Predicting threat potential using cyber sensors

The proliferation of the Internet has created a culture of a connected society dependent upon technology for communication and information sharing needs. In this dissertation, we hypothesize that attackers are increasingly using electronic resources that are capable of leaving a digital footprint, such as social media services, e-mail, text messages, blogs, and websites for the communication, planning, and coordination of attacks. In its current form, however, traffic analysis is primarily concerned with using communications volume to extract intelligence information, but largely ignores the content of communications transmissions that is needed to meet the security challenges and demands of continually emerging threats. In this dissertation, we make use of the enormous amount of electronic data potential and propose a model framework that is capable of predicting malicious intent based on mathematically sound principles in traffic flow theory. We define a set of objects, called threat agents, acting on a threat network and derive the set of values and conditions that allow us to predict the behavior of the network much in the same way a traffic flow model can be used to predict the behavior of a road system. This is accomplished using a set of variables created analogous to velocity, density, and flux in traffic flow theory that allow us to measure the level of congestion on which the threat prediction is based. In this dissertation, we also apply the data mining techniques of classification and clustering analyses to derive not only the basis for our threat network but also to generate locational and categorical information. This contextual information provides a more complete picture of the potential threat that allows us to be in a position to better understand and respond to impending threats in a timely manner. We present experimental results obtained on a set of articles appearing on the Reuters newswire to predict threats defined within the context of the data set. Using a threat prediction profile produced from the model framework, we validate our test results by mapping the predicted threats to actual event occurrences contained within the data set itself with promising results

Establishing a resource center: A guide for organizations supporting community foundations

Maintaining a resource center such as a library is a central tasks of an association to serve its members, though one of the first to be neglected. WINGS-CF commissioned this guide to assist organizations supporting community foundations to review and organize their resource items, and to propose several classification systems / taxonomies

"May I borrow Your Filter?" Exchanging Filters to Combat Spam in a Community

Leveraging social networks in computer systems can be effective in dealing with a number of trust and security issues. Spam is one such issue where the "wisdom of crowds" can be harnessed by mining the collective knowledge of ordinary individuals. In this paper, we present a mechanism through which members of a virtual community can exchange information to combat spam. Previous attempts at collaborative spam filtering have concentrated on digest-based indexing techniques to share digests or fingerprints of emails that are known to be spam. We take a different approach and allow users to share their spam filters instead, thus dramatically reducing the amount of traffic generated in the network. The resultant diversity in the filters and cooperation in a community allows it to respond to spam in an autonomic fashion. As a test case for exchanging filters we use the popular SpamAssassin spam filtering software and show that exchanging spam filters provides an alternative method to improve spam filtering performance

Virtual Geodemographics: Repositioning Area Classification for Online and Offline Spaces

Computer mediated communication and the Internet has fundamentally changed how consumers and producers connect and interact across both real space, and has also opened up new opportunities in virtual spaces. This paper describes how technologies capable of locating and sorting networked communities of geographically disparate individuals within virtual communities present a sea change in the conception, representation and analysis of socioeconomic distributions through geodemographic analysis. We argue that through virtual communities, social networks between individuals may subsume the role of neighbourhood areas as the most appropriate units of analysis, and as such, geodemographics needs to be repositioned in order to accommodate social similarities in virtual, as well as geographical, space. We end the paper by proposing a new model for geodemographics which spans both real and virtual geographies

Topology comparison of Twitter diffusion networks effectively reveals misleading information

In recent years, malicious information had an explosive growth in social media, with serious social and political backlashes. Recent important studies, featuring large-scale analyses, have produced deeper knowledge about this phenomenon, showing that misleading information spreads faster, deeper and more broadly than factual information on social media, where echo chambers, algorithmic and human biases play an important role in diffusion networks. Following these directions, we explore the possibility of classifying news articles circulating on social media based exclusively on a topological analysis of their diffusion networks. To this aim we collected a large dataset of diffusion networks on Twitter pertaining to news articles published on two distinct classes of sources, namely outlets that convey mainstream, reliable and objective information and those that fabricate and disseminate various kinds of misleading articles, including false news intended to harm, satire intended to make people laugh, click-bait news that may be entirely factual or rumors that are unproven. We carried out an extensive comparison of these networks using several alignment-free approaches including basic network properties, centrality measures distributions, and network distances. We accordingly evaluated to what extent these techniques allow to discriminate between the networks associated to the aforementioned news domains. Our results highlight that the communities of users spreading mainstream news, compared to those sharing misleading news, tend to shape diffusion networks with subtle yet systematic differences which might be effectively employed to identify misleading and harmful information.Comment: A revised new version is available on Scientific Report