Biblio
Abstract-Virtual evaluation of complex command and control concepts demands the use of heterogeneous simulation environments. Development challenges include how to integrate multiple simulation platforms with varying semantics and how to integrate simulation models and the complex interactions between them. While existing simulation frameworks may provide many of the required services needed to coordinate among multiple simulation platforms, they lack an overarching integration approach that connects and relates the semantics of heterogeneous domain models and their interactions. This paper outlines some of the challenges encountered in developing a command and control simulation environment and discusses our use of the GME meta-modeling tool-suite to create a model-based integration approach that allows for rapid synthesis of complex HLA-based simulation environments.
The research was conducted by Institute for Software Integrated Systems at Vanderbilt University, in collaboration with George Mason University, University of California at Berkeley, and University of Arizona.
A THz image edge detection approach based on wavelet and neural network is proposed in this paper. First, the source image is decomposed by wavelet, the edges in the low-frequency sub-image are detected using neural network method and the edges in the high-frequency sub-images are detected using wavelet transform method on the coarsest level of the wavelet decomposition, the two edge images are fused according to some fusion rules to obtain the edge image of this level, it then is projected to the next level. Afterwards the final edge image of L-1 level is got according to some fusion rule. This process is repeated until reaching the 0 level thus to get the final integrated and clear edge image. The experimental results show that our approach based on fusion technique is superior to Canny operator method and wavelet transform method alone.
Vehicle-logo location is a crucial step in vehicle-logo recognition system. In this paper, a novel approach of the vehicle-logo location based on edge detection and morphological filter is proposed. Firstly, the approximate location of the vehicle-logo region is determined by the prior knowledge about the position of the vehicle-logo; Secondly, the texture measure is defined to recognize the texture of the vehicle-logo background; Then, vertical edge detection is executed for the vehicle-logo background with the horizontal texture and horizontal edge detection is implemented for the vehicle-logo background with the vertical texture; Finally, position of the vehicle-logo is located accurately by mathematical morphology filter. Experimental results show the proposed method is effective.
Cyber SA is described as the current and predictive knowledge of cyberspace in relation to the Network, Missions and Threats across friendly, neutral and adversary forces. While this model provides a good high-level understanding of Cyber SA, it does not contain actionable information to help inform the development of capabilities to improve SA. In this paper, we present a systematic, human-centered process that uses a card sort methodology to understand and conceptualize Senior Leader Cyber SA requirements. From the data collected, we were able to build a hierarchy of high- and low- priority Cyber SA information, as well as uncover items that represent high levels of disagreement with and across organizations. The findings of this study serve as a first step in developing a better understanding of what Cyber SA means to Senior Leaders, and can inform the development of future capabilities to improve their SA and Mission Performance.
Current post-mortem cyber-forensic techniques may cause significant disruption to the evidence gathering process by breaking active network connections and unmounting encrypted disks. Although newer live forensic analysis tools can preserve active state, they may taint evidence by leaving footprints in memory. To help address these concerns we present Forenscope, a framework that allows an investigator to examine the state of an active system without the effects of taint or forensic blurriness caused by analyzing a running system. We show how Forenscope can fit into accepted workflows to improve the evidence gathering process. Forenscope preserves the state of the running system and allows running processes, open files, encrypted filesystems and open network sockets to persist during the analysis process. Forenscope has been tested on live systems to show that it does not operationally disrupt critical processes and that it can perform an analysis in less than 15 seconds while using only 125 KB of memory. We show that Forenscope can detect stealth rootkits, neutralize threats and expedite the investigation process by finding evidence in memory.