Emerging Phishing Trends and Effectiveness of the Anti-Phishing Landing Page

Each month, more attacks are launched with the aim of making web users believe that they are communicating with a trusted entity which compels them to share their personal, financial information. Phishing costs Internet users billions of dollars every year. Researchers at Carnegie Mellon University (CMU) created an anti-phishing landing page supported by Anti-Phishing Working Group (APWG) with the aim to train users on how to prevent themselves from phishing attacks. It is used by financial institutions, phish site take down vendors, government organizations, and online merchants. When a potential victim clicks on a phishing link that has been taken down, he / she is redirected to the landing page. In this paper, we present the comparative analysis on two datasets that we obtained from APWG's landing page log files; one, from September 7, 2008 - November 11, 2009, and other from January 1, 2014 - April 30, 2014. We found that the landing page has been successful in training users against phishing. Forty six percent users clicked lesser number of phishing URLs from January 2014 to April 2014 which shows that training from the landing page helped users not to fall for phishing attacks. Our analysis shows that phishers have started to modify their techniques by creating more legitimate looking URLs and buying large number of domains to increase their activity. We observed that phishers are exploiting ICANN accredited registrars to launch their attacks even after strict surveillance. We saw that phishers are trying to exploit free subdomain registration services to carry out attacks. In this paper, we also compared the phishing e-mails used by phishers to lure victims in 2008 and 2014. We found that the phishing e-mails have changed considerably over time. Phishers have adopted new techniques like sending promotional e-mails and emotionally targeting users in clicking phishing URLs

Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the pattern by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game makes a decision among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information

Building coalitions, creating change: An agenda for gender transformative research in agricultural development

The CGIAR Research Program on Aquatic Agricultural Systems (AAS) has developed its Gender Research in Development Strategy centered on a transformative approach. Translating this strategy into actual research and development practice poses a considerable challenge, as not much (documented) experience exists in the agricultural sector to draw on, and significant innovation is required. A process of transformative change requires reflecting on multiple facets and dimensions simultaneously. This working paper is a collation of think pieces, structured around broad the mes and topics, reflecting on what works (and what does not) in the application of gender transformative approaches in agriculture and other sectors, and seeking to stimulate a discussion on the way forward for CGIAR Research Programs (CRPs) and other programs to build organizational capacities and partnerships

Accepting Collective Responsibility for the Future

Existing institutions do not seem well-designed to address paradigmatically global, intergenerational and ecological problems, such as climate change. 1 In particular, they tend to crowd out intergenerational concern, and thereby facilitate a “tyranny of the contemporary” in which successive generations exploit the future to their own advantage in morally indefensible ways (albeit perhaps unintentionally). Overcoming such a tyranny will require both accepting responsibility for the future and meeting the institutional gap. I propose that we approach the first in terms of a traditional “delegated responsibility” model of the transmission of individual responsibility to collectives, and the second with a call for a global constitutional convention focused on future generations. In this paper, I develop the delegated responsibility model by suggesting how it leads us to understand both past failures and prospective responsibility. I then briefly defend the call for a global constitutional convention

Effects of Knowledge Base Quality on Peer-to-peer Information Propagation

Peer reviewedPublisher PD

Open Source Software: From Open Science to New Marketing Models

-Open source Software; Intellectual Property; Licensing; Business Model.