Modelling Situation Awareness Information and System Requirements for the Mission using Goal-Oriented Task Analysis Approach

2014 ◽  
pp. 460-478
Author(s):  
Cyril Onwubiko
Keyword(s):  
Situation Awareness ◽  
Task Analysis ◽  
Situational Awareness ◽  
Computer Network ◽  
Valuable Insight ◽  
Test Scenario ◽  
Agent Oriented Software Engineering ◽  
Awareness Information ◽  
System Requirements ◽  
Security Test

This chapter describes work on modelling situational awareness information and system requirements for the mission. Developing this model based on Goal-Oriented Task Analysis representation of the mission using an Agent Oriented Software Engineering methodology advances current information requirement models because it provides valuable insight on how to effectively achieve the mission’s requirements (information, systems, networks, and IT infrastructure), and offers enhanced situational awareness within the Computer Network Defence environment. Further, the modelling approach using Secure Tropos is described, and model validation using a security test scenario is discussed.

Modelling Situation Awareness Information and System Requirements for the Mission using Goal-Oriented Task Analysis Approach

2012 ◽  
pp. 245-262
Author(s):  
Cyril Onwubiko
Keyword(s):  
Situation Awareness ◽  
Task Analysis ◽  
Situational Awareness ◽  
Computer Network ◽  
Valuable Insight ◽  
Test Scenario ◽  
Agent Oriented Software Engineering ◽  
Awareness Information ◽  
System Requirements ◽  
Security Test

This chapter describes work on modelling situational awareness information and system requirements for the mission. Developing this model based on Goal-Oriented Task Analysis representation of the mission using an Agent Oriented Software Engineering methodology advances current information requirement models because it provides valuable insight on how to effectively achieve the mission’s requirements (information, systems, networks, and IT infrastructure), and offers enhanced situational awareness within the Computer Network Defence environment. Further, the modelling approach using Secure Tropos is described, and model validation using a security test scenario is discussed.

Adaptive Visualization of Complex Networks with FocalPoint

2016 ◽  
Vol 60 (1) ◽  
pp. 233-237 ◽  
Author(s):  
Catherine Inibhunu ◽  
Scott Langevin
Keyword(s):  
Complex Networks ◽  
Cognitive Load ◽  
Situation Awareness ◽  
Operations Management ◽  
Computational Models ◽  
Situational Awareness ◽  
Computer Network ◽  
Global Network ◽  
Optimum Level ◽  
Information Structures

Maintaining situational awareness of a dynamic global computer network that consists of ten to hundreds of thousands of computers is a complex task for cyber administrators and operators looking to understand, plan and conduct operations in real time. Currently, cyber specialists must manually navigate complex networks by continuous cycles of overviews, drilldowns and manually mapping network incidents to mission impact. This is inefficient as manually maneuvering of network data is laborious, induces cognitive overload, and is prone to errors caused by distractive information resulting in important information and impacts not being seen. We are investigating “FocalPoint” an adaptive level of detail (LOD) recommender system tailored for hierarchical network information structures. FocalPoint reasons about contextual information associated with the network, user task, and user cognitive load to tune the presentation of network visualization displays to improve user performance in perception, comprehension and projection of current situational awareness. Our system is applied to two complex information constructs important to dynamic cyber network operations: network maps and attack graphs. The key innovations include: (a) context-aware automatic tailoring of complex network views, (b) multi-resolution hierarchical graph aggregation, (c) incorporation of new computational models for adaptive-decision making on user tasks, cost/benefit utility and human situation awareness, and (d) user interaction techniques to integrate recommendations into the network viewing system. Our aim is to have a direct impact on planning and operations management for complex networks by; overcoming information overload, preventing tunnel vision, reducing cognitive load, and increasing time available to focus on optimum level of details of the global network space and missions.

An Alternative Framework for Research on Situational Awareness in Computer Network Defense

2012 ◽  
pp. 71-85 ◽  
Author(s):  
Eric McMillan ◽  
Michael Tyworth
Keyword(s):  
Situation Awareness ◽  
Situational Awareness ◽  
Computer Network ◽  
Levels Of Analysis ◽  
Network Defense ◽  
Human In The Loop ◽  
Research Account ◽  
Computer Network Defense ◽  
Level Of Analysis ◽  
New Framework

In this chapter the authors present a new framework for the study of situation awareness in computer network defense (cyber-SA). While immensely valuable, the research to date on cyber-SA has overemphasized an algorithmic level of analysis to the exclusion of the human actor. Since situation awareness, and therefore cyber-SA, is a human cognitive process and state, it is essential that future cyber-SA research account for the human-in-the-loop. To that end, the framework in this chapter presents a basis for examining cyber-SA at the cognitive, system, work, and enterprise levels of analysis. In describing the framework, the authors present examples of research that are emblematic of each type of analysis.

