Now showing 1 - 5 of 5
  • Publication
    Evaluation of Digital Forensic Process Models with Respect to Digital Forensics as a Service
    (Academic Conferences And Publishing International Limited, 2017-06-12) ; ;
    Digital forensic science is very much still in its infancy, but is becoming increasingly invaluable to investigators. A popular area for research is seeking a standard methodology to make the digital forensic process accurate, robust, and efficient. The first digital forensic process model proposed contains four steps: Acquisition, Identification, Evaluation and Admission. Since then, numerous process models have been proposed to explain the steps of identifying, acquiring, analysing, storage, and reporting on the evidence obtained from various digital devices. In recent years, an increasing number of more sophisticated process models have been proposed. These models attempt to speed up the entire investigative process or solve various of problems commonly encountered in the forensic investigation. In the last decade, cloud computing has emerged as a disruptive technological concept, and most leading enterprises such as IBM, Amazon, Google, and Microsoft have set up their own cloud-based services. In the field of digital forensic investigation, moving to a cloudbased evidence processing model would be extremely beneficial and preliminary attempts have been made in its implementation. Moving towards a Digital Forensics as a Service model would not only expedite the investigative process, but can also result in significant cost savings - freeing up digital forensic experts and law enforcement personnel to progress their caseload. This paper aims to evaluate the applicability of existing digital forensic process models and analyse how each of these might apply to a cloud-based evidence processing paradigm.
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  • Publication
    Automated Artefact Relevancy Determination from Artefact Metadata and Associated Timeline Events
    Case-hindering, multi-year digital forensic evidence backlogs have become commonplace in law enforcement agencies throughout the world. This is due to an ever-growing number of cases requiring digital forensic investigation coupled with the growing volume of data to be processed per case. Leveraging previously processed digital forensic cases and their component artefact relevancy classifications can facilitate an opportunity for training automated artificial intelligence based evidence processing systems. These can significantly aid investigators in the discovery and prioritisation of evidence. This paper presents one approach for file artefact relevancy determination building on the growing trend towards a centralised, Digital Forensics as a Service (DFaaS) paradigm. This approach enables the use of previously encountered pertinent files to classify newly discovered files in an investigation. Trained models can aid in the detection of these files during the acquisition stage, i.e., during their upload to a DFaaS system. The technique generates a relevancy score for file similarity using each artefact's filesystem metadata and associated timeline events. The approach presented is validated against three experimental usage scenarios.
      49Scopus© Citations 9
  • Publication
    SoK: Exploring the State of the Art and the Future Potential of Artificial Intelligence in Digital Forensic Investigation
    Multi-year digital forensic backlogs have become commonplace in law enforcement agencies throughout the globe. Digital forensic investigators are overloaded with the volume of cases requiring their expertise compounded by the volume of data to be processed. Artificial intelligence is often seen as the solution to many big data problems. This paper summarises existing artificial intelligence based tools and approaches in digital forensics. Automated evidence processing leveraging artificial intelligence based techniques shows great promise in expediting the digital forensic analysis process while increasing case processing capacities. For each application of artificial intelligence highlighted, a number of current challenges and future potential impact is discussed.
      53Scopus© Citations 33
  • Publication
    EviPlant: An Efficient Digital Forensic Challenge Creation, Manipulation, and Distribution Solution
    (Elsevier, 2017-03-21) ; ;
    Education and training in digital forensics requires a variety of suitable challenge corpora containing realistic features including regular wear-and-tear, background noise, and the actual digital traces to be discovered during investigation. Typically, the creation of these challenges requires overly arduous effort on behalf of the educator to ensure their viability. Once created, the challenge image needs to be stored and distributed to a class for practical training. This storage and distribution step requires significant resources and time and may not even be possible in an online/distance learning scenario due to the data sizes involved. As part of this paper, we introduce a more capable methodology and system to current approaches. EviPlant is a system designed for the efficient creation, manipulation, storage and distribution of challenges for digital forensics education and training. The system relies on the initial distribution of base disk images, i.e., images containing solely bare operating systems. In order to create challenges for students, educators can boot the base system, emulate the desired activity and perform a diffing of resultant image and the base image. This diffing process extracts the modified artefacts and associated metadata and stores them in an evidence package. Evidence packages can be created for different personas, different wear-and-tear, different emulated crimes, etc., and multiple evidence packages can be distributed to students and integrated with the base images. A number of advantages and additional functionality over the current approaches are discussed that emerge as a result of using EviPlant.
      423Scopus© Citations 14
  • Publication
    Deduplicated Disk Image Evidence Acquisition and Forensically-Sound Reconstruction
    The ever-growing backlog of digital evidence waiting for analysis has become a significant issue for law enforcement agencies throughout the world. This is due to an increase in the number of cases requiring digital forensic analysis coupled with the increasing volume of data to process per case. This has created a demand for a paradigm shift in the method that evidence is acquired, stored, and analyzed. The ultimate goal of the research presented in this paper is to revolutionize the current digital forensic process through the leveraging of centralized deduplicated acquisition and processing approach. Focusing on this first step in digital evidence processing, acquisition, a system is presented enabling deduplicated evidence acquisition with the capability of automated, forensically-sound complete disk image reconstruction. As the number of cases acquired by the proposed system increases, the more duplicate artifacts will be encountered, and the more efficient the processing of each new case will become. This results in a time saving for digital investigators, and provides a platform to enable non-expert evidence processing, alongside the benefits of reduced storage and bandwidth requirements.
      19Scopus© Citations 7