Iliou, C., Kalpakis, G., Tsikrika, T., Vrochidis, S., Kompatsiaris, I..
2016.
Hybrid Focused Crawling for Homemade Explosives Discovery on Surface and Dark Web. 2016 11th International Conference on Availability, Reliability and Security (ARES). :229–234.
This work proposes a generic focused crawling framework for discovering resources on any given topic that reside on the Surface or the Dark Web. The proposed crawler is able to seamlessly traverse the Surface Web and several darknets present in the Dark Web (i.e. Tor, I2P and Freenet) during a single crawl by automatically adapting its crawling behavior and its classifier-guided hyperlink selection strategy based on the network type. This hybrid focused crawler is demonstrated for the discovery of Web resources containing recipes for producing homemade explosives. The evaluation experiments indicate the effectiveness of the proposed ap-proach both for the Surface and the Dark Web.
Baravalle, A., Lopez, M. S., Lee, S. W..
2016.
Mining the Dark Web: Drugs and Fake Ids. 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW). :350–356.
In the last years, governmental bodies have been futilely trying to fight against dark web marketplaces. Shortly after the closing of "The Silk Road" by the FBI and Europol in 2013, new successors have been established. Through the combination of cryptocurrencies and nonstandard communication protocols and tools, agents can anonymously trade in a marketplace for illegal items without leaving any record. This paper presents a research carried out to gain insights on the products and services sold within one of the larger marketplaces for drugs, fake ids and weapons on the Internet, Agora. Our work sheds a light on the nature of the market, there is a clear preponderance of drugs, which accounts for nearly 80% of the total items on sale. The ready availability of counterfeit documents, while they make up for a much smaller percentage of the market, raises worries. Finally, the role of organized crime within Agora is discussed and presented.
Preotiuc-Pietro, Daniel, Carpenter, Jordan, Giorgi, Salvatore, Ungar, Lyle.
2016.
Studying the Dark Triad of Personality Through Twitter Behavior. Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. :761–770.
Research into the darker traits of human nature is growing in interest especially in the context of increased social media usage. This allows users to express themselves to a wider online audience. We study the extent to which the standard model of dark personality – the dark triad – consisting of narcissism, psychopathy and Machiavellianism, is related to observable Twitter behavior such as platform usage, posted text and profile image choice. Our results show that we can map various behaviors to psychological theory and study new aspects related to social media usage. Finally, we build a machine learning algorithm that predicts the dark triad of personality in out-of-sample users with reliable accuracy.
Collarana, Diego, Lange, Christoph, Auer, Sören.
2016.
FuhSen: A Platform for Federated, RDF-based Hybrid Search. Proceedings of the 25th International Conference Companion on World Wide Web. :171–174.
The increasing amount of structured and semi-structured information available on the Web and in distributed information systems, as well as the Web's diversification into different segments such as the Social Web, the Deep Web, or the Dark Web, requires new methods for horizontal search. FuhSen is a federated, RDF-based, hybrid search platform that searches, integrates and summarizes information about entities from distributed heterogeneous information sources using Linked Data. As a use case, we present scenarios where law enforcement institutions search and integrate data spread across these different Web segments to identify cases of organized crime. We present the architecture and implementation of FuhSen and explain the queries that can be addressed with this new approach.
Truvé, Staffan.
2016.
Temporal Analytics for Predictive Cyber Threat Intelligence. Proceedings of the 25th International Conference Companion on World Wide Web. :867–868.
Recorded Future has developed its Temporal Analytics Engine as a general purpose platform for harvesting and analyzing unstructured text from the open, deep, and dark web, and for transforming that content into a structured representation suitable for different analyses. In this paper we present some of the key components of our system, and show how it has been adapted to the increasingly important domain of cyber threat intelligence. We also describe how our data can be used for predictive analytics, e.g. to predict the likelihood of a product vulnerability being exploited or to assess the maliciousness of an IP address.