An Alternative Framework for Research on Situational Awareness in Computer Network Defense

2014 ◽  
pp. 322-336
Author(s):  
Eric McMillan ◽  
Michael Tyworth
Keyword(s):  
Situation Awareness ◽  
Situational Awareness ◽  
Computer Network ◽  
Levels Of Analysis ◽  
Network Defense ◽  
Human In The Loop ◽  
Research Account ◽  
Computer Network Defense ◽  
Level Of Analysis ◽  
New Framework

In this chapter the authors present a new framework for the study of situation awareness in computer network defense (cyber-SA). While immensely valuable, the research to date on cyber-SA has overemphasized an algorithmic level of analysis to the exclusion of the human actor. Since situation awareness, and therefore cyber-SA, is a human cognitive process and state, it is essential that future cyber-SA research account for the human-in-the-loop. To that end, the framework in this chapter presents a basis for examining cyber-SA at the cognitive, system, work, and enterprise levels of analysis. In describing the framework, the authors present examples of research that are emblematic of each type of analysis.

scholarly journals Semantics-Driven Remote Sensing Scene Understanding Framework for Grounded Spatio-Contextual Scene Descriptions

2021 ◽  
Vol 10 (1) ◽  
pp. 32
Author(s):  
Abhishek V. Potnis ◽  
Surya S. Durbha ◽  
Rajat C. Shinde
Keyword(s):  
Remote Sensing ◽  
Domain Knowledge ◽  
Situational Awareness ◽  
Scene Understanding ◽  
Graph Representation ◽  
Observation Data ◽  
Test Scenario ◽  
Semantic Web Technologies ◽  
Earth Observation Data ◽  
Contextual Domain

Earth Observation data possess tremendous potential in understanding the dynamics of our planet. We propose the Semantics-driven Remote Sensing Scene Understanding (Sem-RSSU) framework for rendering comprehensive grounded spatio-contextual scene descriptions for enhanced situational awareness. To minimize the semantic gap for remote-sensing-scene understanding, the framework puts forward the transformation of scenes by using semantic-web technologies to Remote Sensing Scene Knowledge Graphs (RSS-KGs). The knowledge-graph representation of scenes has been formalized through the development of a Remote Sensing Scene Ontology (RSSO)—a core ontology for an inclusive remote-sensing-scene data product. The RSS-KGs are enriched both spatially and contextually, using a deductive reasoner, by mining for implicit spatio-contextual relationships between land-cover classes in the scenes. The Sem-RSSU, at its core, constitutes novel Ontology-driven Spatio-Contextual Triple Aggregation and realization algorithms to transform KGs to render grounded natural language scene descriptions. Considering the significance of scene understanding for informed decision-making from remote sensing scenes during a flood, we selected it as a test scenario, to demonstrate the utility of this framework. In that regard, a contextual domain knowledge encompassing Flood Scene Ontology (FSO) has been developed. Extensive experimental evaluations show promising results, further validating the efficacy of this framework.

scholarly journals Effects of User Interfaces on Take-Over Performance: A Review of the Empirical Evidence

Information ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 162
Author(s):  
Soyeon Kim ◽  
René van Egmond ◽  
Riender Happee
Keyword(s):  
User Interface ◽  
Situation Awareness ◽  
User Interfaces ◽  
Situational Awareness ◽  
Selection Criterion ◽  
Auditory Modality ◽  
Automated Driving ◽  
Automated Vehicle ◽  
Positive Effects ◽  
High Urgency

In automated driving, the user interface plays an essential role in guiding transitions between automated and manual driving. This literature review identified 25 studies that explicitly studied the effectiveness of user interfaces in automated driving. Our main selection criterion was how the user interface (UI) affected take-over performance in higher automation levels allowing drivers to take their eyes off the road (SAE3 and SAE4). We categorized user interface (UI) factors from an automated vehicle-related information perspective. Short take-over times are consistently associated with take-over requests (TORs) initiated by the auditory modality with high urgency levels. On the other hand, take-over requests directly displayed on non-driving-related task devices and augmented reality do not affect take-over time. Additional explanations of take-over situation, surrounding and vehicle information while driving, and take-over guiding information were found to improve situational awareness. Hence, we conclude that advanced user interfaces can enhance the safety and acceptance of automated driving. Most studies showed positive effects of advanced UI, but a number of studies showed no significant benefits, and a few studies showed negative effects of advanced UI, which may be associated with information overload. The occurrence of positive and negative results of similar UI concepts in different studies highlights the need for systematic UI testing across driving conditions and driver characteristics. Our findings propose future UI studies of automated vehicle focusing on trust calibration and enhancing situation awareness in various scenarios.

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