Popularity-based congestion control in named data networking[THESIS NS2]

Popularity-based congestion control in named data networking

However, this would result, in collecting traces during  days at first order and with the setup presented in annex A. Popularity-based congestion control in named data networking This leads to the question: how efficiently and quickly position tiny probes  One may think, to solve this problem, by performing a EMnear field scan NFS to localize the cryptographic block. However this is often insufficient. Popularity-based congestion control in named data networking[THESIS NS2]_As an illustration, shows a peak to peak cartography obtained with the setup introduced in annex A of the EM emissions measured at several coordinates above the FPGA package during a DES ciphering.  is an X-Ray photography of the package containing the circuit under attack.

Finally, discloses the floorplan of the circuit, running at and integrating a DES module,v Popularity-based congestion control in named data networking a finite-state machine and a interface for communication purpose. As shown, it appears impossible to correlate even if the die area is known thanks to the X-ray photography. It appears all the more difficult to localize the DES module and thus decide where to position the magnetic sensor to perform a successful focused DEMA or CEMA to avoid potential global hardware countermeasures. Basics of Global Magnitude Squared Incoherence DEMAexploits the data dependent behavior of theEMemissions radiated by Popularity-based congestion control in named data networkingcircuits during cryptographic operations. EM emissions are generated by flows of electrical charges through the wires connecting logic gates but also trough wires supplying the circuit  Since the switching of gates generates a current flow through the circuit interconnect, we may conclude that gates generate some data dependentEM emissions at different points above the circuit according to the power distribution network. These data dependent behaviors is exploited by statistical means to retrieve the secret key. Even if the timing and power characteristics of CMOS gates are known, it is difficult to deduce any characteristic about the EM emissions generated of actual IC due to the complexity of their power distribution grid.

Popularity-based congestion control in named data networkings

Thus, the only conclusion we may consider is that gates generate some EM perturbations, i.e., generate some data dependentharmonics Popularity-based congestion control in named data networkinglocated somewhere in the whole EM emission spectrum. Within this context, the proposed technique allows disclosing the data dependent behavior of EM emissions in the frequencydomain without making any assumption on the EM emission characteristics. It is based on spectral incoherence analysis of two time domain signals as detailed below. The only observation on which is based the method is: considering two successive hardware operations, we are sure that some gates switch during one computation and do not switch during the other, while some gates switch during both operations.

This leads to the following intuitive conclusion that guides the development of our proposal: Popularity-based congestion control in named data networking between two cryptographic operations some characteristics of the EM emissions remain constant coheren from one operation to another, while some characteristics completely change are incoherent Such a data dependent behavior is disclosed by the WGMSI technique. The magnitude squared coherence MSC between two signals is a real-valued function of frequency with values It is defined by where are the power spectral density of is the cross power spectral density of At a given frequency value of 1 indicates that the two spectra are exactly the same while, a value of  means that the spectra are different, i.e., incoherent. Alternatively, one may compute the Magnitude Squared Incoherence This criterion has also its values between  and  but indicates rigorously the contrary of Considering the whole spectra of two time domain signals, one may compute the WGMSI coefficients between them according to