The proliferation of low-cost IEEE 802.15.4 ZigBee wireless devices in critical infrastructure applications presents security challenges. Network security commonly relies on bit-level credentials that are easily replicated and exploited by hackers. Unauthorized access can be mitigated by physical layer (PHY) security measures that exploit device-dependent emission characteristics that are sufficiently unique to discriminate devices. RF distinct native attribute (RF-DNA) fingerprinting is a PHY-based security measure, which computes statistical features extracted from such device emissions. However, the RF-DNA fingerprints can be numerous, correlated, and noisy, therefore, a dimensional reduction analysis (DRA) via feature selection is, therefore, of interest. Device classification with DRA feature subsets is evaluated using a multiple discriminant analysis (MDA) classifier. Determining feature relevance from MDA was generally dismissed in prior RF fingerprinting work and is seldom considered in other applications. Here, the MDA feature relevance is revisited using a proposed eigen-based MDA loadings fusion (MLF) methodology. The MDA classification models are adopted and used to assess device identification (ID) classification and verification performance for both the authorized and unauthorized (rogue) devices using a claimed versus actual biometric methodology. Performance is compared for six DRA methods using: 1) a two-sample Kolmogorov-Smirnov test; 2) one-way analysis of variance F-test statistics; 3) a Wilk's lambda ratio; 4) generalized relevance learning vector quantized-improved relevance; 5) randomly selected; and 6) the proposed MLF method. Quantitative and qualitative dimensionality assessment methods are compared and contrasted to establish upper bounds on the number of retained features. Experimentally collected ZigBee emissions are considered and ZigBee device classification and ID verification performance using DRA subsets are compared with a full-dimensional feature set. Results show that DRA via the proposed MLF method is superior and more robust than competing methods.

In this letter, a reliable, simple, and intuitive approach for hyperspectral imagery (HSI) anomaly detection (AD) is presented. This method, namely, the global iterative principal component analysis (PCA) reconstruction-error-based anomaly detector (GIPREBAD), examines AD by computing errors (residuals) associated with reconstructing the original image using PCA projections. PCA is a linear transformation and feature extraction process commonly used in HSI and frequently appears in operation prior to any AD task. PCA features represent a projection of the original data into lower-dimensional subspace. An iterative approach is used to mitigate outlier influence on background covariance estimates. GIPREBAD results are provided using receiver-operating-characteristic curves for HSI from the hyperspectral digital imagery collection experiment. Results are compared against the Reed-Xiaoli (RX) algorithm, the linear RX (LRX) algorithm, and the support vector data description (SVDD) algorithm. The results show that the proposed GIPREBAD method performs favorably compared with RX, LRX, and SVDD and is both intuitively and computationally simpler than either RX or SVDD.

Spiking neural networks (SNNs) have become an interesting alternative to conventional artificial neural networks (ANN) thanks to their temporal processing capabilities and energy efficient implementations in neuromorphic hardware. However, the challenges involved in training SNNs have limited their performance in terms of accuracy and thus their applications. Improving learning algorithms and neural architectures for a more accurate feature extraction is therefore one of the current priorities in SNN research. In this paper we present a study on the key components of modern spiking architectures. We design a spiking version of the successful residual network architecture and provide an in-depth study on the possible implementations of spiking residual connections. This study shows how, depending on the use case, the optimal residual connection implementation may vary. Additionally, we empirically compare different techniques in image classification datasets taken from the best performing networks. Our results provide a state of the art guide to SNN design, which allows to make informed choices when trying to build the optimal visual feature extractor. Finally, our network outperforms previous SNN architectures in CIFAR-10 (94.14%) and CIFAR-100 (74.65%) datasets and matches the state of the art in DVS-CIFAR10 (72.98%), with less parameters than the previous state of the art and without the need for ANN–SNN conversion. Code available at: https://github.com/VicenteAlex/Spiking_ResNet.

Spiking neural networks (SNNs) are largely inspired by biology and neuroscience and leverage ideas and theories to create fast and efficient learning systems. Spiking neuron models are adopted as core processing units in neuromorphic systems because they enable event-based processing. Among many neuron models, the integrate-and-fire (I&F) models are often adopted, with the simple leaky I&F (LIF) being the most used. The reason for adopting such models is their efficiency and/or biological plausibility. Nevertheless, rigorous justification for adopting LIF over other neuron models for use in artificial learning systems has not yet been studied. This work considers various neuron models in the literature and then selects computational neuron models that are single-variable, efficient, and display different types of complexities. From this selection, we make a comparative study of three simple I&F neuron models, namely the LIF, the quadratic I&F (QIF) and the exponential I&F (EIF), to understand whether the use of more complex models increases the performance of the system and whether the choice of a neuron model can be directed by the task to be completed. Neuron models are tested within an SNN trained with spike-timing dependent plasticity (STDP) on a classification task on the N-MNIST and DVS gestures datasets. Experimental results reveal that more complex neurons manifest the same ability as simpler ones to achieve high levels of accuracy on a simple dataset (N-MNIST), albeit requiring comparably more hyper-parameter tuning. However, when the data possess richer spatio-temporal features, the QIF and EIF neuron models steadily achieve better results. This suggests that accurately selecting the model based on the richness of the feature spectrum of the data could improve the whole system’s performance. Finally, the code implementing the spiking neurons in the SpykeTorch framework is made publicly available.

“Big Data” is an emerging term used with business, engineering, and other domains. Although Big Data is a popular term used today, it is not a new concept. However, the means in which data can be collected is more readily available than ever, which makes Big Data more relevant than ever because it can be used to improve decisions and insights within the domains it is used. The term Big Data can be loosely defined as data that is too large for traditional analysis methods and techniques. In this article, varieties of prominent but loose definitions for Big Data are shared. In addition, a comprehensive overview of issues related to Big Data is summarized. For example, this paper examines the forms, locations, methods of analyzing and exploiting Big Data, and current research on Big Data. Big Data also concerns a myriad of tangential issues, from privacy to analysis methods that will also be overviewed. Best practices will further be considered. Additionally, the epistemology of Big Data and its history will be examined, as well as technical and societal problems existing with Big Data.