A preliminary radicalisation framework based on social engineering techniques

YesThe use of online forums and social media sites by extremists for recruiting and radicalising individuals has been covered extensively by researchers. Meanwhile, the social engineering techniques utilised by these extremists to lure marginalised individuals into radicalisation has been neglected. In this article, the social engineering aspects of online radicalisation will be explored. Specifically, the five Principles of Persuasion in Social Engineering (PPSE) will be mapped onto the online radicalisation methods employed by extremists online. Analysing these tactics will aid in gaining a deeper understanding of the process of indoctrination and of the psychology of both the attacker and the target of such attacks. This understanding has enabled the development of a preliminary radicalisation framework based on the social traits of a target that may be exploited during an attack

TENSOR: retrieval and analysis of heterogeneous online content for terrorist activity recognition

The proliferation of terrorist generated content online is a cause for concern as it goes together with the rise of radicalisation and violent extremism. Law enforcement agencies (LEAs) need powerful platforms to help stem the influence of such content. This article showcases the TENSOR project which focusses on the early detection of online terrorist activities, radicalisation and recruitment. Operating under the H2020 Secure Societies Challenge, TENSOR aims to develop a terrorism intelligence platform for increasing the ability of LEAs to identify, gather and analyse terrorism-related online content. The mechanisms to tackle this challenge by bringing together LEAs, industry, research, and legal experts are presented

#ISIS vs #ActionCountersTerrorism: A Computational Analysis of Extremist and Counter-extremist Twitter Narratives

The rapid expansion of cyberspace has greatly facilitated the strategic shift of traditional crimes to online platforms. This has included malicious actors, such as extremist organisations, making use of online networks to disseminate propaganda and incite violence through radicalising individuals. In this article, we seek to advance current research by exploring how supporters of extremist organisations craft and disseminate their content, and how posts from counter-extremism agencies compare to them. In particular, this study will apply computational techniques to analyse the narratives of various pro-extremist and counter-extremist Twitter accounts, and investigate how the psychological motivation behind the messages compares between pro-ISIS and counter-extremism narratives. Our findings show that pro-extremist accounts often use different strategies to disseminate content (such as the types of hashtags used) when compared to counter-extremist accounts across different types of organisations, including accounts of governments and NGOs. Through this study, we provide unique insights into both extremist and counter-extremist narratives on social media platforms. Furthermore, we define several avenues for discussion regarding the extent to which counter-messaging may be effective at diminishing the online influence of extremist and other criminal organisations

Security and defence research in the European Union: a landscape review

This landscape report describes the state of play of the European Union’s policies and activities in security and defence and the EU-funded research aimed at supporting them, with an exclusive focus on intentional harm. It is organised around several thematic building blocks under the umbrella of the three core priorities defined in the European agenda on security. The report reviews the current main risks and threats but also those that may emerge within the next 5 years, the policy and operational means developed to combat them, the main active stakeholders and the EU legislation in force. In this context, a short history of EU research on security and defence is presented, followed by an inventory of relevant research and development projects funded under the Horizon 2020 framework programme during the period 2014-2018. The specific contributions of the Joint Research Centre to security research are also highlighted. Finally, future avenues for security and defence research and development are discussed. Please note that the executive summary of this landscape report has been published simultaneously as a companion document.JRC.E.7-Knowledge for Security and Migratio

State of the art 2015: a literature review of social media intelligence capabilities for counter-terrorism

Overview This paper is a review of how information and insight can be drawn from open social media sources. It focuses on the specific research techniques that have emerged, the capabilities they provide, the possible insights they offer, and the ethical and legal questions they raise. These techniques are considered relevant and valuable in so far as they can help to maintain public safety by preventing terrorism, preparing for it, protecting the public from it and pursuing its perpetrators. The report also considers how far this can be achieved against the backdrop of radically changing technology and public attitudes towards surveillance. This is an updated version of a 2013 report paper on the same subject, State of the Art. Since 2013, there have been significant changes in social media, how it is used by terrorist groups, and the methods being developed to make sense of it.  The paper is structured as follows: Part 1 is an overview of social media use, focused on how it is used by groups of interest to those involved in counter-terrorism. This includes new sections on trends of social media platforms; and a new section on Islamic State (IS). Part 2 provides an introduction to the key approaches of social media intelligence (henceforth ‘SOCMINT’) for counter-terrorism. Part 3 sets out a series of SOCMINT techniques. For each technique a series of capabilities and insights are considered, the validity and reliability of the method is considered, and how they might be applied to counter-terrorism work explored. Part 4 outlines a number of important legal, ethical and practical considerations when undertaking SOCMINT work

The Concept and Practice of De-radicalisation in the PREVENT Strand of the UK Counter-terrorism Strategy: What is De-radicalisation?

