A Review-Botnet Detection and Suppression in Clouds

Internet security problems remain a major challenge with many security concerns such as Internet worms, spam, and phishing attacks. Botnets is well-organized distributed network attacks, consist of a large number of bots that generate huge volumes of spam or launch Distributed Denial of Service (DDoS) attacks on victim hosts. Botnet attacks degrade the status of Internet security. Clouds provide botmaster with an ideal environment of rich computing resources where it can easily deploy or remove C&C server and perform attacks.  It is of vital importance for cloud service providers to detect botnet,  prevent attack,  and trace back to the botmaster.  It also becomes necessary to detect and suppress these bots to protect the clouds. This paper provides the various botnet detection techniques and the comparison of various botnet detection techniques. It also provides the botnet suppression technique in cloud. Keywords: Cloud computing, network security, botnet, botmmaster, botnet detection, botnet suppressio

Astrophysical S_{17}(0) factor from a measurement of d(7Be,8B)n reaction at E_{c.m.} = 4.5 MeV

Angular distribution measurements of $^2$H($^7$Be,$^7$Be)$^2$H and $^2$H($^7$Be,$^8$B)$n$ reactions at $E_{c.m.}\sim$~4.5 MeV were performed to extract the astrophysical $S_{17}(0)$ factor using the asymptotic normalization coefficient (ANC) method. For this purpose a pure, low emittance $^7$Be beam was separated from the primary $^7$Li beam by a recoil mass spectrometer operated in a novel mode. A beam stopper at 0$^{\circ}$ allowed the use of a higher $^7$Be beam intensity. Measurement of the elastic scattering in the entrance channel using kinematic coincidence, facilitated the determination of the optical model parameters needed for the analysis of the transfer data. The present measurement significantly reduces errors in the extracted $^7$Be(p,$\gamma$) cross section using the ANC method. We get $S_{17}$~(0)~=~20.7~$\pm$~2.4 eV~b.Comment: 15 pages including 3 eps figures, one figure removed and discussions updated. Version to appear in Physical Review

Electromagnetic transition from the 4+^+ to 2+^+ resonance in 8^8Be measured via the radiative capture in 4^4He+4^4He

An earlier measurement on the 4$^+$ to 2$^+$ radiative transition in $^8$Be provided the first electromagnetic signature of its dumbbell-like shape. However, the large uncertainty in the measured cross section does not allow a stringent test of nuclear structure models. The present paper reports a more elaborate and precise measurement for this transition, via the radiative capture in the $^4$He+$^4$He reaction, improving the accuracy by about a factor of three. The {\it ab initio} calculations of the radiative transition strength with improved three-nucleon forces are also presented. The experimental results are compared with the predictions of the alpha cluster model and {\it ab initio} calculations.Comment: 5 pages and 7 figures, Submitted to Physical Review Letter

A Learning Automata Based Solution to Service Selection in Stochastic Environments

With the abundance of services available in today’s world, identifying those of high quality is becoming increasingly difficult. Reputation systems can offer generic recommendations by aggregating user provided opinions about service quality, however, are prone to ballot stuffing and badmouthing . In general, unfair ratings may degrade the trustworthiness of reputation systems, and changes in service quality over time render previous ratings unreliable. In this paper, we provide a novel solution to the above problems based on Learning Automata (LA), which can learn the optimal action when operating in unknown stochastic environments. Furthermore, they combine rapid and accurate convergence with low computational complexity. In additional to its computational simplicity, unlike most reported approaches, our scheme does not require prior knowledge of the degree of any of the above mentioned problems with reputation systems. Instead, it gradually learns which users provide fair ratings, and which users provide unfair ratings, even when users unintentionally make mistakes. Comprehensive empirical results show that our LA based scheme efficiently handles any degree of unfair ratings (as long as ratings are binary). Furthermore, if the quality of services and/or the trustworthiness of users change, our scheme is able to robustly track such changes over time. Finally, the scheme is ideal for decentralized processing. Accordingly, we believe that our LA based scheme forms a promising basis for improving the performance of reputation systems in general

Deterministic Sampling and Range Counting in Geometric Data Streams

We present memory-efficient deterministic algorithms for constructing epsilon-nets and epsilon-approximations of streams of geometric data. Unlike probabilistic approaches, these deterministic samples provide guaranteed bounds on their approximation factors. We show how our deterministic samples can be used to answer approximate online iceberg geometric queries on data streams. We use these techniques to approximate several robust statistics of geometric data streams, including Tukey depth, simplicial depth, regression depth, the Thiel-Sen estimator, and the least median of squares. Our algorithms use only a polylogarithmic amount of memory, provided the desired approximation factors are inverse-polylogarithmic. We also include a lower bound for non-iceberg geometric queries.Comment: 12 pages, 1 figur

Impurity measurements in semiconductor materials using trace element accelerator mass spectrometry

Abstract Accelerator mass spectrometry (AMS) is commonly used to determine the abundance ratios of long-lived isotopes such as 10 B, 14 C, 36 Cl, 129 I, etc. to their stable counterparts at levels as low as 10 À16 . Secondary ion mass spectrometry (SIMS) is routinely used to determine impurity levels in materials by depth profiling techniques. Trace-element accelerator mass spectrometry (TEAMS) is a combination of AMS and SIMS, presently being used at the University of North Texas, for high-sensitivity (ppb) impurity analyses of stable isotopes in semiconductor materials. The molecular break-up characteristics of AMS are used with TEAMS to remove the molecular interferences present in SIMS. Measurements made with different substrate/impurity combinations demonstrate that TEAMS has higher sensitivity for many elements than other techniques such as SIMS and can assist with materials characterization issues. For example, measurements of implanted As in the presence of Ge in Ge x Si 1Àx /Si is difficult with SIMS because of molecular interferences from 74 GeH, 29 Si 30 Si 16 O, etc. With TEAMS, the molecular interferences are removed and higher sensitivities are obtained. Measured substrates include Si, SiGe, CoSi 2 , GaAs and GaN. Measured impurities include B, N, F, Mg, P, Cl, Cr, Fe, Ni, Co, Cu, Zn, Ge, As, Se, Mo, Sn and Sb. A number of measurements will be presented to illustrate the range and power of TEAMS.