Multilayer Self-Defense System to Protect Enterprise Cloud

A data breach can seriously impact organizational intellectual property, resources, time, and product value. The risk of system intrusion is augmented by the intrinsic openness of commonly utilized technologies like TCP/IP protocols. As TCP relies on IP addresses, an attacker may easily trace the IP address of the organization. Given that many organizations run the risk of data breach and cyber-attacks at a certain point, a repeatable and well-developed incident response framework is critical to shield them. Enterprise cloud possesses the challenges of security, lack of transparency, trust and loss of controls. Technology eases quickens the processing of information but holds numerous risks including hacking and confidentiality problems. The risk increases when the organization outsources the cloud storage services through the vendor and suffers from security breaches and need to create security systems to prevent data networks from being compromised. The business model also leads to insecurity issues which derail its popularity. An attack mitigation system is the best solution to protect online services from emerging cyber-attacks. This research focuses on cloud computing security, cyber threats, machine learning-based attack detection, and mitigation system. The proposed SDN-based multilayer machine learning-based self-defense system effectively detects and mitigates the cyber-attack and protects cloud-based enterprise solutions. The results show the accuracy of the proposed machine learning techniques and the effectiveness of attack detection and the mitigation system.     

Growth and characterization of Ni substituted Bi2Se3 single crystals

Journal of Superconductivity and Novel Magnetism - In this article, we report synthesis and magneto-transport analysis of Ni substituted Bi2Se3 crystals. Phase purity and crystalline growth are...     

Controller Placement in Software Defined Internet of Things Using Optimization Algorithm

The current and future status of the internet is represented by the upcoming Internet of Things (IoT). The internet can connect the huge amount of data, which contains lot of processing operations and efforts to transfer the pieces of information. The emerging IoT technology in which the smart ecosystem is enabled by the physical object fixed with software electronics, sensors and network connectivity. Nowadays, there are two trending technologies that take the platform i.e., Software Defined Network (SDN) and IoT (SD-IoT). The main aim of the IoT network is to connect and organize different objects with the internet, which is managed with the control panel and data panel in the SD network. The main issue and the challenging factors in this network are the increase in the delay and latency problem between the controllers. It is more significant for wide area networks, because of the large packet propagation latency and the controller placement problem is more important in every network. In the proposed work, IoT is implementing with adaptive fuzzy controller placement using the enhanced sunflower optimization (ESFO) algorithm and Pareto Optimal Controller placement tool (POCO) for the placement problem of the controller. In order to prove the efficiency of the proposed system, it is compared with other existing methods like PASIN, hybrid SD and PSO in terms of load balance, reduced number of controllers and average latency and delay. With 2 controllers, the proposed method obtains 400 miles as average latency, which is 22.2% smaller than PSO, 76.9% lesser than hybrid SD and 91.89% lesser than PASIN.     

Performance evaluation of an industrial solar dryer in Indian scenario: a techno-economic and environmental analysis

Clean Technologies and Environmental Policy - Food product drying is used to keep food products preserved for a long period and reduce post-harvest losses. Many industries dealing with food drying...     

Structural,dielectric and reflection analysis of ZnxMg1−xTiO3 ceramics synthesized using auto-ignition combustion method

Zinc-substituted magnesium titanate Zn_xMg_1?xTiO₃ (x?=?0.00–0.10, δx?=?0.025) solid solutions were synthesized using auto-ignition combustion process and characterized for structural, dielectric and reflection properties. X-ray diffraction analysis discovered the creation of single-phase MgTiO₃. Scanning electron microscopy (SEM) indicated the existence of low porosity closely packed grains. The study of the dielectric properties indicated that for Zn_xMg_1?xTiO₃ with x?=?0.05, a reasonably great combination of the relative permittivity (?_r) with value 16.60, loss tangent (tanδ) of 0.9?×?10^?3 and temperature coefficient of resonant frequency (τ_f) of ? 41.37 ppm/°C at 10 kHz was observed. The electromagnetic reflection analysis at microwave frequencies (12.4–26.5 GHz) revealed that composition x?=?0.05 shows maximum 90% reflection bandwidth of 5.60 GHz in the Ku frequency band and 4.59 GHz in the K frequency band. These materials with low to intermediate relative permittivity, low losses, and a minor negative temperature coefficient of resonant frequency might be utilized as a resonator, antenna, and filter substrate for next-generation communication devices.

