Collaborate Disseminate
Researchers are able to create fake fingerprints that result in a 20% false-positive rate. The problem is that these sensors obtain only partial images of users’ fingerprints — at the points where they make contact with the scanner. The paper noted th… Continue reading Using Machine Learning to Create Fake Fingerprints
Researchers are able to create fake fingerprints that result in a 20% false-positive rate. The problem is that these sensors obtain only partial images of users’ fingerprints — at the points where they make contact with the scanner. The paper noted that since partial prints are not as distinctive as complete prints, the chances of one partial print getting matched… Continue reading Using Machine Learning to Create Fake Fingerprints
James Mickens gave an excellent keynote at the USENIX Security Conference last week, talking about the social aspects of security — racism, sexism, etc. — and the problems with machine learning and the Internet. Worth watching…. Continue reading James Mickens on the Current State of Computer Security
Fascinating research de-anonymizing code — from either source code or compiled code: Rachel Greenstadt, an associate professor of computer science at Drexel University, and Aylin Caliskan, Greenstadt’s former PhD student and now an assistant professor at George Washington University, have found that code, like other forms of stylistic expression, are not anonymous. At the DefCon hacking conference Friday, the pair… Continue reading Identifying Programmers by their Coding Style
Really interesting article: A trained eye (or even a not-so-trained one) can discern when something phishy is going on with a domain or subdomain name. There are search tools, such as Censys.io, that allow humans to specifically search through the massive pile of certificate log entries for sites that spoof certain brands or functions common to identity-processing sites. But it’s… Continue reading Detecting Phishing Sites with Machine Learning