Physical layer authentication using ensemble learning technique in wireless communications

Cyber-physical wireless systems have surfaced as an important data communication and networking research area. It is an emerging discipline that allows effective monitoring and efficient real-time communication between the cyber and physical worlds by embedding computer software and integrating communication and networking technologies. Due to their high reliability, sensitivity and connectivity, their security requirements are more comparable to the Internet as they are prone to various security threats such as eavesdropping, spoofing, botnets, man-in-the-middle attack, denial of service (DoS) and distributed denial of service (DDoS) and impersonation. Existing methods use physical layer authentication (PLA), the most promising solution to detect cyber-attacks. Still, the cyber-physical systems (CPS) have relatively large computational requirements and require more communication resources, thus making it impossible to achieve a low latency target. These methods perform well but only in stationary scenarios. We have extracted the relevant features from the channel matrices using discrete wavelet transformation to improve the computational time required for data processing by considering mobile scenarios. The features are fed to ensemble learning algorithms, such as AdaBoost, LogitBoost and Gentle Boost, to classify data. The authentication of the received signal is considered a binary classification problem. The transmitted data is labeled as legitimate information, and spoofing data is illegitimate information. Therefore, this paper proposes a threshold-free PLA approach that uses machine learning algorithms to protect critical data from spoofing attacks. It detects the malicious data packets in stationary scenarios and detects them with high accuracy when receivers are mobile. The proposed model achieves better performance than the existing approaches in terms of accuracy and computational time by decreasing the processing time

Theory of a spherical quantum rotors model: low--temperature regime and finite-size scaling

The quantum rotors model can be regarded as an effective model for the low-temperature behavior of the quantum Heisenberg antiferromagnets. Here, we consider a $d$-dimensional model in the spherical approximation confined to a general geometry of the form $L^{d-d'}\times\infty^{d'}\times L_{\tau}^{z}$ ( $L$-linear space size and $L_{\tau}$-temporal size) and subjected to periodic boundary conditions. Due to the remarkable opportunity it offers for rigorous study of finite-size effects at arbitrary dimensionality this model may play the same role in quantum critical phenomena as the popular Berlin-Kac spherical model in classical critical phenomena. Close to the zero-temperature quantum critical point, the ideas of finite-size scaling are utilized to the fullest extent for studying the critical behavior of the model. For different dimensions $1<d<3$ and $0\leq d'\leq d$ a detailed analysis, in terms of the special functions of classical mathematics, for the susceptibility and the equation of state is given. Particular attention is paid to the two-dimensional case.Comment: 33pages, revtex+epsf, 3ps figures included submitted to PR

Electrochemical Oxidation of Cysteine at a Film Gold Modified Carbon Fiber Microelectrode Its Application in a Flow—Through Voltammetric Sensor

A flow-electrolytical cell containing a strand of micro Au modified carbon fiber electrodes (CFE) has been designedand characterized for use in a voltammatric detector for detecting cysteine using high-performance liquid chromatography. Cysteine is more efficiently electrochemical oxidized on a Au /CFE than a bare gold and carbon fiber electrode. The possible reaction mechanism of the oxidation process is described from the relations to scan rate, peak potentials and currents. For the pulse mode, and measurements with suitable experimental parameters, a linear concentration from 0.5 to 5.0 mg·L−1 was found. The limit of quantification for cysteine was below 60 ng·mL−1

Conditional gene deletion with DiCre demonstrates an essential role for CRK3 in Leishmania mexicana cell cycle regulation

Leishmania mexicana has a large family of cyclin-dependent kinases (CDKs) that reflect the complex interplay between cell cycle and life cycle progression. Evidence from previous studies indicated that Cdc2 related kinase 3 (CRK3) in complex with the cyclin CYC6 is a functional homologue of the major cell cycle regulator CDK1, yet definitive genetic evidence for an essential role in parasite proliferation is lacking. To address this, we have implemented an inducible gene deletion system based on a dimerised Cre recombinase (diCre) to target CRK3 and elucidate its role in the cell cycle of L. mexicana. Induction of diCre activity in promastigotes with rapamycin resulted in efficient deletion of floxed CRK3, resulting in G2/M growth arrest. Co-expression of a CRK3 transgene during rapamycin-induced deletion of CRK3 resulted in complementation of growth, whereas expression of an active site CRK3T178E mutant did not, showing that protein kinase activity is crucial for CRK3 function. Inducible deletion of CRK3 in stationary phase promastigotes resulted in attenuated growth in mice, thereby confirming CRK3 as a useful therapeutic target and diCre as a valuable new tool for analysing essential genes in Leishmania

Comparison of artificial neural network and logistic regression models for prediction of mortality in head trauma based on initial clinical data

BACKGROUND: In recent years, outcome prediction models using artificial neural network and multivariable logistic regression analysis have been developed in many areas of health care research. Both these methods have advantages and disadvantages. In this study we have compared the performance of artificial neural network and multivariable logistic regression models, in prediction of outcomes in head trauma and studied the reproducibility of the findings. METHODS: 1000 Logistic regression and ANN models based on initial clinical data related to the GCS, tracheal intubation status, age, systolic blood pressure, respiratory rate, pulse rate, injury severity score and the outcome of 1271 mainly head injured patients were compared in this study. For each of one thousand pairs of ANN and logistic models, the area under the receiver operating characteristic (ROC) curves, Hosmer-Lemeshow (HL) statistics and accuracy rate were calculated and compared using paired T-tests. RESULTS: ANN significantly outperformed logistic models in both fields of discrimination and calibration but under performed in accuracy. In 77.8% of cases the area under the ROC curves and in 56.4% of cases the HL statistics for the neural network model were superior to that for the logistic model. In 68% of cases the accuracy of the logistic model was superior to the neural network model. CONCLUSIONS: ANN significantly outperformed the logistic models in both fields of discrimination and calibration but lagged behind in accuracy. This study clearly showed that any single comparison between these two models might not reliably represent the true end results. External validation of the designed models, using larger databases with different rates of outcomes is necessary to get an accurate measure of performance outside the development population

Constraints on the χ_(c1) versus χ_(c2) polarizations in proton-proton collisions at √s = 8 TeV

The polarizations of promptly produced χ_(c1) and χ_(c2) mesons are studied using data collected by the CMS experiment at the LHC, in proton-proton collisions at √s=8  TeV. The χ_c states are reconstructed via their radiative decays χ_c → J/ψγ, with the photons being measured through conversions to e⁺e⁻, which allows the two states to be well resolved. The polarizations are measured in the helicity frame, through the analysis of the χ_(c2) to χ_(c1) yield ratio as a function of the polar or azimuthal angle of the positive muon emitted in the J/ψ → μ⁺μ⁻ decay, in three bins of J/ψ transverse momentum. While no differences are seen between the two states in terms of azimuthal decay angle distributions, they are observed to have significantly different polar anisotropies. The measurement favors a scenario where at least one of the two states is strongly polarized along the helicity quantization axis, in agreement with nonrelativistic quantum chromodynamics predictions. This is the first measurement of significantly polarized quarkonia produced at high transverse momentum