Fighting cyber crime, phishing and malware attacks has never been more challenging with the advancement of technology. Now it is time to use technology against itself. This interesting news came to us from Venture Beat in their article, “Researchers use AI to combat and quantify browser fingerprinting.”
Many web browsers have begun providing protections against cross-site tracking methods employing cookies and IP addresses. It’s an encouraging development, but there is fear that it will push criminals to adopt more opaque tracking like browser fingerprinting, which tracks browsers by the configuration information they make visible.
To combat fingerprinting in particular, researchers are investigating the use of a machine learning-based approach called FP-Inspector that trains classifiers to learn fingerprinting. By extracting syntactic and semantic features through a combination of static and dynamic analyses that effectively complement each others’ limitations, FP-Inspector overcomes the coverage issues of dynamic analysis while addressing the inability of static analysis to handle obfuscation.
Some browsers and privacy tools have tried to mitigate fingerprinting using techniques like API changes and network request blocking. However, these require manual analysis where the machine learning approach will be automatic.
Melody K. Smith
Sponsored by Data Harmony, a unit of Access Innovations, the world leader in indexing and making content findable.
