Why Cyber Security is Hard

Abstract There has been a great deal of interest in the area of cyber security in recent years. But what is cyber security exactly? And should society really care about it? We look at some of the challenges of being an academic working in the area of cyber security and explain why cyber security is, to put it rather simply, hard! Speaker Biography Keith Martin Prof. Keith Martin is Professor of Information Security at Royal Holloway, University of London. He received his BSc (Hons) in Mathematics from the University of Glasgow in 1988 and a PhD from Royal Holloway in 1991. Between 1992 and 1996 he held a Research Fellowship at the University of Adelaide, investigating mathematical modelling of cryptographic key distribution problems. In 1996 he joined the COSIC research group of the Katholieke Universiteit Leuven in Belgium, working on security for third generation mobile communications. Keith rejoined Royal Holloway in January 2000, became a Professor in Information Security in 2007 and was Director of the Information Security Group between 2010 and 2015. Keith's research interests range across cyber security, but with a focus on cryptographic applications. He is the author of 'Everyday Cryptography' published by Oxford University Press.

"Thematically Analysing Social Network Content During Disasters Through the Lens of the Disaster Management Lifecycle" & "Investigating Similarity Between Privacy Policies of Social Networking Sites as a Precursor for Standardization"

Abstract 1: Social Networks such as Twitter are often used for disseminating and collecting information during natural disasters. The potential for its use in Disaster Management has been acknowledged. However, more nuanced understanding of the communications that take place on social networks are required to more effectively integrate this information into the processes within disaster management. The type and value of information shared should be assessed, determining the benefits and issues, with credibility and reliability as known concerns. Mapping the tweets in relation to the modelled stages of a disaster can be a useful evaluation for determining the benefits/drawbacks of using data from social networks, such as Twitter, in disaster management.A thematic analysis of tweets’ content, language and tone during the UK Storms and Floods 2013/14 was conducted. Manual scripting was used to determine the official sequence of events, and classify the stages of the disaster into the phases of the Disaster Management Lifecycle, to produce a timeline. Twenty- five topics discussed on Twitter emerged, and three key types of tweets, based on the language and tone, were identified. The timeline represents the events of the disaster, according to the Met Office reports, classed into B. Faulkner’s Disaster Management Lifecycle framework. Context is provided when observing the analysed tweets against the timeline. This illustrates a potential basis and benefit for mapping tweets into the Disaster Management Lifecycle phases. Comparing the number of tweets submitted in each month with the timeline, suggests users tweet more as an event heightens and persists. Furthermore, users generally express greater emotion and urgency in their tweets.This paper concludes that the thematic analysis of content on social networks, such as Twitter, can be useful in gaining additional perspectives for disaster management. It demonstrates that mapping tweets into the phases of a Disaster Management Lifecycle model can have benefits in the recovery phase, not just in the response phase, to potentially improve future policies and activities. Abstract2: The current execution of privacy policies, as a mode of communicating information to users, is unsatisfactory. Social networking sites (SNS) exemplify this issue, attracting growing concerns regarding their use of personal data and its effect on user privacy. This demonstrates the need for more informative policies. However, SNS lack the incentives required to improve policies, which is exacerbated by the difficulties of creating a policy that is both concise and compliant. Standardization addresses many of these issues, providing benefits for users and SNS, although it is only possible if policies share attributes which can be standardized. This investigation used thematic analysis and cross- document structure theory, to assess the similarity of attributes between the privacy policies (as available in August 2014), of the six most frequently visited SNS globally. Using the Jaccard similarity coefficient, two types of attribute were measured; the clauses used by SNS and the coverage of forty recommendations made by the UK Information Commissioner’s Office. Analysis showed that whilst similarity in the clauses used was low, similarity in the recommendations covered was high, indicating that SNS use different clauses, but to convey similar information. The analysis also showed that low similarity in the clauses was largely due to differences in semantics, elaboration and functionality between SNS. Therefore, this paper proposes that the policies of SNS already share attributes, indicating the feasibility of standardization and five recommendations are made to begin facilitating this, based on the findings of the investigation.

TU Graz: Course: 707.000 Web Science and Web Technology: Lecture 3: Network Theory and Terminology

In this class, we will discuss network theory fundamentals, including concepts such as diameter, distance, clustering coefficient and others. We will also discuss different types of networks, such as scale-free networks, random networks etc. Readings: Graph structure in the Web, A. Broder and R. Kumar and F. Maghoul and P. Raghavan and S. Rajagopalan and R. Stata and A. Tomkins and J. Wiener Computer Networks 33 309--320 (2000) [Web link, Alternative Link] Optional: The Structure and Function of Complex Networks, M.E.J. Newman, SIAM Review 45 167--256 (2003) [Web link] Original course at: http://kmi.tugraz.at/staff/markus/courses/SS2008/707.000_web-science/

