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This paper presents a systematic two-layer approach for detecting DNS over HTTPS (DoH) traffic and distinguishing Benign-DoH traffic from Malicious-DoH traffic using six machine learning algorithms. The capability of machine learning classifiers is evaluated considering their accuracy, precision, recall, and F-score, confusion matrices, ROC curves, and feature importance. The results show that LGBM and XGBoost algorithms outperform the other algorithms in almost all the classification metrics reaching the maximum accuracy of 100% in the classification tasks of layers 1 and 2.LGBM algorithms only misclassified one DoH traffic test as non-DoH out of 4000 test datasets. It has also found that out of 34 features extracted from the CIRA-CIC-DoHBrw-2020 dataset, SourceIP is the critical feature for classifying DoH traffic from non-DoH traffic in layer one followed by DestinationIP feature. However, only DestinationIP is an important feature for LGBM and gradient boosting algorithms when classifying Benign-DoH from Malicious-DoH traffic in layer 2.

In its first 2 years of operation, the ground-based Terrestrial gamma ray flash and Energetic Thunderstorm Rooftop Array (TETRA)-II array of gamma ray detectors has recorded 22 bursts of gamma rays of millisecond-scale duration associated with lightning. In this study, we present the TETRA-II observations detected at the three TETRA-II ground-level sites in Louisiana, Puerto Rico, and Panama together with the simultaneous radio frequency signals from the lightning data sets VAISALA Global Lightning Dataset, VAISALA National Lightning Detection Network, Earth Networks Total Lightning Network, and World Wide Lightning Location Network. The relative timing between the gamma ray events and the lightning activity is a key parameter for understanding the production mechanism(s) of the bursts. The gamma ray time profiles and their correlation with radio sferics suggest that the gamma ray events are initiated by lightning leader activity and are produced near the last stage of lightning leader channel development prior to the lightning return stroke.

In this paper, we have investigated the static metrics and switching attributes of graphene nanoribbon field effect transistors (GNR FETs) for scaling the channel length from 15 nm down to 2.5 nm and GNR width by approaching the ultimate vertical scaling of oxide thickness. We have simulated the double gate GNR FET by solving a numerical quantum transport model based on self-consistent solution of the 3D Poisson equation and 1D Schrödinger equation within the non-equilibrium Green's function formulism. The narrow armchair GNR, e.g. (7,0), improved the device robustness to short channel effects, leading to better OFF-state performance considering OFF-current, ION/IOFF ratio, subthreshold swing and drain-induced barrierlowering. The wider armchair GNRs allow the scaling of channel length and supply voltage resulting better ON-state performance such as the larger intrinsic cut-off frequency for the channel length below 7.5 nm at smaller gate voltage as well as smaller intrinsic gate-delay time with the constant slope for scaling the channel length and supply voltage. The wider armchair GNRs, e.g. (13,0), have smaller power-delay product for scaling the channel length and supply voltage, reaching to ~0.18(fJ/µm).

Abstract:In this paper, we present a physics-based analytical model of GNR FET, which allows for the evaluation of GNR FET performance including the effects of line-edge roughness as its practical specific non-ideality. The line-edge roughness is modeled in edge-enhanced band-to-band-tunneling and localization regimes, and then verified for various roughness amplitudes. Corresponding to these two regimes, the off-current is initially increased, then decreased; while, on the other hand, the on-current is continuously decreased by increasing the roughness amplitude.