An Open-Source Proactive Security Infrastructure for Business Process Management

Business Process Management Systems (BPMS) have emerged in the IT arena as cornerstone in the automation and orchestration of complex services for organizations. These systems manage critical information that is crucial for the organizations. The potential cost and consequences of security threats could produce information loss for the reputation of organizations. Therefore, the early response regarding to the non-compliance of security requirement is a real necessity overall during the business process execution. Currently, an active response requires a human intervention with high know-how and expertise in both business process management and security. In this paper, we propose an initial work which presents an open-source proactive infrastructure for the automatic continuous monitoring and checking compliance of security requirements at runtime of business processes

Diagnosis progresiva en el tiempo de sistemas dinámicos

En este trabajo se presenta un nuevo avance en la metodología objeto de estudio por los mismos autores presentada en trabajos anteriores, esta nueva aproximación se basa en realizar detección y diagnosis en momentos tempranos de la evolución de un sistema dinámico, el cual evoluciona desde una situación estable a otra distinta también estable, siendo en esos cambios o pulsos de control en los momentos en los que el fallo será detectado y posteriormente diagnosticado, esta detección y diagnosis temprana se realizará mediante aprendizaje supervisado, el cual se realiza off-line:, obteniendo tres árboles de decisión para realizar diagnosis en un tercio, dos tercios y al final del transitorio. Debemos tener en cuenta que los datos con los que el sistema trabaja pueden obtenerse mediante adquisición del sistema real o mediante simulaciones como es el caso, es de resaltar que las situaciones se han realizado en presencia de ruido, adecuando el exhaustivo tratamiento de los datos a minimizar en lo posible el impacto de este sobre el aprendizaje

Multiple decision trees to diagnose a transient state of dynamic systems. Application to a DC motor.

In this paper, a novel methodology is proposed to diagnose a transient state of a dynamic system us­ing supervised learning. lt is composed by two steps: one off-line process and another on-Iine pro­cess. The off-line phase begins gathering data from the system, both when it is running free of fault and when the system is running in each fault mode. Also, it is possible to generate these data from Monte Cario simulations of a system model. A seg­mentation and normalization algorithm is used to reduce the large amount of gathered data. The fi­nal step of the off-line process is the generation of a decision tree by a classification tool. The on-lme process of the methodology consists in evaluating a new reading of the system sensors with the gen­erated decision trees. The system diagnosis is the result of this evaluation which has a linear compu­tational cost due to the simplicity of the decision trees. In arder to improve diagnosability problems of this methodology, it is proposed a new solution in this work. Instead of generating only one de­cision tree, a different decision tree is generated for each fault mode and free of fault mode. Therefore multiple possibilities of diagnosis can be offered for a given behaviour of dynamic system. Meth­odology has been applied to diagnose a DC motor. Eight different faults have been considered and the results have been discussed including diagnosabil­ity conflicts.Ministerio de Ciencia y Tecnología DPl2003-07146-C02-0