Detection of Gray Hole Attack in Software Defined Networks

Gray Hole Attack is an advanced transformation of black hole attack. Both of them are a common type of attack in Wireless Sensor Network (WSN). Malicious nodes may constantly or randomly drop packets and therefore reduce the efficiency of the networking system. Furthermore Software Define Network (SDN) has been highly developed in recent years. In this type of networks switch/router functionality is separated into the control plane and data plane. Network managers can select control policies and build operating rules according to their own preferences. In addition, network protocols and packet fields are also programmable. Because the switch/router only implements the data transmission and executes the switching/routing decisions based on commends coming from control plane. Compromised switches/routers themselves or malicious control instructions both can result in selectively dropped packets. This makes a gray hole attack possible in the infrastructure of SDN. Therefore, this paper would like to discuss time-base and random-base gray hole attack in SDN, and then propose a useful detection method based on weighted K-Nearest Neighbor (KNN) and Genetic Algorithm (GA). The simulation data collected from switches/routers indicate that our method does demonstrate pretty good performance

AN APPROACH TO RISK MANAGEMENT FOR E-COMMERCE

Today’s trend of online shopping proves the vital role e-commerce plays in our daily life. Online transactions require reliable networks, and reliable networks depend on secure information technology. These networks have many advantages, but they have disadvantages as well—notably, the need for risk management. The growing importance of e-commerce, with its associated need to ensure trust in online transactions, has led the authors to study and propose risk management in e-commerce from a holistic perspective, thus enabling the implementation of real-time auditing of e-commerce transactions using the digital agents’ technology. In this paper, the authors discuss e-commerce’s risks and present a methodology that can be used to manage those risks. It concludes that e-commerce risks are a high priority for online businesses, and that many of the requisite controls are extensions of controls for managing risk in other information systems

A new approach to observational cosmology using the scattering transform

Parameter estimation with non-Gaussian stochastic fields is a common challenge in astrophysics and cosmology. In this paper we advocate performing this task using the scattering transform, a statistical tool rooted in the mathematical properties of convolutional neural nets. This estimator can characterize a complex field without explicitly computing higher-order statistics, thus avoiding the high variance and dimensionality problems. It generates a compact set of coefficients which can be used as robust summary statistics for non-Gaussian information. It is especially suited for fields presenting localized structures and hierarchical clustering, such as the cosmological density field. To demonstrate its power, we apply this estimator to the cosmological parameter inference problem in the context of weak lensing. Using simulated convergence maps with realistic noise, the scattering transform outperforms the power spectrum and peak counts, and is on par with the state-of-the-art CNN. It retains the advantages of traditional statistical descriptors (it does not require any training nor tuning), has provable stability properties, allows to check for systematics, and importantly, the scattering coefficients are interpretable. It is a powerful and attractive estimator for observational cosmology and, in general, the study of physically-motivated fields.Comment: 15 pages, 7 figures; comments welcom

Dynamical masses across the Hertzsprung-Russell diagram

We infer the dynamical masses of stars across the Hertzsprung-Russell (H-R) diagram using wide binaries from the Gaia survey. Gaia's high-precision astrometry measures the wide binaries' orbital motion, which contains the mass information. Using wide binaries as the training sample, we measure the mass of stars across the two-dimensional H-R diagram using the combination of statistical inference and neural networks. Our results provide the dynamical mass measurements for main-sequence stars from 0.1 to 2 M$_\odot$, unresolved binaries and unresolved triples on the main sequence, and the mean masses of giants and white dwarfs. Two regions in the H-R diagram show interesting behaviors in mass, where one of them is pre-main-sequence stars, and the other one may be related to close compact object companions like M dwarf-white dwarf binaries. These mass measurements depend solely on Newtonian dynamics, providing independent constraints on stellar evolutionary models and the occurrence rate of compact objects.Comment: Fig. 5 and Fig. 12 are the key results. Submitted to MNRAS. Comments are welcome

Strain rate dependent mechanical properties in single crystal nickel nanowires

We measure the strain rate dependence of 0.2% offset yield stress in single-crystal nickel nanowires with diameters ranging from 80 to 300 nm. In situ tensile experiments with strain rates from 10 4 s 1 to 10 2 s 1 were conducted, and the small activation volume ( 10b3, where b is the Burgers vector length) and high strain-rate sensitivity ( 0.1) were obtained. These results agreed with atomistic simulations. Our work provides insights into the strength-limiting and rate-controlling mechanism of plasticity at the nanoscale

Seismic analysis of the condensate storage tank in a nuclear power plant

Following the nuclear power plant accident in Fukushima Japan, seismic capacity evaluation has become a crucial issue in combination building safety. Condensate storage tanks are designed to supplies water to the condensate transfer pumps, the control rod drive hydraulic system pumps, and the condenser makeup. A separate connection to the condensate storage tank is used to supply water for the high pressure coolant injection system, reactor core isolation cooling system, and core spray system pumps. A condensate storage tank is defined as a seismic class I structure, playing the important role of providing flow to the operational system and the required static head for the suction of the condensate transfer pumps and the normal supply pump. According to the latest nuclear safety requirements, soil structure interaction must be considered in all seismic analyses. This study aims to rebuild the computer model of condensate storage tanks in Taiwan using the SAP 2000 program in conjunction with the lumped mass stick model and to evaluate the soil structure interaction by employing the SASSI 2000 program. The differences between the results with the soil structure interaction and spring model are compared via natural frequency and response spectrum curves. This computer model enables engineers to rapidly evaluate the safety margin of condensate storage tank following the occurrence of earthquakes or tsunamis