De-radicalisation has become increasingly prevalent in the UK’s counter-terrorism policy as a strategy for tackling the threat of religiously inspired violence/extremism. Recently, British citizens fighting in Middle Eastern conflicts have rekindled the preoccupation of policymakers with the radicalisation of British Muslims. In fact the work of PREVENT post 2011 has primarily been recalibrated towards a greater focus on de-radicalisation interventions, which is delivered by the police through the Channel programme. Channel is perceived by policy-makers to be a more streamlined and effective way of dealing with radicalised/extremist individuals than the wide remit of PREVENT initiatives between 2006 and 2010. Indeed since becoming placed on a statutory footing in 2015, PREVENT requires public institutions, like schools and universities, to identify ‘vulnerable’ individuals’ at risk of radicalisation. And yet despite the greater attention on de-radicalisation, very little continues to be known about what makes violent individuals leave terrorism behind. De-radicalisation in PREVENT is characterised by the absence of credible research, little or no empirical evidence for policy development, confusion surrounding its conceptual framework, and conflicting policy logics. The following thesis is based on a case-study examination of de-radicalisation with 27 PREVENT practitioners. Through qualitative semi-structured interviews, my investigation seeks to address the problems that arise from the concept and practice of de-radicalisation in PREVENT by ascertaining (a) an ontological understanding of de-radicalisation and (b) the practice of de-radicalisation. The findings of the fieldwork data revealed the existence of multiple conceptions of de-radicalisation and a number of conceptual features unique to the UK context. Despite yielding a more fruitful conceptual and empirical understanding of de-radicalisation, the data in itself nevertheless could not fully explicate the relationship between several critical themes comprehensively within an analytically generative framework. With the inductive method falling short, I draw on Michel Foucault’s concept of the ‘technologies of the self’. Comprising of discursive, disciplinary, and confessional technologies, it is argued that the technologies of the self allows us to reframe the concept beyond the narrow confines of counter-terrorism policy and place it within wider governmental relations. Situated within neo-liberal governmentality, the technologies of the self encourage individuals to work on themselves and regulate their behaviour through a wide range of discursive, practical, and technical interventions. Seen in this way, de-radicalisation is therefore less about the mitigation of violence and more about the making of a particular political and ethical subjectivity. Ultimately, the technology of the self eschews the conceptual problems inherent in the PREVENT conception of de-radicalisation, the limitations evident in the literature, whilst amplifying the salient findings of my fieldwork data. It provides a more robust concept and theory that successfully captures and explains de-radicalisation in the UK context. This thesis thus makes an original contribution to knowledge by (1) being the first study to gather primary data on de-radicalisation in the UK; (2) offering an alternative concept of de-radicalisation; and (3) contributing to theories on the governmentality of radicalisation policies, focusing on the micro-politics of identity in neoliberal governance

Us against the World: Detection of Radical Language in Online Platforms

In this paper, we have investigated if we can detect radical comments in an online social network. We used comments from 6 subreddits, 3 of which are considered radical and 3 non-radical. Using various structural features of the texts in the comments, we were able to obtain an F1-score of 91% when using SVM with a linear kernel and a precision of almost 98% when using Random Forest

Towards a National Security Analysis Approach via Machine Learning and Social Media Analytics

Various severe threats at national and international level, such as health crises, radicalisation, or organised crime, have the potential of unbalancing a nation's stability. Such threats impact directly on elements linked to people's security, known in the literature as human security components. Protecting the citizens from such risks is the primary objective of the various organisations that have as their main objective the protection of the legitimacy, stability and security of the state. Given the importance of maintaining security and stability, governments across the globe have been developing a variety of strategies to diminish or negate the devastating effects of the aforementioned threats. Technological progress plays a pivotal role in the evolution of these strategies. Most recently, artificial intelligence has enabled the examination of large volumes of data and the creation of bespoke analytical tools that are able to perform complex tasks towards the analysis of multiple scenarios, tasks that would usually require significant amounts of human resources. Several research projects have already proposed and studied the use of artificial intelligence to analyse crucial problems that impact national security components, such as violence or ideology. However, the focus of all this prior research was examining isolated components. However, understanding national security issues requires studying and analysing a multitude of closely interrelated elements and constructing a holistic view of the problem. The work documented in this thesis aims at filling this gap. Its main contribution is the creation of a complete pipeline for constructing a big picture that helps understand national security problems. The proposed pipeline covers different stages and begins with the analysis of the unfolding event, which produces timely detection points that indicate that society might head toward a disruptive situation. Then, a further examination based on machine learning techniques enables the interpretation of an already confirmed crisis in terms of high-level national security concepts. Apart from using widely accepted national security theoretical constructions developed over years of social and political research, the second pillar of the approach is the modern computational paradigms, especially machine learning and its applications in natural language processing