No abstract

Global information systems are becoming more complex and dynamic everyday: huge amounts of data and messages through those systems show dynamically changing traffic patterns. Because of this, diagnosing when sub-systems are not working properly is difficult. System failures or errors in information exchange protocols sometimes happen and interrupt the correct working of the system. International supply chain systems, for example, need smooth running when performing information exchange tasks between sub-systems but, in practice, show various types of information security breaches. So, finding a solution to diagnose and discover failure spots in the dynamic global system is highly required. This challenge is taken up in this paper. Based on an example prototype of the new European supply chain information system (Data Pipeline) and the required global monitoring process, we tested feasibility and effectiveness of real-time detection of system failures, the results of which are described in this paper.

Firewalls are controlled by rules which often incur anomalies. The anomalies are considered serious problems that administrators do not desire to happen over their firewalls because they cause more vulnerabilities and decrease the overall performance of the firewall. Resolving anomaly rules that have already occurred on the firewall is difficult and mainly depends on the firewall administrator's discretion. In this paper, a model is designed and developed to assist administrators to make effective decisions for optimizing anomaly rules using the probability approach (Bayesian). In this model, the firewall needs to add four property fields (Extra fields) to the firewall rules: frequency of packets matching against rules, evidence of creating rules, the expertise of rules creator and protocol priority. These fields are used to calculate the probability of each firewall rule. The probability for each rule is used while the rules conflict and administrators need to resolve them. The rule having the highest probability value indicates that it has the highest priority in consideration. Experimental results show that the proposed model allows firewall administrators to make significant decisions about solving anomaly rules. The data structure of this model is based on k-ary tree, therefore the speed of building tree, time complexity and space complexity: O(n), O(logmn) and O(m*n) respectively. Besides, the confidence of the proposed firewall for resolving firewall rule anomalies of the administrator increase by 29.6% against the traditional firewall, and the reliability value between the inter-raters also increase by 13.1%.

No abstract

Although business process visibility is considered to be increasingly important for international trade, the visibility of existing supply chains is currently still ambiguous. Information deficiencies such as incorrectness or inconsistency of data are major determinants that decrease clarity. The Data Pipeline principle has been proposed to overcome the data quality shortcomings, enhance the visibility, and improve performance of the supply chain. In a first elaboration, the Data Pipeline model named the Distributed Trust Backbone (DTB) was designed and implemented in three different countries. In order to discover the effectiveness and feasibility of the model, the visualization of the Data Pipeline’s process flow is urgently required in terms of both real-time detection of system failures and process flow representations. However, there is no process visualization feature available for the Data Pipeline. This challenge is taken up in this paper. We propose a Data Pipeline monitoring system and describe the results of performed simulation tests based on a case study of the international trade lane between Southeast Asia and Europe.