Evaluation of forensic DNA traces when propositions of interest relate to activities: analysis and discussion of recurrent concerns

When forensic scientists evaluate and report on the probative strength of single DNA traces, they commonly rely on only one number, expressing the rarity of the DNA profile in the population of interest. This is so because the focus is on propositions regarding the source of the recovered trace material, such as “the person of interest is the source of the crime stain.” In particular, when the alternative proposition is “an unknown person is the source of the crime stain,” one is directed to think about the rarity of the profile. However, in the era of DNA profiling technology capable of producing results from small quantities of trace material (i.e., non-visible staining) that is subject to easy and ubiquitous modes of transfer, the issue of source is becoming less central, to the point that it is often not contested. There is now a shift from the question “whose DNA is this?” to the question “how did it get there?” As a consequence, recipients of expert information are now very much in need of assistance with the evaluation of the meaning and probative strength of DNA profiling results when the competing propositions of interest refer to different activities. This need is widely demonstrated in day-to-day forensic practice and is also voiced in specialized literature. Yet many forensic scientists remain reluctant to assess their results given propositions that relate to different activities. Some scientists consider evaluations beyond the issue of source as being overly speculative, because of the lack of relevant data and knowledge regarding phenomena and mechanisms of transfer, persistence and background of DNA. Similarly, encouragements to deal with these activity issues, expressed in a recently released European guideline on evaluative reporting (Willis et al., 2015), which highlights the need for rethinking current practice, are sometimes viewed skeptically or are not considered feasible. In this discussion paper, we select and discuss recurrent skeptical views brought to our attention, as well as some of the alternative solutions that have been suggested. We will argue that the way forward is to address now, rather than later, the challenges associated with the evaluation of DNA results (from small quantities of trace material) in light of different activities to prevent them being misrepresented in court

Archetypes of Wildfire Arsonists: An Approach by Using Bayesian Networks

Wildfires are a phenomenon of great importance because of their environmental and economic consequences, as well as the human losses they cause. The rate of resolution of arson-caused wildfires is extremely low when compared to other criminal activities. This fact highlights the importance of developing methodologies to assist investigators in the criminal profiling. For that we propose the use of Bayesian networks (BNs), which are a methodology belonging to the field of machine learning. BNs are probabilistic models that have only recently been applied to criminal profiling.We learn a BN model from real data of solved arson-caused wildfires in Spain, and after validation we use it to construct archetypes of the forest fires/arsonists with the aim of better understanding of this phenomenon and help in the task of identification of the culprits. We characterize five different archetypes around author motivation from a quantitative and objective point of view, which are in correspondence with the modes of operation in criminal activities of Shye

Ego-Centric Approach For Predicting Fraudulent Collaboration In Telecommunication

Recently, there has been a surge of interest in social networks ever since the tragic event of September 11, 2001 attacks on The World Trade Center in the United States. E-mail traffic, disease transmission, criminal activity and communication network can all be modeled as social networks. Ego-centric is an approach used in social network analysis. In the social network parlance, the focused person is referred to as “ego” and his or her affiliate, friend or relative is known as “alters”. An egocentered network positions an individual at the center of a social network team for the person to traverse his or her relationships with other team members. Through social network analysis, enforcement officers can recognize how information flows through social ties, how people acquire information and resources and how cleavages and coalitions operate. In this thesis, based on social network theories and link analysis; a data mining technology, a social network analysis model is developed to facilitate in detecting fraudulent collaboration, after which an evaluation is then made on the performance of the developed model. This study aims to explore the usage of embedding social network analysis functions into fraudulent collaboration investigation in call details records. Two types of social network data collection approaches are discussed; (i) social network with centrality measures values and (ii) social network without centrality measures values, where the first approach is based on the previous research while the second is based on the current research experimented. Performance of the models produced by both approaches are measured based on a standard measurement. Performance is tested using statistical models which include Bayesian Network, Naïve Bayesian and Binary Logistic Regression Model is performed. These statistical models are used in order to prove and determine which model is the ‘best’ that can produce a better prediction of fraudulent collaboration. The outcome of this research is thought to be of help to any enforcement agency or relevant authority in its future operations or measures to detect fraudulent activity in social networks

