Spatiotemporal patterns and predictability of cyberattacks

A relatively unexplored issue in cybersecurity science and engineering is whether there exist intrinsic patterns of cyberattacks. Conventional wisdom favors absence of such patterns due to the overwhelming complexity of the modern cyberspace. Surprisingly, through a detailed analysis of an extensive data set that records the time-dependent frequencies of attacks over a relatively wide range of consecutive IP addresses, we successfully uncover intrinsic spatiotemporal patterns underlying cyberattacks, where the term "spatio" refers to the IP address space. In particular, we focus on analyzing {\em macroscopic} properties of the attack traffic flows and identify two main patterns with distinct spatiotemporal characteristics: deterministic and stochastic. Strikingly, there are very few sets of major attackers committing almost all the attacks, since their attack "fingerprints" and target selection scheme can be unequivocally identified according to the very limited number of unique spatiotemporal characteristics, each of which only exists on a consecutive IP region and differs significantly from the others. We utilize a number of quantitative measures, including the flux-fluctuation law, the Markov state transition probability matrix, and predictability measures, to characterize the attack patterns in a comprehensive manner. A general finding is that the attack patterns possess high degrees of predictability, potentially paving the way to anticipating and, consequently, mitigating or even preventing large-scale cyberattacks using macroscopic approaches

Study on a metal-insulator-silicon hydrogen sensor with LaTiON as gate insulator

In this paper, by using a metal-insulator- semiconductor Schottky-diode structure, we examined the electrical and hydrogen-sensing properties of radio frequency sputtered LaTiON thin films that had been annealed at four different temperatures (450 °C, 550 °C, 650 °C, and 750 °C). Characterization of their morphological surface indicates that their average surface roughness decreases from 0.108 to 0.090 nm with increasing annealing temperature. X-ray diffraction shows the growths of La and Ti are in the 1 0 0 direction, i.e., in parallel to the Si substrate. Analysis of measured electrical characteristics indicates that thermionic emission is the dominant mechanism at low temperatures (from RT to 150 °C), while Poole-Frenkel emission plays an important role at high temperatures (above 150 °C) in the electrical conduction. Results suggest that the sample annealed at 650 °C has the most promising hydrogen-sensing performance (better current-voltage characteristics, higher sensitivity of 2.0 at 100 °C) among the four samples. © 2001-2012 IEEE.published_or_final_versio

A study on hydrogen adsorption of Metal-Insulator-Silicon sensor with La2O3 as gate insulator

A new Metal-Insulator-Silicon (MIS) Schottky-diode hydrogen sensor with La203 as gate insulator was fabricated. Its hydrogen-sensing properties were studied from room temperature (RT) to 200°C. Results showed that the device had excellent hydrogen-sensing performance below about 250°C. Furthermore, hydrogen reaction kinetics was confirmed for the sample. The response time extracted from its hydrogen adsorption transient behavior was around 4.5 s at 150°C, while a hydrogen adsorption activation energy of 10.9 kcal/mol was obtained for the sensor.published_or_final_versionThe 2010 IEEE International Conference of Electron Devices and Solid-State Circuits (EDSSC 2010), Hong Kong, China, 15-17 December 2010. In IEEE EDSSC Proceedings, 2010, p. 1-

Neural plasticity in common forms of chronic headaches

Headaches are universal experiences and among the most common disorders. While headache may be physiological in the acute setting, it can become a pathological and persistent condition.The mechanisms underlying the transition from episodic to chronic pain have been the subject of intense study. Using physiological and imaging methods, researchers have identified a number of different forms of neural plasticity associated with migraine and other headaches, including peripheral and central sensitization, and alterations in the endogenous mechanisms of pain modulation. While these changes have been proposed to contribute to headache and pain chronification, some findings are likely the results of repetitive noxious stimulation, such as atrophy of brain areas involved in pain perception and modulation. In this review, we provide a narrative overview of recent advances on the neuroimaging, electrophysiological and genetic aspects of neural plasticity associated with the most common forms of chronic headaches, including migraine, cluster headache, tension-type headache, and medication overuse headache

Hydrogen sensing properties of Pt/lanthanum oxide-molybdenum oxide nanoplatelet/SiC based Schottky diode

An investigation of the electrical and hydrogen sensing properties of a novel Schottky diode based on a nanostructured lanthanum oxide-molybdenum oxide compound is presented herein. Molybdenum oxide (MoO3) nanoplatelets were grown on SiC substrates via thermal evaporation which was then subsequently coated with lanthanum oxide (La2O3) by RF sputtering. The current-voltage characteristics and hydrogen sensing performance (change in barrier height and sensitivity as well as the dynamic response) were examined from 25 to 300°C. At 180°C, a voltage shift of 2.23V was measured from the sensor while exposed to 1% hydrogen gas under a 100 μA constant reverse bias current. The results indicate that the presence of a La2O3 thin layer substantially improves the hydrogen sensitivity of the MoO3 nanoplatelets

A study on metal-insulator-silicon hydrogen sensor with La2O3 as gate insulator

A new MIS Schottky-diode hydrogen sensor with La2O3 as gate insulator was fabricated. Its hydrogen-sensing properties were studied from room temperature (RT) to 300oC. Results showed that the device had excellent hydrogen-sensing performance below about 250oC.published_or_final_versio

Improving Noisy Student Training on Non-target Domain Data for Automatic Speech Recognition

Noisy Student Training (NST) has recently demonstrated extremely strong performance in Automatic Speech Recognition (ASR). In this paper, we propose a data selection strategy named LM Filter to improve the performances of NST on non-target domain data in ASR tasks. Hypothesis with and without Language Model are generated and CER differences between them are utilized as a filter threshold. Results reveal that significant improvements of 10.4% compared with no data filtering baselines. We can achieve 3.31% CER in AISHELL-1 test set, which is best result from our knowledge without any other supervised data. We also perform evaluations on supervised 1000 hour AISHELL-2 dataset and competitive results of 4.72% CER can be achieved