A Method and a Case Study for the Selection of the Best Available Tool for Mobile Device Forensics Using Decision Analysis

The omnipresence of mobile devices (or small scale digital devices - SSDD) and more importantly the utility of their associated applications for our daily activities, which range from financial transactions to learning, and from entertainment to distributed social presence, create an abundance of digital evidence for each individual. Some of the evidence may be a result of illegal activities that need to be identified, understood and eventually prevented in the future. There are numerous tools for acquiring and analyzing digital evidence extracted from mobile devices. The diversity of SSDDs, types of evidence generated and the number of tools used to uncover them posit a rather complex and challenging problem of selecting the best available tool for the extraction and the subsequent analysis of the evidence gathered from a specific digital device. Failing to select the best tool may easily lead to incomplete and or improper extraction, which eventually may violate the integrity of the digital evidence and diminish its probative value. Moreover, the compromised evidence may result in erroneous analysis, incorrect interpretation, and wrong conclusions which may eventually compromise the right of a fair trial. Hence, a digital forensics investigator has to deal with the complex decision problem from the very start of the investigative process called preparatory phase. The problem could be addressed and possibly solved by using multi criteria decision analysis. The performance of the tool for extracting a specific type of digital evidence, and the relevance of that type of digital evidence to the investigative problem are the two central factors for selecting the best available tool, which we advocate in our work. In this paper we explain the method used and showcase a case study by evaluating two tools using two mobile devices to demonstrate the utility of our proposed approach. The results indicated that XRY (Alt1) dominates UFED (Alt2) for most of the cases after balancing the requirements for both performance and relevance

Decideit 3.0: Software for Second-Order Based Decision Evaluations

In this paper, we discuss representation and evaluation in the DecideIT 3.0 decision tool which is based on a belief mass interpretation of the background information. The decision components are imprecise in terms of intervals and qualitative estimates and we emphasise how multiplicative and additive aggregations influence the resulting belief distribution over the expected values

Application of Imprecise Decision Modeling for Regional Development Policies in Indonesia

Regional Development encompasses many aspect of economic, social, and environmental attributes. In the context of developing country, the decision to fulfill these attributes are often hindered by lack of clear development scenarios and constraints. This study is an attempt to capture the complexity of decision makers for regional development scenarios using imprecise decision modeling (IDM) by incorporating imprecise information and uncertainties. A series of social, economic and environmental criteria based on agreement from multi stakeholders dialogues were developed along with four policy development scenarios. Results from such a modeling provides variety of decision alternatives based on probabilities and risk assessment associated with achieving policy objectives

A second-order-based decision tool for evaluating decisions under conditions of severe uncertainty

The requirement to assign precise numerical values to model entities such as criteria weights, probabilities, and utilities is too strong in most real-life decision situations, and hence alternative representations and evaluation mechanisms are important to consider. In this paper, we discuss the DecideIT 3.0 state-of-the-art software decision tool and demonstrate its functionality using a real-life case. The tool is based on a belief mass interpretation of the decision information, where the components are imprecise by means of intervals and qualitative estimates, and we discuss how multiplicative and additive aggregations influence the resulting distribution over the expected values

Analysis of the potentials of multi criteria decision analysis methods to conduct sustainability assessment

Sustainability assessments require the management of a wide variety of information types, parameters and uncertainties. Multi criteria decision analysis (MCDA) has been regarded as a suitable set of methods to perform sustainability evaluations as a result of its flexibility and the possibility of facilitating the dialogue between stakeholders, analysts and scientists. However, it has been reported that researchers do not usually properly define the reasons for choosing a certain MCDA method instead of another. Familiarity and affinity with a certain approach seem to be the drivers for the choice of a certain procedure. This review paper presents the performance of five MCDA methods (i.e. MAUT, AHP, PROMETHEE, ELECTRE and DRSA) in respect to ten crucial criteria that sustainability assessments tools should satisfy, among which are a life cycle perspective, thresholds and uncertainty management, software support and ease of use. The review shows that MAUT and AHP are fairly simple to understand and have good software support, but they are cognitively demanding for the decision makers, and can only embrace a weak sustainability perspective as trade-offs are the norm. Mixed information and uncertainty can be managed by all the methods, while robust results can only be obtained with MAUT. ELECTRE, PROMETHEE and DRSA are non-compensatory approaches which consent to use a strong sustainability concept, accept a variety of thresholds, but suffer from rank reversal. DRSA is less demanding in terms of preference elicitation, is very easy to understand and provides a straightforward set of decision rules expressed in the form of elementary “if … then …” conditions. Dedicated software is available for all the approaches with a medium to wide range of results capability representation. DRSA emerges as the easiest method, followed by AHP, PROMETHEE and MAUT, while ELECTRE is regarded as fairly difficult. Overall, the analysis has shown that most of the requirements are satisfied by the MCDA methods (although to different extents) with the exclusion of management of mixed data types and adoption of life cycle perspective which are covered by all the considered approaches

A Multi-Criteria Approach to Decision Making in Broadband Technology Selection

A new European Union regulatory framework for the telecom sector has been under a process of transposition to national laws by its member states that should have been completed by the end of 2020, notwithstanding some delays. A core purpose of the regulatory framework is to guarantee that most citizens will have access to very fast Internet connections, capable of 100 Mbps download link speed, regardless of where they live. According to this new framework, in areas where the market does not deliver, governments are to launch public tenders for the deployment, maintenance, and operations of network infrastructure as well as services, and public funds should be used to support the deployment of these broadband networks in less densely populated areas. Needless to say, public tenders of this nature are subject to different criteria when it comes to candidate evaluation. In this paper, we present a decision model for the selection of operators to deploy and maintain broadband networks in scarcely populated areas, taking into consideration infrastructure costs, the technical quality offered by the solutions, and the credibility of the candidates. We suggest an integrated multi-stakeholder multi-criteria approach and demonstrate how it can be used in this complex area and find that in the example provided, taking a relevant set of criteria into the analysis, optical fibre networks hold much higher chances to be used in these public tenders compared to networks based on the broadly favoured 5G technology

Application of a structured decision‐making process in cryospheric hazard planning: Case study of Bering Glacier surges on local state planning in Alaska

Surging glaciers are glaciers that experience rapidly accelerated glacier flow over a comparatively short period of time. Though relatively rare worldwide, Alaska is home to the largest number of surge-type glaciers globally. However, their impact on the broader socioecological system in the state is both poorly understood and under-researched, which poses a challenge in developing appropriate sustainability decisions in Alaska. We investigated how the surge patterns of the Bering Glacier in Alaska have potentially devastating effects on the local ecological biodiversity of its watershed via a structured decision-making analysis of the different possible consequences. Specifically, this analysis was conducted to explore the various outcomes of a Bering Glacier surge particularly if humans have an increased presence near the glacier due to the area potentially becoming a state park. This work explored the benefits of applying a risk and decision analytical framework in a cryosphere context, to better understand the socioeconomic impact of glacier surges. This is a novel approach in which a decision analysis tool was used to better understand an environmental sustainability challenge, offering an innovative method to support the achievement of the United Nations Sustainability Development Goals in Alaska. We therefore emphasise the need for integrated biophysical and socioeconomic analyses when it comes to understanding glacier hazards. Our research highlights the importance of understanding and researching biophysical changes as well as using a structured decision-making process for complicated hazard planning scenarios, exemplified via glaciated regions in Alaska, in order to create adaptation strategies that are sustainable and encompass the range of possible outcomes