Network detection of malicious domain name based on adversary model

2021 ◽  
pp. 1-9
Author(s):  
Xingguo Li ◽  
Junfeng Wang
Keyword(s):  
Rapid Development ◽  
Information Leakage ◽  
Statistical Characteristics ◽  
Domain Name ◽  
Distance Method ◽  
Data Set ◽  
Domain Names ◽  
Legal Domain ◽  
Similar Accuracy ◽  
Adversary Model

With the rapid development of the Internet, threats from the network security are emerging one after another. Driven by economic interests, attackers use malicious domain names to promote the development of botnets and phishing sites, which leads to serious information leakage of victims and devices, the proliferation of DDoS attacks and the rapid spread of viruses. Based on the above background, the purpose of this paper is to study the network detection of malicious domain name based on the adversary model. Firstly, this paper studies the generation mechanism of DGA domain name based on PCFG model, and studies the characteristics of the domain name generated by such DGA. The research shows that the domain name generated by PCFG model is usually based on the legal domain name, so the character statistical characteristics of the domain name are similar to the legal domain name. Moreover, the same PCFG model can often generate multiple types of domain names, so it is difficult to extract appropriate features manually. The experimental results show that the accuracy, recall and accuracy of the performance parameters of the classifier are over 95%. By using the open domain name data set, comparing the linear calculation edit distance method and the detection effect under different thresholds, it is proved that the proposed method can improve the detection speed of misplanted domain names under the condition of similar accuracy.

scholarly journals Mask R-CNN Based C. Elegans Detection with a DIY Microscope

Biosensors ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 257
Author(s):  
Sebastian Fudickar ◽  
Eike Jannik Nustede ◽  
Eike Dreyer ◽  
Julia Bornhorst
Keyword(s):  
Cell Biology ◽  
High Throughput Screening ◽  
Low Cost ◽  
Image Acquisition ◽  
Rapid Development ◽  
Model Organism ◽  
Large Data ◽  
Data Set ◽  
C Elegans ◽  
Do It Yourself

Caenorhabditis elegans (C. elegans) is an important model organism for studying molecular genetics, developmental biology, neuroscience, and cell biology. Advantages of the model organism include its rapid development and aging, easy cultivation, and genetic tractability. C. elegans has been proven to be a well-suited model to study toxicity with identified toxic compounds closely matching those observed in mammals. For phenotypic screening, especially the worm number and the locomotion are of central importance. Traditional methods such as human counting or analyzing high-resolution microscope images are time-consuming and rather low throughput. The article explores the feasibility of low-cost, low-resolution do-it-yourself microscopes for image acquisition and automated evaluation by deep learning methods to reduce cost and allow high-throughput screening strategies. An image acquisition system is proposed within these constraints and used to create a large data-set of whole Petri dishes containing C. elegans. By utilizing the object detection framework Mask R-CNN, the nematodes are located, classified, and their contours predicted. The system has a precision of 0.96 and a recall of 0.956, resulting in an F1-Score of 0.958. Considering only correctly located C. elegans with an [email protected] IoU, the system achieved an average precision of 0.902 and a corresponding F1 Score of 0.906.

Research on the Data Driven Practice Teaching Mode: Take the Didi Data Set as Example

2021 ◽  
Vol 105 ◽  
pp. 348-355
Author(s):  
Hou Xiang Liu ◽  
Sheng Han Zhou ◽  
Bang Chen ◽  
Chao Fan Wei ◽  
Wen Bing Chang ◽  
...  
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Teaching Method ◽  
Rapid Development ◽  
Big Data Analysis ◽  
Theoretical Knowledge ◽  
Data Set ◽  
Teaching Mode ◽  
Practice Teaching ◽  
Practical Teaching

The paper proposed a practice teaching mode by making analysis on Didi data set. There are more and more universities have provided the big data analysis courses with the rapid development and wide application of big data analysis technology. The theoretical knowledge of big data analysis is professional and hard to understand. That may reduce students' interest in learning and learning motivation. And the practice teaching plays an important role between theory learning and application. This paper first introduces the theoretical teaching part of the course, and the theoretical methods involved in the course. Then the practice teaching content of Didi data analysis case was briefly described. And the study selects the related evaluation index to evaluate the teaching effect through questionnaire survey and verify the effectiveness of teaching method. The results show that 78% of students think that practical teaching can greatly improve students' interest in learning, 89% of students think that practical teaching can help them learn theoretical knowledge, 89% of students have basically mastered the method of big data analysis technology introduced in the course, 90% of students think that the teaching method proposed in this paper can greatly improve students' practical ability. The teaching mode is effective, which can improve the learning effect and practical ability of students in data analysis, so as to improve the teaching effect.

