Analysis of Cloud Based Keystroke Dynamics for Behavioral Biometrics Using Multiclass Machine Learning

With the rapid proliferation of interconnected devices and the exponential growth of data stored in the cloud, the potential attack surface for cybercriminals expands significantly. Behavioral biometrics provide an additional layer of security by enabling continuous authentication and real-time monitoring. Its continuous and dynamic nature offers enhanced security, as it analyzes an individual's unique behavioral patterns in real-time. In this study, we utilized a dataset consisting of 90 users' attempts to type the 11-character string 'Exponential' eight times. Each attempt was recorded in the cloud with timestamps for key press and release events, aligned with the initial key press. The objective was to explore the potential of keystroke dynamics for user authentication. Various features were extracted from the dataset, categorized into tiers. Tier-0 features included key-press time and key-release time, while Tier-1 derived features encompassed durations, latencies, and digraphs. Additionally, Tier-2 statistical measures such as maximum, minimum, and mean values were calculated. The performance of three popular multiclass machine learning models, namely Decision Tree, Multi-layer Perceptron, and LightGBM, was evaluated using these features. The results indicated that incorporating Tier-1 and Tier-2 features significantly improved the models' performance compared to relying solely on Tier-0 features. The inclusion of Tier-1 and Tier-2 features allows the models to capture more nuanced patterns and relationships in the keystroke data. While Decision Trees provide a baseline, Multi-layer Perceptron and LightGBM outperform them by effectively capturing complex relationships. Particularly, LightGBM excels in leveraging information from all features, resulting in the highest level of explanatory power and prediction accuracy. This highlights the importance of capturing both local and higher-level patterns in keystroke data to accurately authenticate users

Neural reactivations during sleep determine network credit assignment.

A fundamental goal of motor learning is to establish the neural patterns that produce a desired behavioral outcome. It remains unclear how and when the nervous system solves this 'credit assignment' problem. Using neuroprosthetic learning, in which we could control the causal relationship between neurons and behavior, we found that sleep-dependent processing was required for credit assignment and the establishment of task-related functional connectivity reflecting the casual neuron-behavior relationship. Notably, we observed a strong link between the microstructure of sleep reactivations and credit assignment, with downscaling of non-causal activity. Decoupling of spiking to slow oscillations using optogenetic methods eliminated rescaling. Thus, our results suggest that coordinated firing during sleep is essential for establishing sparse activation patterns that reflect the causal neuron-behavior relationship

Elastic behavior of the germanium nanowire membrane

Semiconductor nanowires promise to provide the building blocks for a new generation of nanoscale devices. Recently, researchers have worked on developing new membranes out of semiconductor nanowires. Due to its small dimensions and porous network structure, obtaining the mechanical properties of these membranes is very challenging. This work presents a bulge test method for determining the mechanical properties of the porous germanium nanowire membrane. Theoretical bulge equations for circular and rectangular shaped samples were derived. A parametric analysis was conducted to determine the optimum configuration and guide the selection of components for the apparatus. A laminate comprising the germanium nanowire membrane and a polymer film was fabricated due to the porosity of the nanowire membrane. The bulge test apparatus was designed and developed to test circular and rectangular shaped composite samples that are required to extract Young’s modulus and Poisson’s ratio. The Young’s modulus and the Poisson’s ratio of the germanium nanowire membrane were found to be 208 MPa and 0.10, respectivelyAerospace Engineerin

A 6-year Cross-Sectional study on Leishmaniasis

Leishmaniasis is caused by a protozoan parasite which is transmitted by the bite of infected female phlebotomine sand-fly. Leishmaniasis is more common in poor socioeconomic conditions like poor housing, overcrowding and malnutrition. Asymptomatic Leishmania donovani infections outnumber clinical presentations, however, the predictors for development of active disease are not well known. Asymptomatic persons infected with the parasites causing visceral leishmaniasis (VL) usually outnumber clinically apparent cases by a ratio of 4-10 to 1. Accurate treatment and cure is mainly dependent on the factors like clinical manifestations and diagnosing the causative species among cutaneous Leishmaniasis or mucocutaneous Leishmaniasis and visceral Leishmaniasi

High performance structural laminate composite material for use to 1000.degree. F. and above, apparatus for and method of manufacturing same, and articles made with same

A novel materials technology has been developed and demonstrated for providing a high modulus composite material for use to 1000.degree. F. and above. This material can be produced at 5-20% of the cost of refractory materials, and has higher structural properties. This technology successfully resolves the problem of thermal shock or ply lift, which limits traditional high temperature laminates (such as graphite/polyimide and graphite/phenolic) to temperatures of 550-650.degree. F. in thicker (0.25 and above) laminates. The technology disclosed herein is an enabling technology for the nose for the External Tank (ET) of the Space Shuttle, and has been shown to be capable of withstanding the severe environments encountered by the nose cone through wind tunnel testing, high temperature subcomponent testing, and full scale structural, dynamic, acoustic, and damage tolerance testing

Thickness Dependent Interlayer Magnetoresistance in Multilayer Graphene Stacks

Chemical Vapor Deposition grown multilayer graphene (MLG) exhibits large out-of-plane magnetoresistance due to interlayer magnetoresistance (ILMR) effect. It is essential to identify the factors that influence this effect in order to explore its potential in magnetic sensing and data storage applications. It has been demonstrated before that the ILMR effect is sensitive to the interlayer coupling and the orientation of the magnetic field with respect to the out-of-plane (c-axis) direction. In this work, we investigate the role of MLG thickness on ILMR effect. Our results show that the magnitude of ILMR effect increases with the number of graphene layers in the MLG stack. Surprisingly, thicker devices exhibit field induced resistance switching by a factor of at least ~107. This effect persists even at room temperature and to our knowledge such large magnetoresistance values have not been reported before in the literature at comparable fields and temperatures. In addition, an oscillatory MR effect is observed at higher field values. A physical explanation of this effect is presented, which is consistent with our experimental scenario

Charge Crowding in Graphene-Silicon Diodes

The performance of nanoscale electronic devices based on a two-three dimensional (2D-3D) interface is significantly affected by the electrical contacts that interconnect these materials with external circuitry. This work investigates charge transport effects at the 2D-3D ohmic contact coupled with the thermionic injection model for graphene/Si Schottky junction. Here, w e focus on the intrinsic properties of graphene-metal contacts, paying particular attention to the nature of the contact failure mechanism under high electrical stress. According to our findings, severe current crowding (CC) effects in highly conductive electrical contact significantly affect device failure that can be reduced by spatially varying the contact properties and geometry. The impact of electrical breakdown on material degradation is systematically analyzed by atomic force, Raman, scanning electron, and energy dispersive X-ray spectroscopies. Our devices withstand high electrostatic discharge spikes over a longer period, manifesting high robustness and operational stability. This research paves the way towards a highly robust and reliable graphene/Si heterostructure in futuristic on-chip integration in dynamic switching. The methods we employed here can be extended for other nanoscale electronic devices based on 2D-3D interface