Security challenges in internet of things: Distributed denial of service attack detection using support vector machine-based expert systems

The rapid development of internet of things (IoT) is to be the next generation of the IoT devices are a simple target for attackers due to the lack of security. Attackers can easily hack the IoT devices that can be used to form botnets, which can be used to launch distributed denial of service (DDoS) attack against networks. Botnets are the most dangerous threat to the security systems. Software-defined networking (SDN) is one of the developing filed, which introduce the capacity of dynamic program to the network. Use the flexibility and multidimensional characteristics of SDN used to prevent DDoS attacks. The DDoS attack is the major attack to the network, which makes the entire network down, so that normal users might not avail the services from the server. In this article, we proposed the DDoS attack detection model based on SDN environment by combining support vector machine classification algorithm is used to collect flow table values in sampling time periods. From the flow table values, the five-tuple characteristic values extracted and based on it the DDoS attack can be detected. Based on the experimental results, we found the average accuracy rate is 96.23% with a normal amount of traffic flow. Proposed research offers a better DDoS detection rate on SDN.     

Modelling of Chevron electrothermal actuator and its performance analysis

Microsystem Technologies - In this work, a Chevron electrothermal actuator is designed and its analytical model is developed. Chevron actuator works on the principle of Joules heating effect and...     

Position-Based Adaptive Clustering Model (PACM) for Efficient Data Caching in Vehicular Named Data Networks (VNDN)

The seamless data delivery is essential in VANET for application such as autonomous vehicle, intelligent traffic management and for the road safety and emergency applications. The incorporation of named data networking (NDN) with VANET, intended to frame intelligent traffic flow and seamless data delivery. Such integration of vehicular ad hoc networks (VANET) with NDN is termed as vehicular named data networks (VNDN). Because of the continuous node/vehicle mobility, it is a tedious process to build constant and consistent communication between vehicles. With that concern, for enhancing the performance of VNDN and solving the issues such as frequent cluster formation on heavy loaded data transmissions, position-based adaptive clustering model (PACM) is developed. The major intention of PACM is to form clusters based on trajectory. Besides, PACM performs efficient data caching by collecting significant data from vehicles to establish consistent data communication with all nodes in the network. Efficient data caching is done with the elected cluster heads among the framed clusters based on its positions and mobility models. For handling the vehicles at higher mobility speed, mutual data caching process is also designed that makes vehicles to perform on-demand data gathering from cluster heads. Further, the model is simulated and the obtained results are compared with the existing models based on the metrics such as packet delivery ratio, mean delay, cache hit rate and mean hop distance. The comparative analysis shows that the proposed model outperforms the available techniques.

     

Steering the Craft: UI Elements and Visualizations for Supporting Progressive Visual Analytics

Progressive visual analytics (PVA) has emerged in recent years to manage the latency of data analysis systems. When analysis is performed progressively, rough estimates of the results are generated quickly and are then improved over time. Analysts can therefore monitor the progression of the results, steer the analysis algorithms, and make early decisions if the estimates provide a convincing picture. In this article, we describe interface design guidelines for helping users understand progressively updating results and make early decisions based on progressive estimates. To illustrate our ideas, we present a prototype PVA tool called Insights Feed for exploring Twitter data at scale. As validation, we investigate the tradeoffs of our tool when exploring a Twitter dataset in a user study. We report the usage patterns in making early decisions using the user interface, guiding computational methods, and exploring different subsets of the dataset, compared to sequential analysis without progression.     

Highly Reproducible and Regulated Conductance Quantization in a Polymer‐Based Atomic Switch

A detailed understanding of the conductance quantization and resistive switching phenomena in redox‐based memories is crucial for realizing atomic‐scale memory devices and for finding the adequate design principles on which they can be based. Here, the emergence of quantized conductance states and their correlation with resistive switching characteristics in polymer‐based atomic switches are investigated using combinations of current–voltage measurements and first‐principles density functional theory (DFT) simulations. Various conductance states, including integer and half‐integer multiples of a single atomic point contact and fractional conductance variations, are observed in an Ag/polyethylene oxide/Pt device under sweeping of bias voltage. Moreover, highly controllable and reproducible quantized conductance behaviors by tuning the voltage sweep rate and the sweep voltage range, suggesting well‐controlled formation of the atomic point contact, are demonstrated. The device also exhibits longer retention times for higher conductance states. The DFT simulations reveal the transmission eigenstate of geometrically optimized atomic point contact structures and the impact of the atomic configurations and structural stability on the conductance state, which also explains their resistive switching behaviors. The well‐defined, multiple quantized conductance states observed in these polymer‐based atomic switches show promise for the development of new multilevel memory devices.     