Helping with inquiries: theory and practice in forensic science

This thesis investigates the reasoning practices of forensic scientists, with specific focus on the application of the Bayesian form of probabilistic reasoning to forensic science matters. Facilitated in part by the insights of evidence scholarship, Bayes Theorem has been advocated as an essential resource for the interpretation and evaluation of forensic evidence, and has been used to support the production of specific technologies designed to aid forensic scientists in these processes. In the course of this research I have explored the ways in which Bayesian reasoning can be regarded as a socially constructed collection of practices, despite proposals that it is simply a logical way to reason about evidence. My data are drawn from two case studies. In the first, I demonstrate how the Bayesian algorithms used for the interpretation of complex DNA profiles are themselves elaborately constructed devices necessary for the anchoring of scientific practice to forensic contexts. In the second case study, an investigation of a more generalised framework of forensic investigation known as the Case Assessment and Interpretation (CAI) model, I show how the enactment of Bayesian reasoning is dependent on a series of embodied, experiential and intersubjective knowledge-forming activities. Whilst these practices may seem to be largely independent of theoretical representations of Bayesian reasoning, they are nonetheless necessary to bring the latter into being. This is at least partially due to the ambiguities and liminalities encountered in the process of applying Bayesianism to forensic investigation, and also may result from the heavy informational demands placed on the reasoner. I argue that these practices, or 'forms of Bayes', are necessary in order to negotiate areas of ontological uncertainty. The results of this thesis therefore challenge prevailing conceptions of Bayes Theorem as a universal, immutable signifier, able to be put to work unproblematically in any substantive domain, Instead, I have been able to highlight the diverse range of practices required for 'Bayesian' reasoners to negotiate the sociomaterial contingencies exposed in the process of its application

A comparative analysis of selected clustering algorithms for criminal profiling

Several criminal profiling systems have been developed to assist the Law Enforcement Agencies in solving crimes but the techniques employed in most of the systems lack the ability to cluster criminal based on their behavioral characteristics. This paper reviewed different clustering techniques used in criminal profiling and then selects one fuzzy clustering algorithm (Expectation Maximization) and two hard clustering algorithm (K-means and Hierarchical). The selected algorithms were then developed and tested on real life data to produce "profiles" of criminal activity and behavior of criminals. The algorithms were implemented using WEKA software package. The performance of the algorithms was evaluated using cluster accuracy and time complexity. The results show that Expectation Maximization algorithm gave a 90.5% clusters accuracy in 8.5s, while K-Means had 62.6% in 0.09s and Hierarchical with 51.9% in 0.11s. In conclusion, soft clustering algorithm performs better than hard clustering algorithm in analyzing criminal data. Keywords: Clustering Algorithm, Profiling, Crime, Membership valu

Evaluation of forensic genetics findings given activity level propositions: A review.

The evaluation of results of forensic genetic analyses given activity level propositions is an emerging discipline in forensic genetics. Although it is a topic with a long history, it has never been considered to be such a critically important topic for the field, as today. With the increasing sensitivity of analysis techniques, and advances in data interpretation using probabilistic models ('probabilistic genotyping'), there is an increasing demand on forensic biologists to share specialised knowledge to help recipients of expert information address mode and timing of transfer and persistence of traces in court. Scientists thereby have a critical role in the assessment of their findings in the context of the case. This helps the judiciary with activity level inferences in a balanced, robust and transparent way, when based on (1) proper case assessment and interpretation respecting the hierarchy of propositions (supported by, for example, the use of Bayesian networks as graphical models), (2) use of appropriate data to inform probabilities, and (3) reporting guidelines by international bodies. This critical review of current literature shows that with certain prerequisites for training and quality assurance, there is a solid foundation for evidence interpretation when propositions of interest are at the 'activity level'

Multi-instance learning with application to the profiling of multi-victim homicides

