Votetrust: Leveraging friend invitation graph to defend against social network sybils

Online social networks (OSNs) suffer from the creation of fake accounts that introduce fake product reviews, malware and spam. Existing defenses focus on using the social graph structure to isolate fakes. However, our work shows that Sybils could befriend a large number of real users, invalidating the assumption behind social-graph-based detection. In this paper, we present VoteTrust, a scalable defense system that further leverages user-level activities. VoteTrust models the friend invitation interactions among users as a directed, signed graph, and uses two key mechanisms to detect Sybils over the graph: a voting-based Sybil detection to find Sybils that users vote to reject, and a Sybil community detection to find other colluding Sybils around identified Sybils. Through evaluating on Renren social network, we show that VoteTrust is able to prevent Sybils from generating many unsolicited friend requests. We also deploy VoteTrust in Renen, and our real experience demonstrates that VoteTrust can detect large-scale collusion among Sybils

Mitigating Colluding Attacks in Online Social Networks and Crowdsourcing Platforms

Online Social Networks (OSNs) have created new ways for people to communicate, and for companies to engage their customers -- with these new avenues for communication come new vulnerabilities that can be exploited by attackers. This dissertation aims to investigate two attack models: Identity Clone Attacks (ICA) and Reconnaissance Attacks (RA). During an ICA, attackers impersonate users in a network and attempt to infiltrate social circles and extract confidential information. In an RA, attackers gather information on a target\u27s resources, employees, and relationships with other entities over public venues such as OSNs and company websites. This was made easier for the RA to be efficient because well-known social networks, such as Facebook, have a policy to force people to use their real identities for their accounts. The goal of our research is to provide mechanisms to defend against colluding attackers in the presence of ICA and RA collusion attacks. In this work, we consider a scenario not addressed by previous works, wherein multiple attackers collude against the network, and propose defense mechanisms for such an attack. We take into account the asymmetric nature of social networks and include the case where colluders could add or modify some attributes of their clones. We also consider the case where attackers send few friend requests to uncover their targets. To detect fake reviews and uncovering colluders in crowdsourcing, we propose a semantic similarity measurement between reviews and a community detection algorithm to overcome the non-adversarial attack. ICA in a colluding attack may become stronger and more sophisticated than in a single attack. We introduce a token-based comparison and a friend list structure-matching approach, resulting in stronger identifiers even in the presence of attackers who could add or modify some attributes on the clone. We also propose a stronger RA collusion mechanism in which colluders build their own legitimacy by considering asymmetric relationships among users and, while having partial information of the networks, avoid recreating social circles around their targets. Finally, we propose a defense mechanism against colluding RA which uses the weakest person (e.g., the potential victim willing to accept friend requests) to reach their target

The role of approximate negators in modeling the automatic detection of negation in tweets

Although improvements have been made in the performance of sentiment analysis tools, the automatic detection of negated text (which affects negative sentiment prediction) still presents challenges. More research is needed on new forms of negation beyond prototypical negation cues such as “not” or “never.” The present research reports findings on the role of a set of words called “approximate negators,” namely “barely,” “hardly,” “rarely,” “scarcely,” and “seldom,” which, in specific occasions (such as attached to a word from the non-affirmative adverb “any” family), can operationalize negation styles not yet explored. Using a corpus of 6,500 tweets, human annotation allowed for the identification of 17 recurrent usages of these words as negatives (such as “very seldom”) which, along with findings from the literature, helped engineer specific features that guided a machine learning classifier in predicting negated tweets. The machine learning experiments also modeled negation scope (i.e. in which specific words are negated in the text) by employing lexical and dependency graph information. Promising results included F1 values for negation detection ranging from 0.71 to 0.89 and scope detection from 0.79 to 0.88. Future work will be directed to the application of these findings in automatic sentiment classification, further exploration of patterns in data (such as part-of-speech recurrences for these new types of negation), and the investigation of sarcasm, formal language, and exaggeration as themes that emerged from observations during corpus annotation

February 12, 2009

The Breeze is the student newspaper of James Madison University in Harrisonburg, Virginia

Future of the Internet--and how to stop it

vi, 342 p. : ill. ; 25 cmLibro ElectrónicoOn January 9, 2007, Steve Jobs introduced the iPhone to an eager audience crammed into San Francisco’s Moscone Center.1 A beautiful and brilliantly engineered device, the iPhone blended three products into one: an iPod, with the highest-quality screen Apple had ever produced; a phone, with cleverly integrated functionality, such as voicemail that came wrapped as separately accessible messages; and a device to access the Internet, with a smart and elegant browser, and with built-in map, weather, stock, and e-mail capabilities. It was a technical and design triumph for Jobs, bringing the company into a market with an extraordinary potential for growth, and pushing the industry to a new level of competition in ways to connect us to each other and to the Web.Includes bibliographical references (p. 249-328) and index Acceso restringido a miembros del Consorcio de Bibliotecas Universitarias de Andalucía Electronic reproduction. Palo Alto, Calif. : ebrary, 2009 Modo de acceso : World Wide Webpt. 1. The rise and stall of the generative Net -- Battle of the boxes -- Battle of the networks -- Cybersecurity and the generative dilemma -- pt. 2. After the stall -- The generative pattern -- Tethered appliances, software as service, and perfect enforcement -- The lessons of Wikipedia -- pt. 3. Solutions -- Stopping the future of the Internet : stability on a generative Net -- Strategies for a generative future -- Meeting the risks of generativity : Privacy 2.0. Index32

Evaluating the Effectiveness of Counter-Narrative Tactics in Preventing Radicalization

The U.S. Department of State disseminates counter-radicalization information through social media but has been unable to reach users due to an inability to create engaging posts due to a lack of understanding of the interests of the general population. The purpose of this quantitative study was to assess the utility of data analytics when administering counter-radicalization social media campaigns. The population for this study were social media posts published on the Quilliam Facebook page between 1 January 2018 and 31 December 2018. The nonexperimental quantitative descriptive research design sought to examine the correlation between the independent variables (topic of a post, use of visual aids in the post, and the geopolitical region the post addresses) and the dependent variables (resulting likes and shares). This study relied on the strategic choice theory which argues that individuals perform a cost and benefit analysis when deciding to join a terrorist organization and commit acts of terrorism. Specifically, individuals are often interested in participating in terror-ism in an effort to gain resources and feel a sense of belonging but can be dissuaded upon realization that terrorism can actually degrade their quality of life. The research found that social media can be used as a tool to increase the perceived costs of terrorism and decrease the perceived benefits of terrorism. The study concluded that posts which involved a personal story emphasizing the ramifications of terrorism and included a video resulted in the highest number of likes and shares, respectively. The findings provide a strong argument for utilizing data analytics to improve the dissemination of counter-radicalization information which could prevent individuals from joining terrorist organizations and committing acts of terrorism