Growth of hydroxyapatite on physiologically clotted fibrin capped gold nanoparticles

The growth of hydroxyapatite (HAp) on physiologically clotted fibrin (PCF)-gold nanoparticles is presented for the first time by employing a wet precipitation method. Fourier transform infrared (FTIR) spectroscopy confirmed the characteristic functionalities of PCF and HAp in the PCF-Au-HAp nanocomposite. Scanning electron microscopy (SEM) images have shown cuboidal nanostructures having a size in the range of 70-300?nm of HAp, whereas 2-50?nm sized particles were visualized in high-resolution transmission electron microscopy (TEM). Energy-dispersive x-ray (EDX) and x-ray diffraction (XRD) studies have confirmed the presence of HAp. These results show that gold nanoparticles with PCF acted as a matrix for the growth of HAp, and that PCF-Au-HAp nanocomposite is expected to have better osteoinductive properties.     

Vertically aligned single crystal TiO2 nanowire arrays grown directly on transparent conducting oxide coated glass: synthesis details and applications

Single-crystal one-dimensional (1D) semiconductor architectures are important in materials-based applications requiring a large surface area, morphological control, and superior charge transport. Titania has widespread utility in applications including photocatalysis, photochromism, photovoltaics, and gas sensors. While considerable efforts have focused on the preparation of 1D TiO2, no methods have been available to grow crystalline nanowire arrays directly onto transparent conducting oxide (TCO) substrates, greatly limiting the performance of TiO2 photoelectrochemical devices. Herein, we present a straightforward low temperature method to prepare single crystal rutile TiO2 nanowire arrays up to 5 microm long on TCO glass via a non-polar solvent/hydrophilic substrate interfacial reaction under mild hydrothermal conditions. The as-prepared densely packed nanowires grow vertically oriented from the TCO glass substrate along the (110) crystal plane with a preferred (001) orientation. In a dye sensitized solar cell, N719 dye, using TiO2 nanowire arrays 2-3 microm long we achieve an AM 1.5 photoconversion efficiency of 5.02%.     

RF channelizer architectures using 3-way iterative down conversion for concurrent or fast-switching spectrum analysis

A wide-band RF channelizer architecture using the concept of 3-way iterative down-conversion is introduced. An example RF channelizer implementation splits the input spectrum of 0.6–9 GHz into 7 channels each with a 1.2 GHz bandwidth. This RF channelizer implementation has the ability of concurrently down-converting 3 channels enabling multi-Gbps aggregate data reception. It further demonstrates the ability to rapidly switch from receiving one channel to another which is crucial for rapid spectrum analysis. A prototype of the RF channelizer has been fabricated in a 65 nm standard CMOS process. A 400 Mbps (BPSK) data reception has been demonstrated by down-converting two channels concurrently. Channel switching can be as fast as 8 ns and is always faster than \(1\,\upmu s\). The chip occupies an area of 2 mm \(\times\) 1 mm and consumes an average power of 435 mW while offering a dynamic range between 58 and 63 dB.     

Cystic resolution: a performance metric for ultrasound imaging systems

This paper describes a metric that can be used to characterize the resolution of arbitrary broadband coherent imaging systems. The metric is particularly suited to medical ultrasound because it characterizes scanner performance using the contrast obtained by imaging anechoic cysts of various sizes that are embedded in a speckle-generating background, accounting for the effect of electronic noise. We present the theoretical derivation of the metric and provide simulation examples that demonstrate its utility. We use the metric to compare a low-cost, handheld, C-scan system under development in our laboratory to conventional ultrasound scanners. We also present the results of simulations that were designed to evaluate and optimize various parameters in our system, including the f/# and apodization windows. We investigate the impact of electronic noise on our system and quantify the tradeoffs associated with quantization in the analog to digital converter. Results indicate that an f/1 receive aperture combined with 10-bit precision and a signal-to-noise ratio (SNR) of 0 dB per channel would result in adequate image quality.     

Vertically oriented Ti-Fe-O nanotube array films: toward a useful material architecture for solar spectrum water photoelectrolysis

In an effort to obtain a material architecture suitable for high-efficiency visible spectrum water photoelectrolysis, herein we report on the fabrication and visible spectrum (380-650 nm) photoelectrochemical properties of self-aligned, vertically oriented Ti-Fe-O nanotube array films. Ti-Fe metal films of variable composition, iron content ranging from 69% to 3.5%, co-sputtered onto FTO-coated glass are anodized in an ethylene glycol + NH4F electrolyte. The resulting amorphous samples are annealed in oxygen at 500 degrees C, resulting in nanotubes composed of a mixed Ti-Fe-O oxide. Some of the iron goes into the titanium lattice substituting titanium ions, and the rest either forms alpha-Fe2O3 crystallites or remains in the amorphous state. Depending upon the Fe content, the band gap of the resulting films ranges from about 380 to 570 nm. The Ti-Fe oxide nanotube array films are utilized in solar spectrum water photoelectrolysis, demonstrating 2 mA/cm2 under AM 1.5 illumination with a sustained, time-energy normalized hydrogen evolution rate by water splitting of 7.1 mL/W.hr in a 1 M KOH solution with a platinum counter electrode under an applied bias of 0.7 V. The surface morphology, structure, elemental analysis, optical, and photoelectrochemical properties of the Ti-Fe oxide nanotube array films are considered.