Domain Name Research Articles

Automated management of AWS instances for training.

Amazon Web Services (AWS) instances provide a convenient way to run training on complex 'omics data analysis workflows without requiring participants to install software packages or store large data volumes locally. However, efficiently managing dozens of instances is challenging for training providers. We present a set of Bash scripts that make it quick and easy to manage Linux AWS instances pre-configured with all the software analysis tools and data needed for a course, and accessible using encrypted login keys and optional domain names. Creating over 30 instances takes 10-15minutes. A comprehensive online tutorial describes how to set up and use an AWS account and the scripts, and how to customise AWS instance templates with other software tools and data. We anticipate that others offering similar training may benefit from using the scripts regardless of the analyses being taught.

Personalized Recommendation Method for Cultural Creative Products in Tourism Cities Based on User Profiles

This examine introduces a novel personalised recommendation technique for cultural and innovative merchandise in traveler cities, leveraging the superior abilities of BERT4Rec along designated user photos to noticeably enhance the advice machine's precision and customization. with the aid of undertaking an in-intensity evaluation of users' historical conduct, the studies harnesses the state-of-the-art deep mastering and self-attention mechanisms intrinsic to BERT4Rec. This technique adeptly captures the nuanced long-range dependencies in user activities, imparting a profound perception into the styles and selections that define user behavior. The innovation extends to integrating these dynamic behavioral insights with the static demographic statistics of users—such as age, gender, and occupation—to forge a extra nuanced and whole user profile. This holistic view not solely mirrors the users' beyond interactions however also weaves in fundamental demographic information, paving the way for tailored and nuanced suggestions. The deployment of BERT4Rec stands as a testomony to its prowess in decoding complex consumer engagement tendencies and tendencies, whilst the fusion with consumer pix extensively bolsters the system's potential to cater to the various and specific desires of users.Empirical proof underscores the prevalence of this technique over modern tourism product advice algorithms, marking a giant stride in the realm of cultural innovative product recommendations inside traveler cities. furthermore, this paper's insights and methodologies provide treasured implications for reinforcing advice structures across severa domain names.

Understanding and Characterizing the Adoption of Internationalized Domain Names in Practice

Understanding and Characterizing the Adoption of Internationalized Domain Names in Practice

Longitudinal Measurement Study of the Domain Names Associated With the Olympic Games

Longitudinal Measurement Study of the Domain Names Associated With the Olympic Games

ALTERNATIVE VISIONS FOR THE DNS: CORE, IAHC, AND THE POSSIBILITY FOR EXPANDED GTLDS IN EARLY GOVERNANCE POLICY

In 1994, U.S. President Clinton stated that the commercialization of the internet was a “top priority” for his administration. The domain name system (DNS), which was developed to deal with the growing unwieldiness of the commercial internet, was an early battleground in shaping the values of early internet governance policies. The system would include highly sought after addresses in generic top-level domains (gTLDS) that ended in .com, .gov, .org, .edu, and so on. Below this were second-level domains and country codes which ended web addresses in sequences like .uk, .jp, .ca, etc. This model raised legal and economic questions about trademarks, intellectual property, and the global distribution of addresses on top level domains. Technical experts were wary of the limitations of the proposed system, particularly given the potential to expand the number of gTLDs. While many groups responded to U.S. governance policy, a number of non-profit associations were particular vocal in their critique, most notably the Internet Council of Registrars (CORE), the International Ad Hoc Committee (IAHC), and the Internet Assigned Numbers Authority (IANA) represented by Jon Postel. As the internet transitioned from a network used primarily by government and educational entities to a mass medium, there was a potential for revolutionary modes of communication, information sharing, education, creative expression, and a revolutionary, de-centralized structure of governance. The DNS debate resulted instead in support of predictable structures of power and a failure to realize that potential.

