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In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
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An elastic collision is one that conserves both internal kinetic energy and momentum. Internal kinetic energy is the sum of the kinetic energies of the objects in a system. Truly elastic collisions can only be achieved with subatomic particles, such as electrons striking nuclei. Macroscopic collisions can be very nearly, but not quite, elastic, as some kinetic energy is always converted into other forms of energy such as heat transfer due to friction and sound. An example of a nearly...
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Elastic collision of a system demands conservation of both momentum and kinetic energy. To solve problems involving one-dimensional elastic collisions between two objects, the equations for conservation of momentum and conservation of internal kinetic energy can be used. For the two objects, the sum of momentum before the collision equals the total momentum after the collision. An elastic collision conserves internal kinetic energy, and so the sum of kinetic energies before the collision equals...
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Three-Dimensional Force System:Problem Solving01:30

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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MTD-Diorama: Moving Target Defense Visualization Engine for Systematic Cybersecurity Strategy Orchestration.

Se-Han Lee1,2, Kyungshin Kim3, Youngsoo Kim4

  • 1SysCore Lab., Convergence Engineering for Intelligent Drone, Sejong University, Seoul 05006, Republic of Korea.

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Summary
This summary is machine-generated.

Moving target defense (MTD) strategies are crucial for cybersecurity. This study introduces MTD-Diorama, a visualization tool that systematically links cyberattacks to MTD components for enhanced defense configuration.

Keywords:
classificationcyberattack surfacedata visualizationmoving target defense

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Information Technology

Background:

  • Modern society increasingly relies on computing systems, making them vulnerable to diverse and intelligent cyberattacks.
  • Existing moving target defense (MTD) strategies lack systematic component indicators, hindering effective configuration against evolving threats.

Purpose of the Study:

  • To address the lack of systematic MTD strategy configuration by developing a method to correlate cyberattack information with MTD components.
  • To design and implement a data visualization engine for identifying attack surfaces and corresponding MTD strategies.

Main Methods:

  • Survey and analysis of existing cyberattack information and MTD strategy research.
  • Configuration of a component dataset based on analyzed data.
  • Identification of correlations between cyberattack and MTD component datasets.
  • Design and implementation of the MTD-Diorama data visualization engine.

Main Results:

  • A comprehensive component dataset for MTD strategies was created.
  • The MTD-Diorama engine effectively visualizes the correlation between cyberattack surfaces and MTD components.
  • Researchers can now systematically identify and select MTD strategies relevant to specific cyber threats.

Conclusions:

  • MTD-Diorama facilitates the convenient identification of attack surfaces and corresponding MTD strategies.
  • The tool enables the configuration of more systematic and universally applicable MTD strategies.
  • This approach enhances the adaptability of MTD strategies across various computing systems.