The Experts below are selected from a list of 2955 Experts worldwide ranked by ideXlab platform

Kim-kwang Raymond Choo - One of the best experts on this subject based on the ideXlab platform.

  • Google Drive: Forensic analysis of data remnants
    Journal of Network and Computer Applications, 2014
    Co-Authors: Darren Quick, Kim-kwang Raymond Choo
    Abstract:

    Cloud storage is an emerging challenge to digital forensic examiners. The services are increasingly used by consumers, business, and government, and can potentially store large amounts of data. The retrieval of digital evidence from cloud storage services (particularly from offshore providers) can be a challenge in a digital forensic investigation, due to virtualisation, lack of knowledge on location of digital evidence, privacy issues, and legal or jurisdictional boundaries. Google Drive is a popular service, providing users a cost-effective, and in some cases free, ability to access, store, collaborate, and disseminate data. Using Google Drive as a case study, artefacts were identified that are likely to remain after the use of cloud storage, in the context of the experiments, on a Computer Hard Drive and Apple iPhone3G, and the potential access point(s) for digital forensics examiners to secure evidence. Digital evidence can be stored in cloud storage services, such as Google Drive.Identification of potential data storage is a challenge to forensic examiners.Google Drive was examined in relation to data remnants on a PC and an iPhone.Investigation points include directory listings, prefetch, link and registry files.

  • Microsoft SkyDrive Cloud Storage Forensic Analysis
    Cloud Storage Forensics, 2014
    Co-Authors: Darren Quick, Ben Martini, Kim-kwang Raymond Choo
    Abstract:

    Cloud storage services such as the popular Microsoft® SkyDrive® provide both organizational and individual users a cost-effective, and in some cases free, way of accessing, storing, and disseminating data. The identification of digital evidence relating to cloud storage services can, however, be a challenge in a digital forensic investigation. Using SkyDrive as a case study, we identify the types of terrestrial artifacts that are likely to remain on a client’s machine (in the context of our experiments, Computer Hard Drive and iPhone), and where the access point(s) for digital forensic practitioners are, that will allow them to undertake steps to secure evidence in a timely fashion.

  • Google Drive: Forensic Analysis of Cloud Storage Data Remnants
    Journal of Network and Computer Applications, 2013
    Co-Authors: Darren Quick, Kim-kwang Raymond Choo
    Abstract:

    Cloud storage is an emerging challenge to digital forensic examiners. The services are increasingly used by consumers, business, and government, and can potentially store large amounts of data. The retrieval of digital evidence from cloud storage services (particularly from offshore providers) can be a challenge in a digital forensic investigation, due to virtualisation, lack of knowledge on location of digital evidence, privacy issues, and legal or jurisdictional boundaries. Google Drive is a popular service, providing users a cost-effective, and in some cases free, ability to access, store, collaborate, and disseminate data. Using Google Drive as a case study, artefacts were identified that are likely to remain after the use of cloud storage, in the context of the experiments; on a Computer Hard Drive and Apple iPhone3G, and the potential access point(s) for digital forensics examiners to secure evidence.

  • Digital droplets: Microsoft SkyDrive forensic data remnants
    Future Generation Computer Systems, 2013
    Co-Authors: Darren Quick, Kim-kwang Raymond Choo
    Abstract:

    Cloud storage services such as the popular Microsoft(C) SkyDrive(C) provide both organisational and individual users a cost-effective, and in some cases free, way of accessing, storing and disseminating data. The identification of digital evidence relating to cloud storage services can, however, be a challenge in a digital forensic investigation. Using SkyDrive as a case study, we identified the types of terrestrial artefacts that are likely to remain on a client's machine (in the context of our experiments; Computer Hard Drive and iPhone), and where the access point(s) for digital forensics examiners are, that will allow them to undertake steps to secure evidence in a timely fashion.

Darren Quick - One of the best experts on this subject based on the ideXlab platform.

