Effect of sampling effort and sampling frequency on the composition of the planktonic crustacean assemblage: a case study of the river Danube

Although numerous studies have focused on the seasonal dynamics of riverine zooplankton, little is known about its short-term variation. In order to examine the effects of sampling frequency and sampling effort, microcrustacean samples were collected at daily intervals between 13 June and 21 July of 2007 in a parapotamal side arm of the river Danube, Hungary. Samples were also taken at biweekly intervals from November 2006 to May 2008. After presenting the community dynamics, the effect of sampling effort was evaluated with two different methods; the minimal sample size was also estimated. We introduced a single index (potential dynamic information loss; to determine the potential loss of information when sampling frequency is reduced. The formula was calculated for the total abundance, densities of the dominant taxa, adult/larva ratios of copepods and for two different diversity measures. Results suggest that abundances may experience notable fluctuations even within 1 week, as do diversities and adult/larva ratios

An Analysis of Deep Learning Based Profiled Side-channel Attacks: Custom Deep Learning Layer, CNN Hyperparameters for Countermeasures, and Portability Settings

A side-channel attack (SCA) recovers secret data from a device by exploiting unintended physical leakages such as power consumption. In a profiled SCA, we assume an adversary has control over a target and copy device. Using the copy device the adversary learns a profile of the device. With the profile, the adversary exploits the measurements from a target device and recovers the secret key. As SCAs have shown to be a realistic attack vector, countermeasures have been invented to harden these kinds of attacks. In the last few years, deep learning has been applied in a wide variety of domains. For example, convolutional neural networks have shown to be effective for object recognition in images and recurrent neural networks for text generation. In the side-channel analysis domain, deep learning has shown to be successful. Up until recently, no deep learning layer existed that was specifically designed for SCAs. In this work, we analyze this layer, called the spread layer, and demonstrate the flaws of this layer. We improve the flaws and show the spread layer does not enhance the performance of SCAs. Additionally, we show there is no need to develop a deep learning layer specifically for SCAs on unprotected implementations. For implementations where countermeasures are present, literature demonstrated that convolutional neural networks are the most successful. However, for both the masking and random delay countermeasure, little is known about the influence of the kernel size and depth of the network. In this work, we illustrate that increasing the kernel size and depth of the network both increase the attack efficiency for the random delay countermeasure. For the masking countermeasure, we demonstrate that higher kernel sizes and shallow networks perform the best. Additionally, in this work, we consider a portability setting where the probe position has been changed in between the measurements of the profiling and attack measurements. Here, we show that the probe position causes a typical deep learning SCA to be ineffective. We introduce a normalization method such that the attack becomes effective, and show this method enables the attack to perform as expected.Computer Scienc