Abstract
Nowadays, (cyber)criminals demonstrate an ever-increasing resolve to exploit new technologies so as to achieve their unlawful purposes. Therefore, Law Enforcement Agencies (LEAs) should keep one step ahead by engaging tools and technology that address existing challenges and enhance policing and crime prevention practices. The framework presented in this paper combines algorithms and tools that are used to correlate different pieces of data leading to the discovery and recording of forensic evidence. The collected data are, then, combined to handle inconsistencies, whereas machine learning techniques are applied to detect trends and outliers. In this light, the authors of this paper present, in detail, an innovative Abnormal Behavior Detection Engine, which also encompasses a knowledge base visualization functionality focusing on financial transactions investigation.
Highlights
The rapid growth of Information and Communication Technology (ICT), underpins new types of criminal activities
Law Enforcement Agencies (LEAs) and security practitioners need to adapt to this everchanging reality in order to be able to both prevent and fight crime
The Receiver Operating Characteristics (ROC) graph is used from the early stages of Machine Learning in order to evaluate and compare algorithms as demonstrated by Spackman in [55]
Summary
The rapid growth of Information and Communication Technology (ICT), underpins new types of criminal activities. The Z-score consists the major outlier detection algorithm on one-dimensional data analysis and is a suitable method for distributions in a low dimensional feature space [9].
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