Malicious web pages that use drive-by download attacks or social engineering techniques to install unwanted software on a user's computer have become the main avenue for the propagation of malicious code. To search for malicious web pages, the first step is typically to use a crawler to collect URLs that are live on the Internet. Then, fast prefiltering techniques are employed to reduce the amount of pages that need to be examined by more precise, but slower, analysis tools (such as honeyclients). While effective, these techniques require a substantial amount of resources. A key reason is that the crawler encounters many pages on the web that are benign, that is, the "toxicity" of the stream of URLs being analyzed is low.In this paper, we present EVILSEED, an approach to search the web more efficiently for pages that are likely malicious. EVILSEED starts from an initial seed of known, malicious web pages. Using this seed, our system automatically generates search engines queries to identify other malicious pages that are similar or related to the ones in the initial seed. By doing so, EVILSEED leverages the crawling infrastructure of search engines to retrieve URLs that are much more likely to be malicious than a random page on the web. In other words EVILSEED increases the "toxicity" of the input URL stream. Also, we envision that the features that EVILSEED presents could be directly applied by search engines in their prefilters. We have implemented our approach, and we evaluated it on a large-scale dataset. The results show that EVILSEED is able to identify malicious web pages more efficiently when compared to crawler-based approaches.

The retina is a complex assembly of neurons packed into a three-layer structure containing five classes of cells. Each class of retinal cells is regularly arranged within its layer in an orderly configuration called the retinal mosaic. We have set up a mathematical model of retinal mosaic formation focusing on the actions of local mechanical forces on the neuron's cytoskeleton. The cytoskeleton has been modeled according to two approaches, one based on the tensegrity concept (a structure made of elastic and rigid elements), and the other based on a simple model with viscoelastic features. We have assumed causing deformation of their cytoskeleton, overlap of dendritic areas and movement of the neuron. Simulations based on these two models indicate that a random distribution of neurons reaches an orderly configuration by local and mechanical neuron interaction in the case in which the cytoskeleton is modeled using the tensegrity approach, but not when the neuron is modeled as a purely viscoelastic system. Considering that the main structural difference between the Maxwell model and the tensegrity model is that the latter model contains rigid elements whereas the former does not, this suggests that the presence of rigid components in the cytoskeleton of retinal neurons plays a key role in the formation processes of the retinal mosaic.

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