1,144 research outputs found
Effective Iterative Techniques for Fingerprinting Design IP
Fingerprinting is an approach that assigns a unique
and invisible ID to each sold instance of the intellectual property
(IP). One of the key advantages fingerprinting-based intellectual
property protection (IPP) has over watermarking-based IPP is the
enabling of tracing stolen hardware or software. Fingerprinting
schemes have been widely and effectively used to achieve this goal;
however, their application domain has been restricted only to static
artifacts, such as image and audio, where distinct copies can be
obtained easily. In this paper, we propose the first generic fingerprinting
technique that can be applied to an arbitrary synthesis
(optimization or decision) or compilation problem and, therefore
to hardware and software IPs.
The key problem with design IP fingerprinting is that there is a
need to generate a large number of structurally unique but functionally
and timing identical designs. To reduce the cost of generating
such distinct copies, we apply iterative optimization in an incremental
fashion to solve a fingerprinted instance. Therefore, we
leverage on the optimization effort already spent in obtaining previous
solutions, yet we generate a uniquely fingerprinted new solution.
This generic approach is the basis for developing specific fingerprinting
techniques for four important problems in VLSI CAD:
partitioning, graph coloring, satisfiability, and standard-cell placement.
We demonstrate the effectiveness of the new fingerprinting-based
IPP techniques on a number of standard benchmarks
No NAT'd User left Behind: Fingerprinting Users behind NAT from NetFlow Records alone
It is generally recognized that the traffic generated by an individual
connected to a network acts as his biometric signature. Several tools exploit
this fact to fingerprint and monitor users. Often, though, these tools assume
to access the entire traffic, including IP addresses and payloads. This is not
feasible on the grounds that both performance and privacy would be negatively
affected. In reality, most ISPs convert user traffic into NetFlow records for a
concise representation that does not include, for instance, any payloads. More
importantly, large and distributed networks are usually NAT'd, thus a few IP
addresses may be associated to thousands of users. We devised a new
fingerprinting framework that overcomes these hurdles. Our system is able to
analyze a huge amount of network traffic represented as NetFlows, with the
intent to track people. It does so by accurately inferring when users are
connected to the network and which IP addresses they are using, even though
thousands of users are hidden behind NAT. Our prototype implementation was
deployed and tested within an existing large metropolitan WiFi network serving
about 200,000 users, with an average load of more than 1,000 users
simultaneously connected behind 2 NAT'd IP addresses only. Our solution turned
out to be very effective, with an accuracy greater than 90%. We also devised
new tools and refined existing ones that may be applied to other contexts
related to NetFlow analysis
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