#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

The role of diet on intestinal microbiota metabolism: downstream impacts on host immune function and health, and therapeutic implications

Dietary impacts on health may be one of the oldest concepts in medicine; however, only in recent years have technical advances in mass spectroscopy, gnotobiology, and bacterial sequencing enabled our understanding of human physiology to progress to the point where we can begin to understand how individual dietary components can affect specific illnesses. This review explores the current understanding of the complex interplay between dietary factors and the host microbiome, concentrating on the downstream implications on host immune function and the pathogenesis of disease. We discuss the influence of the gut microbiome on body habitus and explore the primary and secondary effects of diet on enteric microbial community structure. We address the impact of consumption of non-digestible polysaccharides (prebiotics and fiber), choline, carnitine, iron, and fats on host health as mediated by the enteric microbiome. Disease processes emphasized include nonalcoholic fatty liver disease (NAFLD)/non-alcoholic steatohepatitis (NASH), IBD, and cardiovascular disease (CVD)/atherosclerosis. The concepts presented in this review have important clinical implications, although more work needs to be done to fully develop and validate potential therapeutic approaches. Specific dietary interventions offer exciting potential for nontoxic, physiologic ways to alter enteric microbial structure and metabolism to benefit the natural history of many intestinal and systemic disorders

The Data that Drives Cyber Insurance: A Study into the Underwriting and Claims Processes

Cyber insurance is a key component in risk management, intended to transfer risks and support business recovery in the event of a cyber incident. As cyber insurance is still a new concept in practice and research, there are many unanswered questions regarding the data and economic models that drive it, the coverage options and pricing of premiums, and its more procedural policy-related aspects. This paper aims to address some of these questions by focusing on the key types of data which are used by cyber-insurance practitioners, particularly for decision-making in the insurance underwriting and claim processes. We further explore practitioners' perceptions of the challenges they face in gathering and using data, and identify gaps where further data is required. We draw our conclusions from a qualitative study by conducting a focus group with a range of cyber-insurance professionals (including underwriters, actuaries, claims specialists, breach responders, and cyber operations specialists) and provide valuable contributions to existing knowledge. These insights include examples of key data types which contribute to the calculation of premiums and decisions on claims, the identification of challenges and gaps at various stages of data gathering, and initial perspectives on the development of a pre-competitive dataset for the cyber insurance industry. We believe an improved understanding of data gathering and usage in cyber insurance, and of the current challenges faced, can be invaluable for informing future research and practice

Towards Designing a Multipurpose Cybercrime Intelligence Framework

With the wide spread of the Internet and the increasing popularity of social networks that provide prompt and ease of communication, several criminal and radical groups have adopted it as a medium of operation. Existing literature in the area of cybercrime intelligence focuses on several research questions and adopts multiple methods using techniques such as social network analysis to address them. In this paper, we study the broad state-of-the-art research in cybercrime intelligence in order to identify existing research gaps. Our core aim is designing and developing a multipurpose framework that is able to fill these gaps using a wide range of techniques. We present an outline of a framework designed to aid law enforcement in detecting, analysing and making sense out of cybercrime data

Mu Opioid Signaling Protects Against Acute Murine Intestinal Injury in a Manner Involving Stat3 Signaling

Opiates have long been used as analgesics to relieve pain associated with various medical conditions. Here, we evaluated the effect and mechanism of mu opioid signaling on the intestinal wound healing response and assessed downstream pathways known to be protective against intestinal injury. Mice (C57BL/6) were exposed to 3% dextran sodium sulfate (DSS) for 7 days or 4% DSS for 5 days followed by 7 days of water. The mu opioid receptor (MOR)-specific agonist [D-Arg2,Lys4]dermorphin-(1,4)-amide (DALDA) and the antagonist cyprodime were injected s.c. daily for in vivo studies or used for in vitro analysis. We found that MOR activation attenuated DSS-induced histologic and gross intestinal injury and weight loss; diminished Ifng, Tnf, and Il6 mRNA expression; and promoted intestinal healing during recovery. DALDA also enhanced colonocyte proliferation (Ki-67 staining) by 350%. MOR activation increased Stat3 phosphorylation in both DALDA-treated mice and the CMT-93 cell line. Importantly, DALDA-induced colonocyte migration was completely ablated by shStat3 knockdown. Together, this work shows that MOR activation protects against and enhances recovery from DSS-induced intestinal injury. This is associated with an increase in Stat3 activation. Furthermore, Stat3 is required for DALDA-induced colonocyte migration. Consequently, manipulation of MOR signaling may represent a novel means to promote mucosal healing and to maintain intestinal homeostasis after intestinal injury