Networked Life: Module CIS 112, University of Pennsylvania, Spring 2008

What kind of science is appropriate for understanding the Facebook? How does Google find what you're looking for... ...and exactly how do they make money doing so? What structural properties might we expect any social network to have? How does your position in an economic network (dis)advantage you? How are individual and collective behavior related in complex networks? What might we mean by the economics of spam? What do game theory and the Paris subway have to do with Internet routing? What's going on in the pictures to the left and right? Networked Life looks at how our world is connected -- socially, economically, strategically and technologically -- and why it matters. The answers to the questions above are related. They have been the subject of a fascinating intersection of disciplines including computer science, physics, psychology, mathematics, economics and finance. Researchers from these areas all strive to quantify and explain the growing complexity and connectivity of the world around us, and they have begun to develop a rich new science along the way. Networked Life will explore recent scientific efforts to explain social, economic and technological structures -- and the way these structures interact -- on many different scales, from the behavior of individuals or small groups to that of complex networks such as the Internet and the global economy. This course covers computer science topics and other material that is mathematical, but all material will be presented in a way that is accessible to an educated audience with or without a strong technical background. The course is open to all majors and all levels, and is taught accordingly. There will be ample opportunities for those of a quantitative bent to dig deeper into the topics we examine. The majority of the course is grounded in scientific and mathematical findings of the past two decades or less.

Research on Social Network Sites

A bibliography of research on Social Network web sites. The research contained below is focused specifically on social network sites (or "social networking" sites). Some of this is connected to social media, social software, Web2.0, social bookmarking, educational technologies, communities research, etc. but this is not the organizing focus and not everything related to these other topics is included here. This list is not methodologically or disciplinarily organized. There is work here from communications, information science, anthropology, sociology, economics, political science, cultural studies, computer science, etc.

Introduction to Network Mathematics

Introduction to Network Mathematics provides college students with basic graph theory to better understand the Internet

Big Data: Wrongs and Rights by Andrew Cormack (WAIS Seminar)

Abstract: Big Data has been characterised as a great economic opportunity and a massive threat to privacy. Both may be correct: the same technology can indeed be used in ways that are highly beneficial and those that are ethically intolerable, maybe even simultaneously. Using examples of how Big Data might be used in education - normally referred to as "learning analytics" - the seminar will discuss possible ethical and legal frameworks for Big Data, and how these might guide the development of technologies, processes and policies that can deliver the benefits of Big Data without the nightmares. Speaker Biography: Andrew Cormack is Chief Regulatory Adviser, Jisc Technologies. He joined the company in 1999 as head of the JANET-CERT and EuroCERT incident response teams. In his current role he concentrates on the security, policy and regulatory issues around the network and services that Janet provides to its customer universities and colleges. Previously he worked for Cardiff University running web and email services, and for NERC's Shipboard Computer Group. He has degrees in Mathematics, Humanities and Law.

Data-Driven Text Generation using Neural Networks & Provenance is Complicated and Boring — Is there a solution?

Title: Data-Driven Text Generation using Neural Networks Speaker: Pavlos Vougiouklis, University of Southampton Abstract: Recent work on neural networks shows their great potential at tackling a wide variety of Natural Language Processing (NLP) tasks. This talk will focus on the Natural Language Generation (NLG) problem and, more specifically, on the extend to which neural network language models could be employed for context-sensitive and data-driven text generation. In addition, a neural network architecture for response generation in social media along with the training methods that enable it to capture contextual information and effectively participate in public conversations will be discussed. Speaker Bio: Pavlos Vougiouklis obtained his 5-year Diploma in Electrical and Computer Engineering from the Aristotle University of Thessaloniki in 2013. He was awarded an MSc degree in Software Engineering from the University of Southampton in 2014. In 2015, he joined the Web and Internet Science (WAIS) research group of the University of Southampton and he is currently working towards the acquisition of his PhD degree in the field of Neural Network Approaches for Natural Language Processing. Title: Provenance is Complicated and Boring — Is there a solution? Speaker: Darren Richardson, University of Southampton Abstract: Paper trails, auditing, and accountability — arguably not the sexiest terms in computer science. But then you discover that you've possibly been eating horse-meat, and the importance of provenance becomes almost palpable. Having accepted that we should be creating provenance-enabled systems, the challenge of then communicating that provenance to casual users is not trivial: users should not have to have a detailed working knowledge of your system, and they certainly shouldn't be expected to understand the data model. So how, then, do you give users an insight into the provenance, without having to build a bespoke system for each and every different provenance installation? Speaker Bio: Darren is a final year Computer Science PhD student. He completed his undergraduate degree in Electronic Engineering at Southampton in 2012.

IBM's Internet of Things and Academic Initiative

IBM provide a comprehensive academic initiative, (http://www-304.ibm.com/ibm/university/academic/pub/page/academic_initiative) to universities, providing them free of charge access to a wide range of IBM Software. As part of this initiative we are currently offering free IBM Bluemix accounts, either to be used within a course, or for students to use for personal skills development. IBM Bluemix provides a comprehensive cloud based platform as a service solution set which includes the ability to quickly and easily integrate data from devices from Internet of Things ( IoT) solutions to develop and run productive and user focused web and mobile applications. If you would be interested in hearing more about IBM and Internet of Things or you would like to discuss prospective research projects that you feel would operate well in this environment, please come along to the seminar!