduct the user to the desired behavior by tuning QoS levels in different Web sessions in a dynamic manner. This implication is due to the fact that QoS-CBMG can predict the upcoming clickstream of the customer at different QoS levels.

Using the proposed model for the adaptation of service quality in e-commerce websites not only results in the efficient management of the provider’s resources but also encourages customer purchases from the website and increases profitability. It is noteworthy that with the advent of cloud computing, e-commerce websites are enabled to provide various levels of QoS for their customers by supplying their basic services (e.g. infrastructure, platform) through cloud platforms.

According to the best of our knowledge, no previous model has taken into account the QoS dimension for customer behavior modeling. The main contribution of this paper is to propose a CBMG that is sensitive to the QoS provided to customers during their navigation to formulate QoS-aware offers to them.

This paper aims to propose a comprehensive model to find out the most preventive subset of security controls against potential security attacks inside the limited budget. Deploying the appropriate collection of information security controls, especially in information system-dependent organizations, ensures their businesses' continuity alongside with their effectiveness and efficiency.

Impacts of security attacks are measured based on interdependent asset structure. Regarding this objective, the asset operational dependency graph is mapped to the security attack graph to assess the risks of attacks. This mapping enables us to measure the effectiveness of security controls against attacks. The most effective subset is found by mapping its features (cost and effectiveness) to items’ features in a binary knapsack problem, and then solving the problem by a modified version of the classic dynamic programming algorithm.

Exact solutions are achieved using the dynamic programming algorithm approach in the proposed model. Optimal security control subset is selected based on its implementation cost, its effectiveness and the limited budget.

Estimation of control effectiveness is the most significant limitation of the proposed model utilization. This is caused by lack of experience in risk management in organizations, which forces them to rely on reports and simulation results.

So far, cost-benefit approaches in security investments are followed only based on vulnerability assessment results. Moreover, dependency weights and types in interdependent structure of assets have been taken into account by a limited number of models. In the proposed model, a three-dimensional graph is used to capture the dependencies in risk assessment and optimal control subset selection, through a holistic approach.