{"id":161399,"date":"2011-06-01T00:00:00","date_gmt":"2011-06-01T00:00:00","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/msr-research-item\/sex-lies-and-cyber-crime-surveys\/"},"modified":"2018-10-16T20:12:35","modified_gmt":"2018-10-17T03:12:35","slug":"sex-lies-and-cyber-crime-surveys","status":"publish","type":"msr-research-item","link":"https:\/\/www.microsoft.com\/en-us\/research\/publication\/sex-lies-and-cyber-crime-surveys\/","title":{"rendered":"Sex, Lies and Cyber-crime Surveys"},"content":{"rendered":"<div class=\"asset-content\">\n<p>Much of the information we have on cyber-crime losses is derived from surveys. We examine some of the difficulties of forming an accurate estimate by survey. First, losses are extremely concentrated, so that representative sampling of the population does not give representative sampling of the losses. Second, losses are based on unverified self-reported numbers. Not only is it possible for a single outlier to distort the result, we find evidence that most surveys are dominated by a minority of responses in the upper tail (i.e., a majority of the estimate is coming from as few as one or two responses). Finally, the fact that losses are confined to a small segment of the population magnifies the difficulties of refusal rate and small sample sizes. Far from being broadly-based estimates of losses across the population, the cyber-crime estimates that we have appear to be largely the answers of a handful of people extrapolated to the whole population. A single individual who claims <i>50,000 losses, in an N=1000 person survey, is all it takes to generate a <\/i>10 billion loss over the population. One unverified claim of <i>7,500 in phishing losses translates into <\/i>1.5 billion.<\/p>\n<\/div>\n<p><!-- .asset-content --><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Much of the information we have on cyber-crime losses is derived from surveys. We examine some of the difficulties of forming an accurate estimate by survey. First, losses are extremely concentrated, so that representative sampling of the population does not give representative sampling of the losses. Second, losses are based on unverified self-reported numbers. Not 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