LDA-Based Clustering as a Side-Channel Distinguisher

Side-channel attacks put the security of the implementations of cryptographic algorithms under threat. Secret information can be recovered by analyzing the physical measurements acquired during the computations and using key recovery distinguishing functions to guess the best candidate. Several generic and model based distinguishers have been proposed in the literature. In this work we describe two contributions that lead to better performance of side-channel attacks in challenging scenarios. First, we describe how to transform the physical leakage traces into a new space where the noise reduction is near-optimal. Second, we propose a new generic distinguisher that is based upon minimal assumptions. It approaches a key distinguishing task as a problem of classification and ranks the key candidates according to the separation among the leakage traces. We also provide experiments and compare their results to those of the Correlation Power Analysis (CPA). Our results show that the proposed method can indeed reach better success rates even in the presence of significant amount of noise

ALGORITHM AND SOFTWARE DEVELOPMENT OF ATMOSPHERIC MOTION VECTOR (AMV) PRODUCTS FOR THE FUTURE GOES-R ADVANCED BASELINE IMAGER (ABI)

Atmospheric motion vectors (AMVs), derived from the current GOES series of satellites, provide invaluable tropospheric wind information to the meteorological community. AMVs obtained from tracking features (i.e., clouds and moisture gradients) are used for: i) Improving numerical weather prediction (NWP) analyses and forecasts; ii) Supporting short term forecasting activities at National Weather Service (NWS) field offices; and iii) Generating tropical and mesoscale wind analyses. The Geostationary Operational Environmental Satellites (GOES)-R Algorithm Working Group (AWG) Winds application team is working on development of algorithms and software for the generation of Atmospheric Motion Vectors (AMVs) from the GOES-R Advanced Baseline Imager (ABI) to be flown on the next generation of GOES satellites. The GOES-R series of satellites offers exciting new capabilities that are expected to directly benefit and improve the derivation and quality of the AMVs. These new capabilities include: continuous scanning with no loss of imagery due to eclipse or conflicting scanning schedules, higher resolution (spatial and temporal) imagery, and improved navigation. Improved cloud-top height assignments derived from the GOES-R ABI are expected to contribute to further improvement and utilization of the AMV products