  • Google Drive: Forensic analysis of data remnants
    Journal of Network and Computer Applications, 2014
    Co-Authors: Darren Quick, Kim-kwang Raymond Choo
    Abstract:

    Cloud storage is an emerging challenge to digital forensic examiners. The services are increasingly used by consumers, business, and government, and can potentially store large amounts of data. The retrieval of digital evidence from cloud storage services (particularly from offshore providers) can be a challenge in a digital forensic investigation, due to virtualisation, lack of knowledge on location of digital evidence, privacy issues, and legal or jurisdictional boundaries. Google Drive is a popular service, providing users a cost-effective, and in some cases free, ability to access, store, collaborate, and disseminate data. Using Google Drive as a case study, artefacts were identified that are likely to remain after the use of cloud storage, in the context of the experiments, on a Computer Hard Drive and Apple iPhone3G, and the potential access point(s) for digital forensics examiners to secure evidence. Digital evidence can be stored in cloud storage services, such as Google Drive.Identification of potential data storage is a challenge to forensic examiners.Google Drive was examined in relation to data remnants on a PC and an iPhone.Investigation points include directory listings, prefetch, link and registry files.

  • Microsoft SkyDrive Cloud Storage Forensic Analysis
    Cloud Storage Forensics, 2014
    Co-Authors: Darren Quick, Ben Martini, Kim-kwang Raymond Choo
    Abstract:

    Cloud storage services such as the popular Microsoft® SkyDrive® provide both organizational and individual users a cost-effective, and in some cases free, way of accessing, storing, and disseminating data. The identification of digital evidence relating to cloud storage services can, however, be a challenge in a digital forensic investigation. Using SkyDrive as a case study, we identify the types of terrestrial artifacts that are likely to remain on a client’s machine (in the context of our experiments, Computer Hard Drive and iPhone), and where the access point(s) for digital forensic practitioners are, that will allow them to undertake steps to secure evidence in a timely fashion.

  • Google Drive: Forensic Analysis of Cloud Storage Data Remnants
    Journal of Network and Computer Applications, 2013
    Co-Authors: Darren Quick, Kim-kwang Raymond Choo
    Abstract:

    Cloud storage is an emerging challenge to digital forensic examiners. The services are increasingly used by consumers, business, and government, and can potentially store large amounts of data. The retrieval of digital evidence from cloud storage services (particularly from offshore providers) can be a challenge in a digital forensic investigation, due to virtualisation, lack of knowledge on location of digital evidence, privacy issues, and legal or jurisdictional boundaries. Google Drive is a popular service, providing users a cost-effective, and in some cases free, ability to access, store, collaborate, and disseminate data. Using Google Drive as a case study, artefacts were identified that are likely to remain after the use of cloud storage, in the context of the experiments; on a Computer Hard Drive and Apple iPhone3G, and the potential access point(s) for digital forensics examiners to secure evidence.

  • Digital droplets: Microsoft SkyDrive forensic data remnants
    Future Generation Computer Systems, 2013
    Co-Authors: Darren Quick, Kim-kwang Raymond Choo
    Abstract:

    Cloud storage services such as the popular Microsoft(C) SkyDrive(C) provide both organisational and individual users a cost-effective, and in some cases free, way of accessing, storing and disseminating data. The identification of digital evidence relating to cloud storage services can, however, be a challenge in a digital forensic investigation. Using SkyDrive as a case study, we identified the types of terrestrial artefacts that are likely to remain on a client's machine (in the context of our experiments; Computer Hard Drive and iPhone), and where the access point(s) for digital forensics examiners are, that will allow them to undertake steps to secure evidence in a timely fashion.

Manoj Kumar Nagalla - One of the best experts on this subject based on the ideXlab platform.

  • Flow Characteristics of Rotating Disks Simulating a Computer Hard Drive
    Numerical Heat Transfer Part A-applications, 2005
    Co-Authors: Majid Molki, Manoj Kumar Nagalla
    Abstract:

    A computational effort was undertaken to study the fluid flow inside a Computer Hard Drive. Disk arrangements with multiple arms were considered inside a stationary enclosure. The results indicated large differences between circumferential velocities. In some cases, the slopes of the circumferential velocities were close to those represented by the solid-body rotation and the analytical solution for a thin fluid gap with one stationary and one rotating wall. The circumferential velocities were strongly affected by the arm length. The rotational speed and arm length had an increasing effect on the viscous power dissipation of the disks.

