Insider Attack Detection Using Deep Belief Neural Network in Cloud Computing

Cloud computing is a high network infrastructure where users, owners, third users, authorized users, and customers can access and store their information quickly. The use of cloud computing has realized the rapid increase of information in every field and the need for a centralized location for processing efficiently. This cloud is nowadays highly affected by internal threats of the user. Sensitive applications such as banking, hospital, and business are more likely affected by real user threats. An intruder is presented as a user and set as a member of the network. After becoming an insider in the network, they will try to attack or steal sensitive data during information sharing or conversation. The major issue in today's technological development is identifying the insider threat in the cloud network. When data are lost, compromising cloud users is difficult. Privacy and security are not ensured, and then, the usage of the cloud is not trusted. Several solutions are available for the external security of the cloud network. However, insider or internal threats need to be addressed. In this research work, we focus on a solution for identifying an insider attack using the artificial intelligence technique. An insider attack is possible by using nodes of weak users’ systems. They will log in using a weak user id, connect to a network, and pretend to be a trusted node. Then, they can easily attack and hack information as an insider, and identifying them is very difficult. These types of attacks need intelligent solutions. A machine learning approach is widely used for security issues. To date, the existing lags can classify the attackers accurately. This information hijacking process is very absurd, which motivates young researchers to provide a solution for internal threats. In our proposed work, we track the attackers using a user interaction behavior pattern and deep learning technique. The usage of mouse movements and clicks and keystrokes of the real user is stored in a database. The deep belief neural network is designed using a restricted Boltzmann machine (RBM) so that the layer of RBM communicates with the previous and subsequent layers. The result is evaluated using a Cooja simulator based on the cloud environment. The accuracy and F-measure are highly improved compared with when using the existing long short-term memory and support vector machine.     

Transesterification of β-Keto Esters Catalysed by Basic Porous Material

-keto esters have been successfully transesterified with primary, secondary, tertiary, allylic and alkynic alcohols in good yields using TBD anchored on MCM support for the first time. The hybrid solid base catalyst can be recycled several times with consistent activity.     

Magnetoresistance studies on barium doped nanocrystalline manganite

An energetically attractive, simple, fast and a novel low temperature (300 °C) solution combustion route for the synthesis of crystalline and homogeneous nanoparticles of lanthanum barium manganese oxide La_0.9Ba_0.1MnO_3+δ (LBMO) is reported. Formation and homogeneity of the solid solutions have been confirmed by powder X-ray diffraction (PXRD) and energy dispersive X-ray analysis (EDS) respectively. The Rietveld analysis shows both as-formed as well as calcined samples are in cubic phase with space group pm3m. The microstructure and agglomerated particle size of the compounds are examined by scanning electron microscope. Infrared spectroscopy revealed that both MnOMn bending mode and MnO stretching mode are influenced by calcination temperature. The magnetoresistance measurement on sintered LBMO pellet exhibits a broad metal–insulator transition (T_M-I) at around 228 K. At 1 T applied magnetic field, LBMO shows magnetoresistance (MR) of 10%, whereas for 4 and 7 T, the negative magnetoresistance values are in the range 51 and 59% respectively at T_M-I. The experimental resistivity data of the present investigation are fitted to a simple empirical equation in order to understand conduction mechanism in this compound.     

Modeling the viral load dependence of residence times of virus-laden droplets from COVID-19-infected subjects in indoor environments

In the ongoing COVID-19 pandemic situation, exposure assessment and control strategies for aerosol transmission path are feebly understood. A recent study pointed out that Poissonian fluctuations in viral loading of airborne droplets significantly modifies the size spectrum of the virus-laden droplets (termed as “virusol”) (Anand and Mayya, 2020). Herein we develop the theory of residence time of the virusols, as contrasted with complete droplet system in indoor air using a comprehensive “Falling-to-Mixing-Plate-out” model that considers all the important processes namely, indoor dispersion of the emitted puff, droplet evaporation, gravitational settling, and plate out mechanisms at indoor surfaces. This model fills the existing gap between Wells falling drop model (Wells, 1934) and the stirred chamber models (Lai and Nazarofff, 2000). The analytical solutions are obtained for both 1-D and 3-D problems for non-evaporating falling droplets, used mainly for benchmarking the numerical formulation. The effect of various parameters is examined in detail. Significantly, the mean residence time of virusols is found to increase nonlinearly with the viral load in the ejecta, ranging from about 100 to 150 s at low viral loads (<10⁴/ml) to about 1100–1250 s at high viral loads (>10¹¹/ml). The implications are discussed.     