     

A review on graphene and its derivatives as the forerunner of the two-dimensional material family for the future

Journal of Materials Science - In 2004, Geim and Novoselov discovered two-dimensional graphene comprised of sp2 carbon atoms. Graphene is a thin layer of carbon consisting of excellent surface...     

Top Indian scientists as public communicators: a survey of their perceptions,attitudes and communication behaviors

Scientometrics - Research on science communication, especially from scientists’ point of view, is rare in the Indian context. This first of its kind study in India explores the perceptions...     

Influence of growth parameters on the dopant configuration of nitrogen-doped graphene synthesized from phthalocyanine molecules

Synthesis of nitrogen-doped graphene (NDG) via chemical vapor deposition (CVD) using phthalocyanine, a solid precursor containing carbon and nitrogen, is reported. The effect of the growth parameters (temperature, time, and carrier gas) on the surface morphology, dopant configuration, and conductivity of the films was studied. The NDG films were synthesized at different substrate temperatures of 1050 °C, 950 °C, and 850 °C for different growth times of 5–15 min in the presence of an Ar?+?H₂ gas mixture. Significantly, pyrrolic-N type defects are observed predominantly after 5 min of growth time. At 1050 °C, pyrrolic N content is around 45.4% after 5 min of growth which decreased to 24.1% after 15 min of growth, while the graphitic-N content increased from 41.2 to 76% at the same time. It is demonstrated that the conversion of pyrrolic type of nitrogen to graphitic nitrogen defects can be arrested by changing the carrier gas from Ar?+?H₂ to Ar. The pyrrolic-N content increased to 64% by changing the gas from Ar?+?H₂ to Ar at 15 min. The electrolyte gated field-effect transistors were fabricated using the obtained films, and dopant-dependent mobility was observed. The mobility for pyrrolic-N-dominated film is 13.6 cm² V^?1 s^?1 increasing to 62.8 cm² V^?1 s^?1 for graphitic-N-dominated film.

     

An improved multimodal medical image fusion scheme based on hybrid combination of nonsubsampled contourlet transform and stationary wavelet transform

This research proposes an improved hybrid fusion scheme for non-subsampled contourlet transform (NSCT) and stationary wavelet transform (SWT). Initially, the source images are decomposed into different sub-bands using NSCT. The locally weighted sum of square of the coefficients based fusion rule with consistency verification is used to fuse the detailed coefficients of NSCT. The SWT is employed to decompose approximation coefficients of NSCT into different sub-bands. The entropy of square of the coefficients and weighted sum-modified Laplacian is employed as the fusion rules with SWT. The final output is obtained using inverse NSCT. The proposed research is compared with existing fusion schemes visually and quantitatively. From the visual analysis, it is observed that the proposed scheme retained important complementary information of source images in a better way. From the quantitative comparison, it is seen that this scheme gave improved edge information, clarity, contrast, texture, and brightness in the fused image.     

Metabolizable Semiconducting Polymer Nanoparticles for Second Near‐Infrared Photoacoustic Imaging

Photoacoustic (PA) imaging in the second near‐infrared (NIR‐II) window (1000–1700 nm) holds great promise for deep‐tissue diagnosis due to the reduced light scattering and minimized tissue absorption; however, exploration of such a noninvasive imaging technique is greatly constrained by the lack of biodegradable NIR‐II absorbing agents. Herein, the first series of metabolizable NIR‐II PA agents are reported based on semiconducting polymer nanoparticles (SPNs). Such completely organic nanoagents consist of π‐conjugated yet oxidizable optical polymer as PA generator and hydrolyzable amphiphilic polymer as particle matrix to provide water solubility. The obtained SPNs are readily degraded by myeloperoxidase and lipase abundant in phagocytes, transforming from nonfluorescent nanoparticles (30 nm) into NIR fluorescent ultrasmall metabolites (≈1 nm). As such, these nanoagents can be effectively cleared out via both hepatobiliary and renal excretions after systematic administration, leaving no toxicity to living mice. Particularly these nanoagents possess high photothermal conversion efficiencies and emit bright PA signals at 1064 nm, enabling sensitive NIR‐II PA imaging of both subcutaneous tumor and deep brain vasculature through intact skull in living animals at a low systematic dosage. This study thus provides a generalized molecular design toward organic metabolizable semiconducting materials for biophotonic applications in NIR‐II window.