Privacy Information Leakage Prevention in Cognitive Social Mining Applications

2019 ◽  
pp. 188-212
Author(s):  
Suriya Murugan ◽  
Anandakumar H.
Keyword(s):  
Social Networks ◽  
Social Relations ◽  
Private Information ◽  
Online Social Networks ◽  
Information Leakage ◽  
User Profile ◽  
User Privacy ◽  
Sensitive Data ◽  
Data Set ◽  
Secure Information

Online social networks, such as Facebook are increasingly used by many users and these networks allow people to publish and share their data to their friends. The problem is user privacy information can be inferred via social relations. This chapter makes a study and performs research on managing those confidential information leakages which is a challenging issue in social networks. It is possible to use learning methods on user released data to predict private information. Since the main goal is to distribute social network data while preventing sensitive data disclosure, it can be achieved through sanitization techniques. Then the effectiveness of those techniques is explored, and the methods of collective inference are used to discover sensitive attributes of the user profile data set. Hence, sanitization methods can be used efficiently to decrease the accuracy of both local and relational classifiers and allow secure information sharing by maintaining user privacy.

Structure and Organization of the Domain Name System

2015 ◽  
Author(s):  
Torsten Bettinger
Keyword(s):  
Host Computer ◽  
The Internet ◽  
Domain Name System ◽  
Domain Name ◽  
Global Regulation ◽  
Ip Address ◽  
Domain Names ◽  
Data Packets ◽  
System P ◽  
Administrative Tasks

Although the Internet has no cross-organizational, financial, or operational management responsible for the entire Internet, certain administrative tasks are coordinated centrally. Among the most important organizational tasks that require global regulation is the management of Internet Protocol (IP) addresses and their corresponding domain names. The IP address consists of an existing 32 bit (IP4) or 128 bit (IP6) sequence of digits and is the actual physical network address by which routing on the Internet takes place and which will ensure that the data packets reach the correct host computer.

Netherlands (‘.nl’)

2015 ◽  
Author(s):  
Adonna Alkema
Keyword(s):  
The Netherlands ◽  
Tort Law ◽  
Domain Name ◽  
Domain Names ◽  
Court Proceedings ◽  
Trade Names ◽  
Appeal Courts

In the Netherlands, there is no legislation dealing with the registration and use of domain names. Domain name conflicts are therefore decided on the basis of existing laws, such as laws regarding the protection of trademarks and trade names and tort law. Domain name conflicts often lead to court proceedings, resulting in over 500 decisions rendered by first instance courts so far and more than 90 decisions rendered by appeal courts.

Italy (‘.it’)

2015 ◽  
Author(s):  
Philipp Fabbio
Keyword(s):  
Civil Law ◽  
Business Activity ◽  
Domain Name ◽  
Domain Names ◽  
General Rules ◽  
Explicit Reference ◽  
Trademark Protection

Statutory provisions dealing specifically with domain names are found in the Codice della Proprietà Industriale (‘the CPI’),1 ss 12(1)(c), 22, 118(6), and 133. Sections 12(1)(c) and 22 define the scope of trademark protection. In doing so, they also consider interference with domain names that are used in the course of a business activity (nomi a dominio aziendali). Sections 118(6) and 133 deal with remedies for trademark infringements and make explicit reference to domain names as well. Besides these specific rules, conflicts before the Italian courts based on domain name registrations are to be resolved according to the general rules of trademark, competition, and civil law.

scholarly journals Intelligent Prediction of Private Information Diffusion in Social Networks

Electronics ◽  
2020 ◽  
Vol 9 (5) ◽  
pp. 719 ◽  
Author(s):  
Yangyang Li ◽  
Hao Jin ◽  
Xiangyi Yu ◽  
Haiyong Xie ◽  
Yabin Xu ◽  
...  
Keyword(s):  
Social Networks ◽  
Private Information ◽  
Information Diffusion ◽  
Social Impact ◽  
Information Leakage ◽  
Prediction Method ◽  
Data Set ◽  
Sina Weibo ◽  
Critical Paths ◽  
The Impact

In the information age, leaked private information may cause significant physical and mental harm to the relevant parties, leading to a negative social impact. In order to effectively evaluate the impact of such information leakage in today’s social networks, it is necessary to accurately predict the scope and depth of private information diffusion. By doing so, it would be feasible to prevent and control the improper spread and diffusion of private information. In this paper, we propose an intelligent prediction method for private information diffusion in social networks based on comprehensive data analysis. We choose Sina Weibo, one of the most prominent social networks in China, to study. Firstly, a prediction model of message forwarding behavior is established by analyzing the characteristic factors that influence the forwarding behavior of the micro-blog users. Then the influence of users is calculated based on the interaction time and topological structure of users relationship, and the diffusion critical paths are identified. Finally, through the user forwarding probability transmission, we determine the micro-blog diffusion cut-off conditions. The simulation results on Sina Weibo data set show that the prediction accuracy is 86.9%, which indicates that our method is efficient to predict the message diffusion in real-world social networks.

scholarly journals Predictive modelling using pathway scores: robustness and significance of pathway collections