Altres ajuts: acords transformatius de la UABHomicide involving multiple victims has a significant negative effect on society. Criminal profiling consists of determining the traits of an unknown offender based on those of the crime and the victims, with a view to their identification. To provide the most likely profile of the perpetrator of a multi-victim homicide, we propose a predictive model of supervised machine learning based on a Bayesian Network. Conventional classifiers can generate the perpetrator's profile according to the traits of each of the victims of the same homicide, but the profiles may differ from one another. To address this issue, we consider the Multi-Instance (MI) learning framework, in which the victims of the same incident form a bag, and each bag is associated with a unique label for each of the perpetrator's features. We introduce the unanimity MI assumption in this domain, and accordingly allocate a label to the bag based on the labels and probabilities the Bayesian Network has assigned its instances, using a combination rulefrom those of the ensemble of classifiers. We apply this methodology to the Federal Bureau of Investigation (FBI) homicide database to compare three combination rules empirically in the validation process, as well as theoretically, using the one that ultimately proves to be the best to build the final model, which is then applied in some illustrative examples to achieve the criminal profile. PID2021-123733NB-I0

A Case-Based Reasoning Method for Locating Evidence During Digital Forensic Device Triage

The role of triage in digital forensics is disputed, with some practitioners questioning its reliability for identifying evidential data. Although successfully implemented in the field of medicine, triage has not established itself to the same degree in digital forensics. This article presents a novel approach to triage for digital forensics. Case-Based Reasoning Forensic Triager (CBR-FT) is a method for collecting and reusing past digital forensic investigation information in order to highlight likely evidential areas on a suspect operating system, thereby helping an investigator to decide where to search for evidence. The CBR-FT framework is discussed and the results of twenty test triage examinations are presented. CBR-FT has been shown to be a more effective method of triage when compared to a practitioner using a leading commercial application

Data Mining Techniques for Fraud Detection

The paper presents application of data mining techniques to fraud analysis. We present some classification and prediction data mining techniques which we consider important to handle fraud detection. There exist a number of data mining algorithms and we present statistics-based algorithm, decision tree-based algorithm and rule-based algorithm. We present Bayesian classification model to detect fraud in automobile insurance. Naïve Bayesian visualization is selected to analyze and interpret the classifier predictions. We illustrate how ROC curves can be deployed for model assessment in order to provide a more intuitive analysis of the models. Keywords: Data Mining, Decision Tree, Bayesian Network, ROC Curve, Confusion Matri

Evaluation of Forensic DNA Traces When Propositions of Interest Relate to Activities : Analysis and Discussion of Recurrent Concerns

When forensic scientists evaluate and report on the probative strength of single DNA traces, they commonly rely on only one number, expressing the rarity of the DNA profile in the population of interest. This is so because the focus is on propositions regarding the source of the recovered trace material, such as “the person of interest is the source of the crime stain.” In particular, when the alternative proposition is “an unknown person is the source of the crime stain,” one is directed to think about the rarity of the profile. However, in the era of DNA profiling technology capable of producing results from small quantities of trace material (i.e., non-visible staining) that is subject to easy and ubiquitous modes of transfer, the issue of source is becoming less central, to the point that it is often not contested. There is now a shift from the question “whose DNA is this?” to the question “how did it get there?” As a consequence, recipients of expert information are now very much in need of assistance with the evaluation of the meaning and probative strength of DNA profiling results when the competing propositions of interest refer to different activities. This need is widely demonstrated in day-to-day forensic practice and is also voiced in specialized literature. Yet many forensic scientists remain reluctant to assess their results given propositions that relate to different activities. Some scientists consider evaluations beyond the issue of source as being overly speculative, because of the lack of relevant data and knowledge regarding phenomena and mechanisms of transfer, persistence and background of DNA. Similarly, encouragements to deal with these activity issues, expressed in a recently released European guideline on evaluative reporting (Willis et al., 2015), which highlights the need for rethinking current practice, are sometimes viewed skeptically or are not considered feasible. In this discussion paper, we select and discuss recurrent skeptical views brought to our attention, as well as some of the alternative solutions that have been suggested. We will argue that the way forward is to address now, rather than later, the challenges associated with the evaluation of DNA results (from small quantities of trace material) in light of different activities to prevent them being misrepresented in court