A Review and Comparison of AI Enhanced Side Channel Analysis

Side Channel Analysis (SCA) presents a clear threat to privacy and security in modern computing systems. The vast majority of communications are secured through cryptographic algorithms. These algorithms are often provably-secure from a cryptographical perspective, but their implementation on real hardware introduces vulnerabilities. Adversaries can exploit these vulnerabilities to conduct SCA and recover confidential information, such as secret keys or internal states. The threat of SCA has greatly increased as machine learning, and in particular deep learning, enhanced attacks become more common. In this work, we will examine the latest state-of-the-art deep learning techniques for side channel analysis, the theory behind them, and how they are conducted. Our focus will be on profiling attacks using deep learning techniques, but we will also examine some new and emerging methodologies enhanced by deep learning techniques, such as non-profiled attacks, artificial trace generation, and others. Finally, different deep learning enhanced SCA schemes attempted against the ANSSI SCA Database (ASCAD) and their relative performance will be evaluated and compared. This will lead to new research directions to secure cryptographic implementations against the latest SCA attacks.Comment: This paper has been accepted by ACM Journal on Emerging Technologies in Computing Systems (JETC

Vortex Dynamics in Rotating Rayleigh-B\'enard Convection

We investigate the spatial distribution and dynamics of the vortices in rotating Rayleigh-B\'enard convection in a reduced Rayleigh-number range $1.3{\le}Ra/Ra_{c}{\le}166$. Under slow rotations ($Ra{\gtrsim}10Ra_{c}$), the vortices are randomly distributed. The size-distribution of the Voronoi cells of the vortex centers is well described by the standard $\Gamma$ distribution. In this flow regime the vortices exhibit Brownian-type horizontal motion. The probability density functions of the vortex displacements are, however, non-Gaussian at short time scales. At modest rotating rates ($4Ra_{c}{\le}Ra{\lesssim}10Ra_{c}$) the centrifugal force leads to radial vortex motions, i.e., warm cyclones (cold anticyclones) moving towards (outward from) the rotation axis. The mean-square-displacements of the vortices increase faster than linearly at large time. This super-diffusive behavior can be satisfactorily explained by a Langevin model incorporating the centrifugal force. In the rapidly rotating regime ($1.6Ra_{c}{\le}Ra{\le}4Ra_{c}$) the vortices are densely distributed, with the size-distribution of their Voronoi cells differing significantly from the standard $\Gamma$ distribution. The hydrodynamic interaction of neighboring vortices results in formation of vortex clusters. Inside clusters the correlation of the vortex velocity fluctuations is scale free, with the correlation length being approximately $30\%$ of the cluster length. We examine the influence of cluster forming on the dynamics of individual vortex. Within clusters, cyclones exhibit inverse-centrifugal motion as they submit to the motion of strong anticyclones, while the velocity for outward motion of the anticyclones is increased. Our analysis show that the mobility of isolated vortices, scaled by their vorticity strength, is a simple power function of the Froude number

Analyzing Chinese Customers’ Switching Intention of Smartphone Brands: Integrating the Push-Pull-Mooring Framework

With increasing technology advancement, online shopping, and growth of affordable segment, smartphone users’ switching behavior is becoming a concern for smartphone companies. To fill the research gap that persists in relation to the switching behavior of smartphone users from a multidimensional view, this study integrates the push-pull-mooring model to investigate and classify factors that affect the switching behavior of smartphone users. To test the hypotheses in relation to different predictors, data were collected from a survey of 246 users of the top ten smartphone brands in China and analyzed using structural model equation through regression analyses. The results revealed that the pull, push, and mooring factors have a significant impact on the switching behavior of smartphone users. While the pull effects have a stronger impact than push effects, the mooring factors were found to have a significant and strongest effect on smartphone users’ switching behavior. In particular, subjective norm showed the greatest impact on switching behavior, product quality and obsolete features showed significant and weak impact while brand image, switching cost, and poor customer service did not show any significant impact. These findings provide useful implications and insights for smartphone brands to develop competitive strategies for customer relationship management

Toll-like receptor 2 -196 to -174 del polymorphism influences the susceptibility of Han Chinese people to Alzheimer's disease

<p>Abstract</p> <p>Background</p> <p>Toll-like receptor 2 (<it>TLR2</it>) represents a reasonable functional and positional candidate gene for Alzheimer's disease (AD) as it is located under the linkage region of AD on chromosome 4q, and functionally is involved in the microglia-mediated inflammatory response and amyloid-β clearance. The -196 to -174 del polymorphism affects the <it>TLR2 </it>gene and alters its promoter activity.</p> <p>Methods</p> <p>We recruited 800 unrelated Northern Han Chinese individuals comprising 400 late-onset AD (LOAD) patients and 400 healthy controls matched for gender and age. The -196 to -174 del polymorphism in the <it>TLR2 </it>gene was genotyped using the polymerase chain reaction (PCR) method.</p> <p>Results</p> <p>There were significant differences in genotype (P = 0.026) and allele (P = 0.009) frequencies of the -196 to -174 del polymorphism between LOAD patients and controls. The del allele was associated with an increased risk of LOAD (OR = 1.31, 95% CI = 1.07-1.60, Power = 84.9%). When these data were stratified by apolipoprotein E (<it>ApoE</it>) ε4 status, the observed association was confined to <it>ApoE </it>ε4 non-carriers. Logistic regression analysis suggested an association of LOAD with the polymorphism in a recessive model (OR = 1.64, 95% CI = 1.13-2.39, Bonferroni corrected P = 0.03).</p> <p>Conclusions</p> <p>Our data suggest that the -196 to -174 del/del genotype of <it>TLR2 </it>may increase risk of LOAD in a Northern Han Chinese population.</p