Advances in the Analysis of Pharmaceuticals by Using Graphene-Based Sensors

Safe and effective use of drugs relies on proper pharmaceutical analysis. Graphene has been extensively used to construct sensors for this purpose. Over the years, a large variety of pharmaceutical sensors have been developed from graphene or its derivatives. This article reviews the current status of sensor development from graphene and its derivatives, and discusses the use of graphene-based sensors in pharmaceutical analysis. It is hoped that this article offers not only a snapshot of recent advances in the fabrication and use of graphene-based sensors, but also provides insights into future engineering and optimization of the sensors for effective pharmaceutical analysis.     

SWI post processing using granularity controlled edge-preserved denoising of multichannel GRE images

The influence of granularities in the background suppressed phase of susceptibility-weighted images (SWI) and susceptibility-weighted angiogram (SWAN) becomes significant when the susceptibility based contrast is enhanced by exponential weighting of the high-pass filtered phase. Furthermore, the effect of noise due to the inherently low signal-to-noise ratio resulting from high-resolution SWI/SWAN acquisition, can be minimized by application of edge-preserved denoising of the channel phase images without loss of venous structural details. Simultaneous reduction of granularity effects with edge-preserved denoising is achieved using the proposed granularity controlled adaptive edge-preserved regularization (GRADER). In this approach, the edge-preserving cost is minimized with respect to the desired channel phase image and an unknown scale parameter that adaptively tunes the high-pass filter. The algorithm is implemented using quasi-Newton type iterations, with the scale parameter updated using a search procedure in each alternating minimization step. The iterations are stopped once the scale parameter converges to a steady state value. Extension of GRADER to parallel MRI (pMRI) by processing the real and imaginary components of complex channel images (IR-GRADER) results in enhanced susceptibility-related contrast-to-noise ratio of the magnitude SWI, leading to improved visualization of superficial veins and deep gray matter structures.     

Influence of mixed alkali on the spectral properties of vanadyl ions doped xNa2O-(30 − x)K2O-60B2O3 glasses—an EPR and optical study

Electron paramagnetic resonance (EPR) and optical investigations have been performed in the mixed alkali borate xNa₂O-(30 − x)K₂O-60B₂O₃ (5 ≤ x ≤ 25) glasses doped with 10 mol% of vanadyl ions in order to look for the effect of ‘mixed alkalis’ on the spectral properties of the glasses. The observed EPR spectra have structures for x > 5 mol% which are characteristic of a hyperfine interaction arising from an unpaired electron with the ⁵¹V nucleus and it builds up in intensity as x increases. It is observed that the mixed alkali play a significant role in accommodating the vanadyl ions in these mixed alkali glasses and for x > 5 mol%, shows a well resolved hyperfine structure typical for isolated VO²⁺ ions. The spin-Hamiltonian parameters (g and A), the dipolar hyperfine coupling parameter (P) and Fermi contact interaction parameter (k) have been evaluated. It is observed that the spin-Hamiltonian parameters do not vary much with the change in composition. It is observed that with increase of x, an increase occurs in tetragonal distortion for VO²⁺. The number of spins (N) participating in resonance and the paramagnetic susceptibility (χ) have been calculated. It is observed that N and χ increase with x. The optical bandgap energies evaluated for these glasses slightly increase with x and reach a maximum around x = 20 and thereafter decrease showing the mixed alkali effect. Optical band gap energies obtained in the present work vary from 2.73 to 3.10 eV for both the direct and indirect transitions. The physical parameters of the glasses are also determined with respect to the composition.     

A Novel Mesoporous Silica‐Supported Lewis Acid Catalyst for C?C Bond Formation Reactions in Water

Catalytic allylation of carbonyl compounds in water using tetraallyltin in the presence of lanthanum sulfonate immobilized on mesoporous silica (SBA‐15) has been carried out for the first time. The reaction proceeded smoothly in water and furnished the corresponding homoallylic alcohols in good to excellent yields. The catalyst could be reused without significant loss of catalytic activity.     

Recycling Chiral Imidazolidin‐4‐one Catalyst for Asymmetric Diels–Alder Reactions: Screening of Various Ionic Liquids

Various imidazolium ionic liquids such as [Bmim]PF₆, [Bmim]SbF₆, [Bmim]OTf and [Bmim]BF₄ were screened for recycling an organic catalyst [(5S)‐5‐benzyl‐2,2,3‐trimethylimidazolidin‐4‐one ( 1 )] for asymmetric Diels–Alder reactions. Good yields and enantioselectivies (up to 85% yield and 93% ee) were obtained from reactions in [Bmim]PF₆ or [Bmim]SbF₆. However, reactions in [Bmim]OTf or [Bmim]BF₄ gave racemic products in low yields. Isolation of the products by simple extraction using diethyl ether allowed recycling of the ionic liquid containing the immobilized catalyst in subsequent reactions without significant decrease of yields and enantioselectivities.