Leakage Detection with Kolmogorov-Smirnov Test

Leakage detection seeking the evidence of sensitive data dependencies in the side-channel traces instead of trying to recover the sensitive data directly under the enormous efforts with numerous leakage models and state-of-the-art distinguishers can provide a fast preliminary security assessment on the cryptographic devices for designers and evaluators. Therefore, it is a popular topic in recent side-channel research of which the Welch\u27s $t$-test-based Test Vector Leakage Assessment (TVLA) methodology is the most widely used one. However, the TVLA is not always the best option under all kinds of conditions (as we can see in the latter section of this paper). Kolmogorov-Smirnov test is a well-known nonparametric method for statistical analysis to determine whether the samples are from the same distribution by analyzing the cumulative distribution. It has been proposed into side-channel analysis as a successful distinguisher. This paper proposes---to our knowledge, for the first time---Kolmogorov-Smirnov test as a new method for leakage detection. Besides, we propose two implementations to speed up the KS leakage detection procedure. Experimental results on simulated leakage with various parameters and the practical traces verify that KS is an effective and robust leakage detection tool and the comprehensive comparison with TVLA shows that KS-based leakage detection can be a right-hand supplement to TVLA when performing the side-channel assessment

O(N)-Space Spatiotemporal Filter for Reducing Noise in Neuromorphic Vision Sensors

Neuromorphic vision sensors are an emerging technology inspired by how retina processing images. A neuromorphic vision sensor only reports when a pixel value changes rather than continuously outputting the value every frame as is done in an 'ordinary' Active Pixel Sensor (ASP). This move from a continuously sampled system to an asynchronous event driven one effectively allows for much faster sampling rates; it also fundamentally changes the sensor interface. In particular, these sensors are highly sensitive to noise, as any additional event reduces the bandwidth, and thus effectively lowers the sampling rate. In this work we introduce a novel spatiotemporal filter with O(N)O(N) memory complexity for reducing background activity noise in neuromorphic vision sensors. Our design consumes 10× less memory and has 100× reduction in error compared to previous designs. Our filter is also capable of recovering real events and can pass up to 180 percent more real events

A novel pipeline leak detection technique based on acoustic emission features and two-sample Kolmogorov–Smirnov test.

Pipeline leakage remains a challenge in various industries. Acoustic emission (AE) technology has recently shown great potential for leak diagnosis. Many AE features, such as root mean square (RMS), peak value, standard deviation, mean value, and entropy, have been suggested to detect leaks. However, background noise in AE signals makes these features ineffective. The present paper proposes a pipeline leak detection technique based on acoustic emission event (AEE) features and a Kolmogorov–Smirnov (KS) test. The AEE features, namely, peak amplitude, energy, rise-time, decay time, and counts, are inherent properties of AE signals and therefore more suitable for recognizing leak attributes. Surprisingly, the AEE features have received negligible attention. According to the proposed technique, the AEE features are first extracted from the AE signals. For this purpose, a sliding window was used with an adaptive threshold so that the properties of both burst- and continuous-type emissions can be retained. The AEE features form distribution that change its shape when the pipeline condition changes from normal to leakage. The AEE feature distributions for leak and healthy conditions were discriminated using the two-sample KS test, and a pipeline leak indicator (PLI) was obtained. The experimental results demonstrate that the developed PLI accurately distinguishes the leak and no-leak conditions without any prior leak information and it performs better than the traditional features such as mean, variance, RMS, and kurtosis

Early outcome and blood-brain barrier integrity after co-administered thrombolysis and hyperbaric oxygenation in experimental stroke

Background After promising results in experimental stroke, normobaric (NBO) or hyperbaric oxygenation (HBO) have recently been discussed as co-medication with tissue plasminogen activator (tPA) for improving outcome. This study assessed the interactions of hyperoxia and tPA, focusing on survival, early functional outcome and blood-brain barrier (BBB) integrity following experimental stroke. Methods Rats (n=109) underwent embolic middle cerebral artery occlusion or sham surgery. Animals were assigned to: Control, NBO (60-minute pure oxygen), HBO (60-minute pure oxygen at 2.4 absolute atmospheres), tPA, or HBO+tPA. Functional impairment was assessed at 4 and 24 hours using Menzies score, followed by intravenous application of FITC-albumin as a BBB permeability marker, which was allowed to circulate for 1 hour. Further, blood sampling was performed at 5 and 25 hours for MMP-2, MMP-9, TIMP-1 and TIMP-2 concentration. Results Mortality rates did not differ significantly between groups, whereas functional improvement was found for NBO, tPA and HBO+tPA. NBO and HBO tended to stabilize BBB and to reduce MMP-2. tPA tended to increase BBB permeability with corresponding MMP and TIMP elevation. Co-administered HBO failed to attenuate these early deleterious effects, independent of functional improvement. Conclusions The long-term consequences of simultaneously applied tPA and both NBO and HBO need to be addressed by further studies to identify therapeutic potencies in acute stroke, and to avoid unfavorable courses following combined treatment