Friday, May 4, 2012

Religious sites 'riskier than porn for viruses'

Religious sites 'riskier than porn for viruses'

Web wanderers are more likely to get a computer virus by visiting a religious website than by peering at porn, according to a study released on Tuesday.

"Drive-by attacks" in which hackers booby-trap legitimate websites with malicious code continue to be a bane, the US-based anti-virus vendor Symantec said in its Internet Security Threat Report.

Websites with religious or ideological themes were found to have triple the average number of "threats" that those featuring adult content, according to Symantec.

"It is interesting to note that websites hosting adult/pornographic content are not in the top five, but ranked tenth," Symantec said in the report.

"We hypothesize that this is because pornographic website owners already make money from the Internet and, as a result, have a vested interest in keeping their sites malware-free; it's not good for repeat business."

The report was based on information gathered last year by the Symantec Global Intelligence Network, which monitors cyber attack activity in more than 200 countries through its services and sensors.

Symantec said that it blocked 5.5 billion attacks in 2011 in an increase of 81 percent from the prior year.

In keeping with trends seen by other Internet security firms, Symantec reported surges in hacks aimed at smartphones or tablet computers and in attacks targeting workers in companies or government agencies.

Posted via email from Tony Burkhart - Information Sciences - User identity verification via mouse dynamics


Identity theft is a crime in which hackers perpetrate fraudulent activity under stolen identities by using credentials, such as passwords and smartcards, unlawfully obtained from legitimate users or by using logged-on computers that are left unattended. User verification methods provide a security layer in addition to the username and password by continuously validating the identity of logged-on users based on their physiological and behavioral characteristics.

We introduce a novel method that continuously verifies users according to characteristics of their interaction with the mouse.

The contribution of this work is threefold: first, user verification is derived based on the classification results of each individual mouse action, in contrast to methods which aggregate mouse actions. Second, we propose a hierarchy of mouse actions from which the features are extracted. Third, we introduce new features to characterize the mouse activity which are used in conjunction with features proposed in previous work.

The proposed algorithm outperforms current state-of-the-art methods by achieving higher verification accuracy while reducing the response time of the system.

Posted via email from Tony Burkhart