tailieunhanh - Hack proofing your network second edition - Part 3

Cuối cùng, kết quả của quá trình năng động này được hiển thị cho một người dùng hoặc nhà phân tích của con người (thông qua một giao diện humancomputer (HCI) chức năng). Lưu ý rằng điều này mô tả của quá trình phản ứng tổng hợp dữ liệu đã được rất nhiều đơn giản hóa cho | TABLE Mapping between Sensor Classes and Activities for a Bridging Operation State MTI Radar SAR COMINT ELINT FLIR Optical Acoustic Engineers move to river bank Construction activity Forces move toward river bank Forces move from opposite side of river behavior can be interpreted with respect to a highly local perspective as indicated in column 6 Local Interpretation. By assuming that the object is performing some higher level behavior progressively more global interpretations can be developed as indicated in columns 7 and 8. Individual battle space objects are typically organized into operational or functional-level units enabling observed behavior among groups of objects to be analyzed to generate higher level situation awareness products. Table categorizes the behavioral fragments of an engineer battalion engaged in a bridge-building operation and identifies sensors that could contribute to the recognition of each fragment. Situation awareness development involves the recursive refinement of a composite multiple level-of-abstraction scene description. Consequently the generalized fusion process model shown in Figure b supports the effective combination of 1 domain observables 2 a priori reasoning knowledge and 3 the multiple level-of-abstraction multiple-perspective fusion product. The process refinement loop controls both effective information combination and collection management. Each element of the process model is potentially sensitive to implicit non-sensor-derived domain knowledge. Fusion Process Model Extensions Recasting the generalized fusion process model within a biologically motivated framework establishes its relationship to the more familiar manual analysis paradigm. With suitable extensions this biological framework leads to the development of a problem-solving taxonomy that categorizes the spectrum of machine-based approaches to reasoning. Drawing on this taxonomy of problem solving approaches helps to Reveal underlying .