Dynamic Data Driven Application Systems (DDDAS) entails the ability to incorporate additional data into an executing application – these data can be archival or collected on-line; and in reverse, the ability of applications to dynamically steer the measurement process. The paradigm offers the promise of improving modeling methods, and augmenting the analysis and prediction capabilities of application simulations and the effectiveness of measurement systems. This presents the potential to transform the way science and engineering are done, and induce a major impact in the way many functions in our society are conducted, such as manufacturing, commerce, hazard management, medicine. Enabling this synergistic feedback and control-loop between application simulations and measurements requires novel application modeling approaches and frameworks, algorithms tolerant to perturbations from dynamic data injection and steering, and systems software to support the dynamic environments of concern here. Recent advances in complex applications, the advent of grid computing and of sensor systems, are some of the technologies that make it timely to embark in developing DDDAS capabilities. Research and development of such technologies requires synergistic multidisciplinary collaboration in the applications, algorithms, software systems, and measurements systems areas, and involving researchers in basic sciences, engineering, and computer sciences. The rest of the papers in the proceedings of this workshop provide examples of ongoing research developing DDDAS technologies within the context of specific and important application areas.

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Dynamic Data Driven Applications Systems: A New Paradigm for Application Simulations and Measurements

(1962). Management Models and Industrial Applications of Linear Programming. Journal of the Operational Research Society: Vol. 13, No. 3, pp. 274-275.

https://link.springer.com/content/pdf/10.1057%2Fjors.1962.42.pdf

Management Models and Industrial Applications of Linear Programming

(1966). Management Goals and Accounting for Control. Journal of the Operational Research Society: Vol. 17, No. 4, pp. 486-486.

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Management Goals and Accounting for Control

Researchers have previously looked into the problem of determining if a given set of security hardening measures can effectively make a networked system secure. Many of them also addressed the problem of minimizing the total cost of implementing these hardening measures, given costs for individual measures. However, system administrators are often faced with a more challenging problem since they have to work within a fixed budget which may be less than the minimum cost of system hardening. Their problem is how to select a subset of security hardening measures so as to be within the budget and yet minimize the residual damage to the system caused by not plugging all required security holes. In this work, we develop a systematic approach to solve this problem by formulating it as a multi-objective optimization problem on an attack tree model of the system and then use an evolutionary algorithm to solve it.

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Optimal security hardening using multi-objective optimization on attack tree models of networks

When investing in cyber security resources, information security managers have to follow effective decision-making strategies. We refer to this as the cyber security investment challenge.In this paper, we consider three possible decision support methodologies for security managers to tackle this challenge. We consider methods based on game theory, combinatorial optimisation, and a hybrid of the two. Our modelling starts by building a framework where we can investigate the effectiveness of a cyber security control regarding the protection of different assets seen as targets in presence of commodity threats. As game theory captures the interaction between the endogenous organisation's and attackers' decisions, we consider a 2-person control game between the security manager who has to choose among different implementation levels of a cyber security control, and a commodity attacker who chooses among different targets to attack. The pure game theoretical methodology consists of a large game including all controls and all threats. In the hybrid methodology the game solutions of individual control-games along with their direct costs (e.g. financial) are combined with a Knapsack algorithm to derive an optimal investment strategy. The combinatorial optimisation technique consists of a multi-objective multiple choice Knapsack based strategy. To compare these approaches we built a decision support tool and a case study regarding current government guidelines. The endeavour of this work is to highlight the weaknesses and strengths of different investment methodologies for cyber security, the benefit of their interaction, and the impact that indirect costs have on cyber security investment. Going a step further in validating our work, we have shown that our decision support tool provides the same advice with the one advocated by the UK government with regard to the requirements for basic technical protection from cyber attacks in SMEs.

/pdf/decision-support-approaches-for-cyber-security-investment-1gd4vz0khq.pdf

Decision support approaches for cyber security investment

Organizations are making substantial investments in information security to reduce the risk presented by vulnerabilities in their information technology (IT) infrastructure. However, each security technology only addresses specific vulnerabilities and potentially creates additional vulnerabilities. The objective of this research is to present and evaluate a Genetic Algorithm (GA)-based approach enabling organizations to choose the minimal-cost security profile providing the maximal vulnerability coverage. This approach is compared to an enumerative approach for a given test set. The GA-based approach provides favorable results, eventually leading to improved tools for supporting information security investment decisions.

/pdf/matching-information-security-vulnerabilities-to-5fq1gb2fyk.pdf

Matching information security vulnerabilities to organizational security profiles: a genetic algorithm approach

Large scale agent-based simulation has become the focus of great interest from both academy and industry in resent years. It has been shown an effective tool for understanding a variety of complex systems, such as market economics, war games, and epidemic propagation models. As the systems of interest grow in complexity, it is often desirable to have different categories of artificial agents that execute tasks on different time scales. With the added complexity, the scalability of a simulation environment becomes a crucial measure of its ability in coping with the complexity of the underlying system. In this paper, we present the design of SAMAS, a highly scalable architecture for multi-resolution agent-based simulation. SAMAS is a dynamic data driven application system (DDDAS) that adopts an efficient architecture that allows large number of agents to operate on a wide range of time scales. It uses gossiping, an efficient broadcasting communication model for maintaining the overall consistency of the simulation environment. We demonstrate the effectiveness of this communication model using experimental results obtained from simulation.

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SAMAS: Scalable Architecture for Multi-resolution Agent-Based Simulation *

Employment History • Professor and University Scholar, Department of Computer Science, Purdue University, W. Lafayette, IN, Aug. 2006 . • Associate Professor, Department of Computer Science, Purdue University, W. Lafayette, IN, Aug. 2001 July 2006. • Assistant Professor, Department of Computer Science, Purdue University, W. Lafayette, IN, Aug. 1996 July 2001. • Assistant Professor, Department of Computer Science, University of Minnesota, Minneapolis, MN, June 1996 Aug. 1996. • Visiting Research Scientist, Hewlett Packard Labs, Palo Alto, CA, June 1994 to Nov 1994.

Jie Chi

Papers

Matching information security vulnerabilities to organizational security profiles: a genetic algorithm approach

SAMAS: Scalable Architecture for Multi-resolution Agent-Based Simulation *

Oceanus: A Distributed Web-Based Framework for Execution of Genetic Algorithms.