Project objectives Radicalisation research leads to ethical and legal questions and issues. These issues need to be addressed in way that helps the project progress in ethically and legally acceptable manner. Description of Work The legal analysis in SAFIRE addressed questions such as which behavior associated with radicalisation is criminal behaviour. The ethical issues were addressed throughout the project in close cooperation between the ethicists and the researchers using a method called ethical parallel research. Results A legal analysis was made about criminal law and radicalisation. During the project lively discussions were held in the research team about ethical issues. An ethical justification for interventions in radicalisation processes has been written. With regard to research ethics: An indirect informed consent procedure for interviews with (former) radicals has been designed. Practical guidelines to prevent obtaining information that could lead to indirect identification of respondents were developed.
Much research has been done into the relationship between students’ motivation to learn and their basic psychological needs as defined by the self-determination theory (autonomy, competence, relatedness). However, few studies have explored how these psychological needs relate to different types of maladaptive behavior in the classroom. To prevent or remedy such behavior, more insight into its relationships is required. The present study attempted to determine the relationship between maladaptive behavior of secondary school students (grades 8 and 9) and the degree to which both teachers and peers address their needs for competence, autonomy, and relatedness. Results show significant, negative correlations between maladaptive student behavior in the classroom and the extent to which students’ basic psychological needs are met by teachers and fellow students. Both teachers and fellow students play a role in students’ maladaptive behavior toward school and withdrawn behavior. When it comes to unfriendly behavior, the perceived support of teachers appears to be particularly relevant, while the role of peers is an important factor in delinquent behavior.
The past two decades, a disproportionate growth of females entering the criminal justice system and forensic mental health services has been observed worldwide. However, there is a lack of knowledge on the background of women who are convicted for violent offenses. What is their criminal history, what are their motives for offending and in which way do they differ from men convicted for violent offenses? In this study, criminal histories and the offenses for which they were admitted to forensic care were analyzed of 218 women and 218 men who have been treated between 1984 and 2014 with a mandatory treatment order in one of four Dutch forensic psychiatric settings admitting both men and women. It is concluded that there are important differences in violent offending between male and female patients. Most importantly, female violence was more often directed towards their close environment, like their children, and driven by relational frustration. Furthermore, female patients received lower punishments compared to male patients and were more often considered to be diminished accountable for their offenses due to a mental illness.
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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.