Kreutz-delgadokenneth - One of the best experts on this subject based on the ideXlab platform.

Pitakrat Teerat - One of the best experts on this subject based on the ideXlab platform.

  • Architecture-aware online failure prediction for software systems
    2018
    Co-Authors: Pitakrat Teerat
    Abstract:

    Failures at runtime in complex software systems are inevitable because these systems usually contain a large number of components. Having all components working perfectly at the same time is, if at all possible, very difficult. Hardware components can fail and software components can still have hidden faults waiting to be triggered at runtime and cause the system to fail. Existing online failure prediction approaches predict failures by observing the errors or the symptoms that indicate looming problems. This observable data is used to create models that can predict whether the system will transition into a failing state. However, these models usually represent the whole system as a monolith without considering their internal components. This thesis proposes an architecture-aware online failure prediction approach, called Hora. The Hora approach improves online failure prediction by combining the results of failure prediction with the architectural knowledge about the system. The task of failure prediction is split into predictingthe failure of each individual component, in contrast to predicting the whole system failure. Suitable prediction techniques can be employed for different types of components. The architectural knowledge is used to deduce the dependencies between components which can reflect how a failure of one component can affect the others. The failure prediction and the component dependencies are combined into one model which employs Bayesian network theory to represent failure propagation. The combined model is continuously updated at runtime and makes predictions for individual components, as well as inferring their effects on other components and the whole system. The evaluation of component failure prediction is performed on three different experiments. The predictors are applied to predict component failures in a microservice-based application, critical events in Blue Gene/L superComputer, and Computer Hard Drive failures. The results show that the failures of individual components can be accurately predicted. The evaluation of the whole Hora approach is carried out on a microservice-based application. The results show that the Hora approach, which combines component failure prediction and architectural knowledge, can predict the component failures, their effects on other parts of the system, and the failures of the whole service. The Hora approach outperforms the monolithic approach that does not consider architectural knowledge and can improve the area under the Receiver Operating Characteristic (ROC) curve by 9.9%

  • Architecture-aware Online Failure Prediction for Software Systems
    2017
    Co-Authors: Pitakrat Teerat
    Abstract:

    Failures at runtime in complex software systems are inevitable because these systems usually contain a large number of components. Having all components working perfectly at the same time is, if at all possible, very difficult. Hardware components can fail and software components can still have hidden faults waiting to be triggered at runtime and cause the system to fail. Existing online failure prediction approaches predict failures by observing the errors or the symptoms that indicate looming problems. This observable data is used to create models that can predict whether the system will transition into a failing state. However, these models usually represent the whole system as a monolith without considering their internal components. This dissertation proposes an architecture-aware online failure prediction approach, called Hora. The Hora approach improves online failure prediction by combining the results of failure prediction with the architectural knowledge about the system. The task of failure prediction is split into predicting the failure of each individual component, in contrast to predicting the whole system failure. Suitable prediction techniques can be employed for different types of components. The architectural knowledge is used to deduct the dependencies between components which can reflect how a failure of one component can affect the others. The failure prediction and the component dependencies are combined into one model which employs Bayesian network theory to represent failure propagation. The combined model is continuously updated at runtime and makes predictions for individual components, as well as inferring their effects on other components and the whole system. The evaluation of component failure prediction is performed on three different experiments. The predictors are applied to predict component failures in a microservice-based application, critical events in Blue Gene/L superComputer, and Computer Hard Drive failures. The results show that the failures of individual components can be accurately predicted. The evaluation of the whole Hora approach is carried out on a microservice-based application. The results show that the Hora approach, which combines component failure prediction and architectural knowledge, can predict the component failures, their effects on other parts of the system, and the failures of the whole service. The Hora approach outperforms the monolithic approach that does not consider architectural knowledge and can improve the area under the Receiver Operating Characteristic (ROC) curve by 9.9%