scholarly journals A Call to Record Stormwater Control Functions and to Share Network Data

Author(s):  
Benjamin Choat ◽  
Amber Pulido ◽  
Aditi S. Bhaskar ◽  
Rebecca L. Hale ◽  
Harry X. Zhang ◽  
...  
Repositor ◽  
2020 ◽  
Vol 2 (11) ◽  
pp. 1491
Author(s):  
Muhammad Qaidin Syahputra ◽  
Denar Regata Akbi ◽  
Diah Risqiwati

Software Defined Network (SDN) merupakan paradigma baru dalam manajemen jaringan yang memberikan fasilitas untuk melakukan konfigurasi, virtualisasi, dan mengolah infrasturktur jaringan secara terpusat. Manajemen jaringan secara terpusat dilakukan pada SDN Controller yang dimana memisahkan network data plane dari control functions. Serangan Distributed Denial of Service (DDoS) adalah salah satu permasalahan besar dalam kemanan jaringan yang menyebabkan services yang ada pada jaringan menjadi tidak dapat diakses dalam jangka waktu tertentu. Penelitian ini bertujuan untuk membuat sistem deteksi menggunakan algortima Decision Tree dan mitigasi serangan DDoS dengan metode drop packet pada Software Defined Network. Model klasifikasi yang telah dibangun berdasarkan dataset CICIDS 2017 diterapkan pada controller dan kemudian menjadi pendeteksi serangan DDoS jenis User Data Protocol (UDP). Setiap packet in yang masuk ke dalam controller akan melalui proses pendeteksian sebelum diteruskan kepada destination source, adapun jika packet in terdeteksi sebagai serangan DDoS maka controller akan melakukan fungsi mitigasi drop packet terhadap host yang terbukti melakukan penyerangan. Dari percobaan yang telah dilakukan UDP Flood terbukti menghabiskan banyak network resources dan meningkatkan penggunaan CPU sehingga menyebabkan controller mengalami gangguan berfungsi selama proses penyerangan berlangsung. Hasil penelitian ini menunjukkan bahwa sistem yang dibuat berhasil melakukan proses deteksi dan mitigasi  serangan UDP Flood dengan akurasi sebesar 99.95% dan diikuti proses mitigasi dari setiap paket yang terbukti melakukan penyerangan.   Kata kunci: SDN, CICIDS 2017, UDP Flood, Decision Tree, Drop Packet.


Author(s):  
U. Gross ◽  
P. Hagemann

By addition of analytical equipment, scanning transmission accessories and data processing equipment the basic transmission electron microscope (TEM) has evolved into a comprehensive information gathering system. This extension has led to increased complexity of the instrument as compared with the straightforward imaging microscope, since in general new information capacity has required the addition of new control hardware. The increased operational complexity is reflected in a proliferation of knobs and buttons.In the conventional electron microscope design the operating panel of the instrument has distinct control elements to alter optical conditions of the microscope column in different modes. As a consequence a multiplicity of control functions has been inevitable. Examples of this are the three pairs of focus and magnification controls needed for TEM imaging, diffraction patterns, and STEM images.


2015 ◽  
Vol 21 ◽  
pp. 301
Author(s):  
Armand Krikorian ◽  
Lily Peng ◽  
Zubair Ilyas ◽  
Joumana Chaiban

Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 42-47 ◽  
Author(s):  
Bonne J. H. Zijlstra ◽  
Marijtje A. J. van Duijn ◽  
Tom A. B. Snijders

The p 2 model is a random effects model with covariates for the analysis of binary directed social network data coming from a single observation of a social network. Here, a multilevel variant of the p 2 model is proposed for the case of multiple observations of social networks, for example, in a sample of schools. The multilevel p 2 model defines an identical p 2 model for each independent observation of the social network, where parameters are allowed to vary across the multiple networks. The multilevel p 2 model is estimated with a Bayesian Markov Chain Monte Carlo (MCMC) algorithm that was implemented in free software for the statistical analysis of complete social network data, called StOCNET. The new model is illustrated with a study on the received practical support by Dutch high school pupils of different ethnic backgrounds.


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 7-15 ◽  
Author(s):  
Joachim Gerich ◽  
Roland Lehner

Although ego-centered network data provide information that is limited in various ways as compared with full network data, an ego-centered design can be used without the need for a priori and researcher-defined network borders. Moreover, ego-centered network data can be obtained with traditional survey methods. However, due to the dynamic structure of the questionnaires involved, a great effort is required on the part of either respondents (with self-administration) or interviewers (with face-to-face interviews). As an alternative, we will show the advantages of using CASI (computer-assisted self-administered interview) methods for the collection of ego-centered network data as applied in a study on the role of social networks in substance use among college students.


Methodology ◽  
2006 ◽  
Vol 2 (1) ◽  
pp. 24-33 ◽  
Author(s):  
Susan Shortreed ◽  
Mark S. Handcock ◽  
Peter Hoff

Recent advances in latent space and related random effects models hold much promise for representing network data. The inherent dependency between ties in a network makes modeling data of this type difficult. In this article we consider a recently developed latent space model that is particularly appropriate for the visualization of networks. We suggest a new estimator of the latent positions and perform two network analyses, comparing four alternative estimators. We demonstrate a method of checking the validity of the positional estimates. These estimators are implemented via a package in the freeware statistical language R. The package allows researchers to efficiently fit the latent space model to data and to visualize the results.


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