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Marcelo P. Segura-Lepe ◽  
Hector C. Keun ◽  
Timothy M. D. Ebbels
Keyword(s):  
Gene Expression ◽  
Prediction Models ◽  
Disease Status ◽  
Predictive Modelling ◽  
Data Set ◽  
Prediction Rules ◽  
Similar Accuracy ◽  
Robust To Noise ◽  
Pathway Databases ◽  
Gene Expression Levels

Abstract Background Transcriptomic data is often used to build statistical models which are predictive of a given phenotype, such as disease status. Genes work together in pathways and it is widely thought that pathway representations will be more robust to noise in the gene expression levels. We aimed to test this hypothesis by constructing models based on either genes alone, or based on sample specific scores for each pathway, thus transforming the data to a ‘pathway space’. We progressively degraded the raw data by addition of noise and examined the ability of the models to maintain predictivity. Results Models in the pathway space indeed had higher predictive robustness than models in the gene space. This result was independent of the workflow, parameters, classifier and data set used. Surprisingly, randomised pathway mappings produced models of similar accuracy and robustness to true mappings, suggesting that the success of pathway space models is not conferred by the specific definitions of the pathway. Instead, predictive models built on the true pathway mappings led to prediction rules with fewer influential pathways than those built on randomised pathways. The extent of this effect was used to differentiate pathway collections coming from a variety of widely used pathway databases. Conclusions Prediction models based on pathway scores are more robust to degradation of gene expression information than the equivalent models based on ungrouped genes. While models based on true pathway scores are not more robust or accurate than those based on randomised pathways, true pathways produced simpler prediction rules, emphasizing a smaller number of pathways.

Statistical Characteristics of Long-Term High-Resolution Precipitable Water Vapor Data at Darwin

2018 ◽  
Vol 10 (04) ◽  
pp. 1850010
Author(s):  
Kimberly Leung ◽  
Aneesh C. Subramanian ◽  
Samuel S. P. Shen
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Southern Oscillation ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Statistical Characteristics ◽  
Convective Precipitation ◽  
Data Set ◽  
Best Estimates

This paper studies the statistical characteristics of a unique long-term high-resolution precipitable water vapor (PWV) data set at Darwin, Australia, from 12 March 2002 to 28 February 2011. To understand the convective precipitation processes for climate model development, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program made high-frequency radar observations of PWV at the Darwin ARM site and released the best estimates from the radar data retrievals for this time period. Based on the best estimates, we produced a PWV data set on a uniform 20-s time grid. The gridded data were sufficient to show the fractal behavior of precipitable water with Hausdorff dimension equal to 1.9. Fourier power spectral analysis revealed modulation instability due to two sideband frequencies near the diurnal cycle, which manifests as nonlinearity of an atmospheric system. The statistics of PWV extreme values and daily rainfall data show that Darwin’s PWV has El Nino Southern Oscillation (ENSO) signatures and has potential to be a predictor for weather forecasting. The right skewness of the PWV data was identified, which implies an important property of tropical atmosphere: ample capacity to hold water vapor. The statistical characteristics of this long-term high-resolution PWV data will facilitate the development and validation of climate models, particularly stochastic models.

scholarly journals An assessment of Bayesian bias estimator for numerical weather prediction

2008 ◽  
Vol 15 (6) ◽  
pp. 1013-1022 ◽  
Author(s):  
J. Son ◽  
D. Hou ◽  
Z. Toth
Keyword(s):  
Time Series ◽  
Numerical Weather Prediction ◽  
Sampling Error ◽  
Weather Prediction ◽  
Training Data ◽  
Statistical Characteristics ◽  
Forecast Errors ◽  
Data Sets ◽  
Data Set ◽  
Numerical Weather

Abstract. Various statistical methods are used to process operational Numerical Weather Prediction (NWP) products with the aim of reducing forecast errors and they often require sufficiently large training data sets. Generating such a hindcast data set for this purpose can be costly and a well designed algorithm should be able to reduce the required size of these data sets. This issue is investigated with the relatively simple case of bias correction, by comparing a Bayesian algorithm of bias estimation with the conventionally used empirical method. As available forecast data sets are not large enough for a comprehensive test, synthetically generated time series representing the analysis (truth) and forecast are used to increase the sample size. Since these synthetic time series retained the statistical characteristics of the observations and operational NWP model output, the results of this study can be extended to real observation and forecasts and this is confirmed by a preliminary test with real data. By using the climatological mean and standard deviation of the meteorological variable in consideration and the statistical relationship between the forecast and the analysis, the Bayesian bias estimator outperforms the empirical approach in terms of the accuracy of the estimated bias, and it can reduce the required size of the training sample by a factor of 3. This advantage of the Bayesian approach is due to the fact that it is less liable to the sampling error in consecutive sampling. These results suggest that a carefully designed statistical procedure may reduce the need for the costly generation of large hindcast datasets.

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