Combining commercial consensus and community crowd-sourced categorization of web sites for integrity against phishing and other web fraud

Traditionally, the protection provided by 3rd party anti-Malware endpoint security products is measured using a sample set that is representative of the prevalent universe of attacks at that point in time (malicious URLs and/or malicious files in the world). The methodology used for such a selection of the Malware attack samples, the so-called Stimulus Workload (SW), has been a matter of controversy for a number of years. The reason is simple. Given a carefully crafted selection of such files or URLs, then, the results of the measurements can varied drastically favoring one vendor versus the other. In [1], Colon Osorio, et.al. argued that the selection process must be strictly regulated, and further, that such a selection must take into account the fact that amongst the samples selected, some pose a greater threat to users than others, as they are more widespread, and hence are more likely to affect a given user. Further, some Malware attack samples may only be found on specific websites, affect specific countries/regions, or only be relevant to a particular operating system version or interface languages (English, German, Chinese, and so forth). In [1], [2], the idea of a Customizable Stimulus Workloads, (CSW) was first suggested, whereas, the collection of samples selected as the Stimulus Workload is required to take into account all the elements described above. Within this context, CSWs are created by filtering attack samples base on prevalence, geographic regions, customer application environments, and other factors. Within the context of this methodology, in this manuscript we will pay special attention to one such specific application environment, primarily, Social Networks. With such a target environment in mind, a CSW was created and used to evaluate the performance of end-point security products. Basically, we examine the protection provided against Malware that uses internet Social Networks as part of the attack vector. When Social Network CSWs are used,- together with differential metrics of effectiveness, we found that amongst the Social Networks studied (Facebook, Google+, and Twitter) the amount of inherent protection provided ranged from negligible to a level that we will call modest self-protection (0% to 18% prevention rate). Further, results of our evaluation showed that the supplemental protection provided by 3rd party anti-Malware products was erratic, ranging from a low of 0% to a high of 93% depending on the product and/or Social Network combination.