This chapter explores the legal and moral implications of the use of data science in criminal justice at two levels: police surveillance and the criminal trial of a defendant. At the first level, police surveillance, data science is used to identify places and people at high risk of criminal activity, allowing police officers to target surveillance and take proactive measures to try to prevent crime (predictive policing). At the second level, the criminal trial of a defendant, data science is used to make risk assessments to support decisions about bail, sentencing, probation, and supervision and detention orders for high-risk offenders. The use of data science at these levels has one thing in common: it is about predicting risk. The uncertainty associated with risk prediction raises specific related legal and ethical dilemmas, for example in the areas of reasonable suspicion, presumption of innocence, privacy, and the principle of non-discrimination.
Longitudinal criminological studies greatly improved our understanding of the longitudinal patterns of criminality. These studies, however, focused almost exclusively on traditional types of offending and it is therefore unclear whether results are generalizable to online types of offending. This study attempted to identify the developmental trajectories of active hackers who perform web defacements. The data for this study consisted of 2,745,311 attacks performed by 66,553 hackers and reported to Zone-H between January 2010 and March 2017. Semi-parametric group-based trajectory models were used to distinguish six different groups of hackers based on the timing and frequency of their defacements. The results demonstrated some common relationships to traditional types of crime, as a small population of defacers accounted for the majority of defacements against websites. Additionally, the methods and targeting practices of defacers differed based on the frequency with which they performed defacements generally.
Based on the results of two research projects from the Netherlands, this paper explores how street oriented persons adapt and use digital technologies by focusing on the changing commission of instrumental, economically motivated, street crime. Our findings show how social media are used by street offenders to facilitate or improve parts of the crime script of already existing criminal activities but also how street offenders are engaging in criminal activities not typically associated with the street, like phishing and fraud. Taken together, this paper documents how technology has permeated street life and contributed to the ‘hybridization’ of street offending in the Netherlands—i.e. offending that takes place in person and online, often at the same time.
Despite their various appealing features, drones also have some undesirable side-effects. One of them is the psychoacoustic effect that originates from their buzzing noise that causes significant noise pollutions. This has an effect on nature (animals run away) and on humans (noise nuisance and thus stress and health problems). In addition, these buzzing noises contribute to alerting criminals when low-flying drones are deployed for safety and security applications. Therefore, there is an urgent demand from SMEs for practical knowledge and technologies that make existing drones silent, which is the main focus of this project. This project contributes directly to the KET Digital Innovations\Robotics and multiple themes of the top sectors: Agriculture, Water and Food, Health & Care and Safety. The main objective of this project is: Investigate the desirability and possibilities of extremely silent drone technologies for agriculture, public space and safety This is an innovative project and there exist no such drone technology that attempts to reduce the noises coming from drones. The knowledge within this project will be converted into the first proof-of-concepts that makes the technology the first Minimum Viable Product suitable for market evaluations. The partners of this project include WhisperUAV, which has designed the first concept of a silent drone. As a fiber-reinforced 3D composite component printer, Fiberneering plays a crucial role in the (further) development of silent drone technologies into testable prototypes. Sorama is involved as an expert company in the context of mapping the sound fields in and around drones. The University of Twente is involved as a consultant and co-developer, and Research group of mechatronics at Saxion is involved as concept developer, system and user requirement verifier and validator. As an unmanned systems innovation cluster, Space53 will be involved as innovation and networking consultant.
Despite the benefits of the widespread deployment of diverse Internet-enabled devices such as IP cameras and smart home appliances - the so-called Internet of Things (IoT) has amplified the attack surface that is being leveraged by cyber criminals. While manufacturers and vendors keep deploying new products, infected devices can be counted in the millions and spreading at an alarming rate all over consumer and business networks. The objective of this project is twofold: (i) to explain the causes behind these infections and the inherent insecurity of the IoT paradigm by exploring innovative data analytics as applied to raw cyber security data; and (ii) to promote effective remediation mechanisms that mitigate the threat of the currently vulnerable and infected IoT devices. By performing large-scale passive and active measurements, this project will allow the characterization and attribution of compromise IoT devices. Understanding the type of devices that are getting compromised and the reasons behind the attacker’s intention is essential to design effective countermeasures. This project will build on the state of the art in information theoretic data mining (e.g., using the minimum description length and maximum entropy principles), statistical pattern mining, and interactive data exploration and analytics to create a casual model that allows explaining the attacker’s tactics and techniques. The project will research formal correlation methods rooted in stochastic data assemblies between IoT-relevant measurements and IoT malware binaries as captured by an IoT-specific honeypot to aid in the attribution and thus the remediation objective. Research outcomes of this project will benefit society in addressing important IoT security problems before manufacturers saturate the market with ostensibly useful and innovative gadgets that lack sufficient security features, thus being vulnerable to attacks and malware infestations, which can turn them into rogue agents. However, the insights gained will not be limited to the attacker behavior and attribution, but also to the remediation of the infected devices. Based on a casual model and output of the correlation analyses, this project will follow an innovative approach to understand the remediation impact of malware notifications by conducting a longitudinal quasi-experimental analysis. The quasi-experimental analyses will examine remediation rates of infected/vulnerable IoT devices in order to make better inferences about the impact of the characteristics of the notification and infected user’s reaction. The research will provide new perspectives, information, insights, and approaches to vulnerability and malware notifications that differ from the previous reliance on models calibrated with cross-sectional analysis. This project will enable more robust use of longitudinal estimates based on documented remediation change. Project results and methods will enhance the capacity of Internet intermediaries (e.g., ISPs and hosting providers) to better handle abuse/vulnerability reporting which in turn will serve as a preemptive countermeasure. The data and methods will allow to investigate the behavior of infected individuals and firms at a microscopic scale and reveal the causal relations among infections, human factor and remediation.
