Study: Online retail revenues, attacks surge on Cyber Monday

Bedford, Mass. – Daily mobile/online retail revenues surge an average of 55% on Cyber Monday (the Monday after Thanksgiving), but a corresponding surge in attacks drives hard losses, on average, as much as $500,000 per hour or $8,000 per minute. In addition, a new study of 1,100 U.S., and U.K., retail IT staffers from RSA and the Ponemon Institute shows that 66% of respondents expect that disruption would result in customer churn that would damage reputation and brand and could push losses as high as $3.4 million from a single hour of disruption.



While 64% of organizations see significant increases in attack activity, more than 70% of organizations do not take additional precautions in anticipation of increased attacks. Additionally, with current capabilities, 51% say that they do not have real-time visibility into web traffic making it difficult to identify the root cause of such attacks, leaving only 23% feeling that most attacks can be quickly detected and remediated.



The report also identifies the top nine scenarios organizations will likely face approaching Cyber Monday with the vast majority categorizing these as difficult or very difficult to detect. In order of likelihood, the attack scenarios are botnet and distributed denial of service, app store fraud, mobile access/account compromise, click fraud, stolen credit card/validation, e-coupon abuse, account hijacking, electronic wallet abuse, and brand promotion hijacking.



"The competitive climate and the unpredictability of the economy does not leave organizations much margin for business error,” said Demetrios Lazankos, IT threat strategist for RSA. “Unfortunately, the stealth and savvy cybercriminals have advanced to a point where traditional security and fraud defenses on which businesses rely on are at best insufficient and at worst...obsolete. Business logic abuse hides in plain sight because it uses 'legitimate' processes for illegitimate gain. The problem requires universal visibility, a risk layered approach, and a new way of understanding the adversary. Isolating the outliers in crowd behavior that indicate attacks is critical for identifying malicious behavior and business logic abuse."