Asa Weinholt, & Tobias Andersson Granberg. (2013). Evaluation of enhanced collaboration between fire and rescue services and security officers. In J. Geldermann and T. Müller S. Fortier F. F. T. Comes (Ed.), ISCRAM 2013 Conference Proceedings – 10th International Conference on Information Systems for Crisis Response and Management (pp. 735–740). KIT; Baden-Baden: Karlsruher Institut fur Technologie.
Abstract: The hypothesis of this study is that collaboration between fire and rescue services and new actors, with basic rescue skills, might be a cost effective way to improve emergency response. Interview studies of collaborations between fire and rescue services and security officers in three Swedish municipalities are presented. Seven semi-structured interviews are conducted with representatives from the security officer companies, the fire and rescue services and security managers at the municipalities. The method used to evaluate the collaborations quantitatively is Cost-benefit analysis. The collaborations have positive economic effects for society that most likely outweighs the costs. There also exist several external effects that are not possible to value monetarily, but that represent positive values for society. The results and their generalizability are discussed, as well as the possibility for these new collaborations to improve crisis management.
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Matt Wolff. (2010). Unsupervised methods for detecting a malicious insider. In C. Zobel B. T. S. French (Ed.), ISCRAM 2010 – 7th International Conference on Information Systems for Crisis Response and Management: Defining Crisis Management 3.0, Proceedings. Seattle, WA: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: One way a malicious insider can attack a network is by masquerading as a different user. Various algorithms have been proposed in an effort to detect when a user masquerade attack has occurred. In this paper, two unsupervised algorithms are proposed with the intended goal of detecting user masquerade attacks. The effectiveness of these two unsupervised algorithms are then compared against supervised algorithms.
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Ying Zhao, Mengqi Yuan, Guofeng Su, & Tao Chen. (2015). Crowd Security Detection based on Entropy Model. In L. Palen, M. Buscher, T. Comes, & A. Hughes (Eds.), ISCRAM 2015 Conference Proceedings ? 12th International Conference on Information Systems for Crisis Response and Management. Kristiansand, Norway: University of Agder (UiA).
Abstract: Identifying the terror attack, illegal public gathering or other mass events risks by utilizing cameras is an important concern both in crowd security area and in pattern recognition research area. This paper provides a physical entropy model to measure the crowd security level.The entropy model was created by identifying individuals?moving velocity and the related probability. The individuals are represented by Harris Corners in videos, thus to avoid the time-consuming human recognition task. Simulation experiment and video detection experiments were conducted, verified that in the disordered state, the entropy is higher; while in ordered state, the entropy is much lower; when the crowd security has a sudden change, the entropy will change. It was verified that the entropy is the applicable indicator of crowd security. By recognizing the entropy mutation, it is possible to automatically detect the abnormal crowd behavior and to set the warning alarm.
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Zeno Franco, Nina Zumel, & Larry E. Beutler. (2007). A ghost in the system: Integrating conceptual and methodology considerations from the behavioral sciences into disaster technology research. In K. Nieuwenhuis P. B. B. Van de Walle (Ed.), Intelligent Human Computer Systems for Crisis Response and Management, ISCRAM 2007 Academic Proceedings Papers (pp. 115–124). Delft: Information Systems for Crisis Response and Management, ISCRAM.
Abstract: As the complexity of disasters increases, a transdisciplinary conceptual framework designed to address three key variables-technology, disaster severity, and human characteristics-must be developed and elaborated. Current research at the nexus of disaster management and information science typically addresses one or two of these factors, but rarely accounts for all three adequately-thus rendering formal inquiry open to a variety of threats to validity. Within this tripartite model, several theories of human behavior in disaster are explored using the response of the Federal Government and the general public during Hurricane Katrina as an illustrative background. Lessons learned from practice-based scientific inquiry in the social sciences are discussed to address concerns revolving around measurement and statistical power in disaster studies. Finally, theory building within the transdisciplinary arena of disaster management and information science is encouraged as a way to improve the quality of future research.
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