Identifying Key-Players in Online Activist Groups on Facebook Social Network

Online social media applications have become an integral part of our everyday life. Not only are they being utilised by individuals and legitimate businesses, but also recently several organised groups, such as activists, hactivists, and cyber-criminals have adopted them to communicate and spread their ideas. This represents a new source for intelligence gathering for law enforcement for instance, as it allows them an inside look at the behaviour of these previously closed, secretive groups. One possible opportunity with this online data source is to utilise the public exchange of social-media messages to identify key users in such groups. This is particularly important for law enforcement that wants to monitor or interrogate influential people in suspicious groups. In this paper, we utilise Social Network Analysis (SNA) techniques to understand the dynamics of the interaction between users in a Facebook-based activist group. Additionally, we aim to identify the most influential users in the group and infer their relationship strength. We incorporate sentiment analysis to identify users with clear positive and negative influences on the group; this could aid in facilitating a better understanding of the group.We also perform a temporal analysis to correlate online activities with relevant real-life events. Our results show that applying such data analysis techniques on users online behaviour is a powerful tool to predict levels of influence and relationship strength between group members. Finally, we validated our results against the ground truth and found that our approach is very promising at achieving its aims

Understanding the Radical Mind: Identifying Signals to Detect Extremist Content on Twitter

The Internet and, in particular, Online Social Networks have changed the way that terrorist and extremist groups can influence and radicalise individuals. Recent reports show that the mode of operation of these groups starts by exposing a wide audience to extremist material online, before migrating them to less open online platforms for further radicalization. Thus, identifying radical content online is crucial to limit the reach and spread of the extremist narrative. In this paper, our aim is to identify measures to automatically detect radical content in social media. We identify several signals, including textual, psychological and behavioural, that together allow for the classification of radical messages. Our contribution is three-fold: (1) we analyze propaganda material published by extremist groups and create a contextual text-based model of radical content, (2) we build a model of psychological properties inferred from these material, and (3) we evaluate these models on Twitter to determine the extent to which it is possible to automatically identify online radical tweets. Our results show that radical users do exhibit distinguishable textual, psychological, and behavioural properties. We find that the psychological properties are among the most distinguishing features. Additionally, our results show that textual models using vector embedding features significantly improves the detection over TF-IDF features. We validate our approach on two experiments achieving high accuracy. Our findings can be utilized as signals for detecting online radicalization activities

Guidelines for Usable Cybersecurity: Past and Present

Usability is arguably one of the most significant social topics and issues within the field of cybersecurity today. Supported by the need for confidentiality, integrity, availability and other concerns, security features have become standard components of the digital environment which pervade our lives requiring use by novices and experts alike. As security features are exposed to wider cross-sections of the society, it is imperative that these functions are highly usable. This is especially because poor usability in this context typically translates into inadequate application of cybersecurity tools and functionality, thereby ultimately limiting their effectiveness. With this goal of highly usable security in mind, there have been a plethora of studies in the literature focused on identifying security usability problems and proposing guidelines and recommendations to address them. Our paper aims to contribute to the field by consolidating a number of existing design guidelines and defining an initial core list for future reference. Whilst investigating this topic, we take the opportunity to provide an up-to-date review of pertinent cybersecurity usability issues and evaluation techniques applied to date. We expect this research paper to be of use to researchers and practitioners with interest in cybersecurity systems which appreciate the human and social elements of design

A Formalised Approach to Designing Sonification Systems for Network-Security Monitoring

Sonification systems, in which data are represented through sound, have the potential to be useful in a number of network-security monitoring applications in Security Operations Centres (SOCs). Security analysts working in SOCs generally monitor networks using a combination of anomaly-detection techniques, Intrusion Detection Systems and data presented in visual and text-based forms. In the last two decades significant progress has been made in developing novel sonification systems to further support network-monitoring tasks, but many of these systems have not been sufficiently validated, and there is a lack of uptake in SOCs. Furthermore, little guidance exists on design requirements for the sonification of network data. In this paper, we identify the key role that sonification, if implemented correctly, could play in addressing shortcomings of traditional network-monitoring methods. Based on a review of prior research, we propose an approach to developing sonification systems for network monitoring. This approach involves the formalisation of a model for designing sonifications in this space; identification of sonification design aesthetics suitable for realtime network monitoring; and system refinement and validation through comprehensive user testing. As an initial step in this system development, we present a formalised model for designing sonifications for network-security monitoring. The application of this model is demonstrated through our development of prototype sonification systems for two different use-cases within network security monitoring

Attacker-Parametrised Attack Graphs

Computer network attackers chain system exploits together to achieve their goals, which range from stealing data to corrupting systems. Attack graphs represent these paths through the network, and provide the basis for calculating many security metrics. In this paper, we seek to extend graph-based analysis from the consideration of single graphs to the consideration of multiple. By performing analysis on many graphs at once, we consider the range of threats faced and avoid the downsides of several current techniques, which focus purely on known and expected attackers. In particular, we propose a novel method of generating a set of attack graphs, parametrised by attacker profiles. Our technique would enable security analysts to consider the security of their network from the perspective of many attackers simultaneously. This contrasts with existing techniques, which typically analyse attacker-independent graphs or graphs constructed around predefined attacker profiles. We analyse the resulting set of graphs first through deterministic methods and then using a probability measure