Ontology for Complex Mission Scenarios in Forensic Computing

Nogueira, José Helano Matos; Vasconcelos, Wamberto

doi:10.5769/j200801004

Cited by 6 publications

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“…Therefore, we created the Autonomic Forensic Ontology (AFO) to map the forensic application domain (Figure 3). Among the advantages that have been presented in literature for the adoption of ontologies [13,14,15] In AFO, we created an autonomic representation the four basic characteristics of self-management: self-configuring, self-optimizing, self-healing, and self-protecting. The ellipses describe domain concepts while the arrows among the concepts are the relationships.…”

Section: Why Would Someone Wantmentioning

confidence: 99%

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Autonomic Forensics a New Frontier to Computer Crime Investigation Management

Nogueira¹,

Júnior

2009

IJoFCS

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The remarkable growth of investigations and exams numbers accomplished in computer crime investigation, as well as the integration of a variety of different technologies with the goal of providing quality of services, has transformed computer forensic management into an extremely complex activity. As the complexity continues to increase, it will be necessary to delegate management tasks to the machines themselves. The forensic systems and tools will need to execute activities that are currently performed by experts and investigators, in a fast and transparent way, with few or no mistakes. These will be the autonomic forensics: a new paradigm that defines the method of management. Hence, the main challenge of this work is to use autonomic computing to create this paradigm and apply it to computer forensic management.

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Section: Why Would Someone Wantmentioning

confidence: 99%

“…Relations labeled "is-a", describe the existence of specialization relations (subclasses), which create hierarchies among the concepts. In this way, we intend to go in the same direction of author [13].…”

Section: Why Would Someone Wantmentioning

confidence: 99%

Autonomic Forensics a New Frontier to Computer Crime Investigation Management

Nogueira¹,

Júnior

2009

IJoFCS

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“…The use of ontologies to represent devices, systems etc., such as those described by Brinson et al (2006) and Nogueira & Vasconcelos (2008), may be used to good effect in implementation of a DFRMS. A DFRIMM stores a large amount of information about elements such as devices, systems and staff.…”

Section: Future Workmentioning

confidence: 99%

The architecture of a digital forensic readiness management system

Reddy¹,

Venter²

2013

Computers & Security

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A coordinated approach to digital forensic readiness (DFR) in a large organisation requires the management and monitoring of a wide variety of resources, both human and technical. The resources involved in DFR in large organisations typically include staff from multiple departments and business units, as well as network infrastructure and computing platforms. The state of DFR within large organisations may therefore be adversely affected if the myriad human and technical resources involved are not managed in an optimal manner. This paper contributes to DFR by proposing the novel concept of a digital forensic readiness management system (DFRMS). The purpose of a DFRMS is to assist large organisations in achieving an optimal level of management for DFR. In addition to this, we offer an architecture for a DFRMS. This architecture is based on requirements for DFR that we ascertained from an exhaustive review of the DFR literature. We describe the architecture in detail and show that it meets the requirements set out in the DFR literature. The merits and disadvantages of the architecture are also discussed. Finally, we describe and explain an early prototype of a DFRMS.

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Reduce the Warfighters’ Cognitive Burden

Maher¹,

Orlando²

2019

Cognitive Computing – ICCC 2019

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As the DoD and Military acquisitions shift the paradigm from antiquated software capabilities to artificial intelligence (AI) and machine learning (ML) capabilities, they need to ensure key infrastructure (big data and knowledge artifacts) is also mature enough to handle the transition. Before any organizations or domains develop or adopt a strategy based on AI, ML, or any other form of intelligent system, they need to do a self-assessment that answers three key questions. How well does your organization/domain share knowledge? What is the state of your data? Do you understand the cognitive requirements? Answering and understanding these questions is the key to knowing where you are now (point A) so you can take actionable and progressive steps towards where you want to go (point B) to make your organization or domain AIcapable.To provide the answers and/or a path to solving the questions above we execute a Mission Engineering (ME) process that analyzes the organization's or domain's state of readiness. The ME process is a detailed analysis that provides an assessment of how the people, systems, knowledge, data, and processes are aligned to the operational outcomes. ME adds a layer of operational viability to existing engineering processes with the goal of ensuring the program enhances knowledge sharing, reduces cognitive burdens, and increases semantic understanding.

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Ontology for Complex Mission Scenarios in Forensic Computing

Cited by 6 publications

References 0 publications

Autonomic Forensics a New Frontier to Computer Crime Investigation Management

Autonomic Forensics a New Frontier to Computer Crime Investigation Management

The architecture of a digital forensic readiness management system

Reduce the Warfighters’ Cognitive Burden

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