The search term ‘suicide’ is being used to lead web browsers to online casinos

ABSTRACT While Search Engine Optimisation seeks to enhance PageRankings, some methods are not approved or condoned by browser developers. To understand the risks faced by suicidal gamblers in the online environment, 2 studies examined the behaviour of an online search engine. A series of Google searches in 2021 used key terms such as ‘suicide’ and ‘gambling’ that might be employed by a suicidal gambler. During these searches browser ‘hits’ included opportunities to gamble. Webpages (N = 200) offered to a potentially suicidal gambler were primarily categorised as: other suicides (20%), treatment providers (8.5%), online casinos (7%); politics (22%), academic (23.5%). From a Google search providing 1,090 hits, the links to 113 online casinos were classified as a function of Domain Name hijacking, Metatag Stuffing, Error 404, and presence of Malware. There were significant relationships between the size of the businesses whose Domain Names were hijacked, and the presence of Malware. The deliberate use by webpage designers of the word ‘suicide’ to attract customers to online casinos appears inappropriate and ethically questionable.

The Importance of Hormonal Balance for Women's Health

This look at explores the tricky dating among hormonal balance and women's fitness throughout numerous physiological domain names. Drawing upon current literature and empirical findings, the research highlights the extensive impact of hormonal equilibrium on reproductive, mental, metabolic, and bone fitness consequences in ladies. Through a complete evaluation of hormonal dynamics, the take a look at elucidates the position of hormonal imbalances, consisting of polycystic ovary syndrome (PCOS), in reproductive disorders and fertility demanding situations. Furthermore, the observe underscores the affiliation among hormonal fluctuations, mainly estrogen, and mood issues such as premenstrual syndrome (PMS) and postpartum depression. Additionally, the studies examine the interaction of hormones in metabolic law, emphasizing the hyperlink among hormonal dysregulation and metabolic disturbances consisting of insulin resistance and metabolic syndrome. Moreover, the look at confirms the vital function of estrogen in preserving bone density and highlights the extended hazard of osteoporosis and fractures for the duration of menopause due to estrogen decline. Overall, this have a look at gives insights into the multifaceted dimensions of hormonal stability in girls's health, underscoring the significance of holistic approaches to healthcare.

Using EBPF to identify ransomware that use DGA DNS queries

In today's world, where the Internet has become an integral part of the functioning of government and corporate institutions, the integrity and availability of information is becoming a key issue for many organizations and individual users. The issue of protection against crypto viruses and attacks, in particular, using DGA (Domain Generation Algorithms), a method used by attackers to automatically generate domain names for client-server (Command & Control) communication in the DNS-based virus ecosystem, is particularly relevant, making it difficult to detect and block them due to the way DNS is used in modern computer networks. Given the growing number of attacks that use DGA, there is a need to develop new methods that are faster and can analyze large traffic flows in real time and provide functionality for detecting and blocking them. eBPF (Extended Berkeley Packet Filter) is a modern tool that allows you to create small programs to monitor and analyze various aspects of the system in real time, including network traffic. These programs are executed directly in the operating system kernel and/or at the network card level. In this study, we consider the possibility of using eBPF to detect DGA activity in DNS traffic. The goal is to determine the effectiveness of real-time ransomware detection. We developed a ransomware analysis lab environment where we developed eBPF-based modules, tested them, and simulated an attack. In addition, a cloud-based data analysis environment based on Splunk was set up and rules for detecting a DGA attack were developed based on this analysis. This article presents the results of developing an eBPF-based program for analyzing DNS traffic, conducting DGA attacks, and methods for detecting them. These results can be an important contribution to the development of strategies to protect against malicious attacks in the network.

TIPE KONSUMEN MARKETPLACE BLIBLI.COM MENURUT TEORI HAUSEL

The rapid development of technology in economics and business is changing the patterns and lifestyles of consumptive humans towards the use of online shopping applications, shopping services via the internet that are easy and practical of course make consumers become accustomed to and continue to use them from the many online shopping applications of course each customer has his own experience in using related applications, this research also combines neuromarketing science. The study aims to obtain customer assessments of the use of the blibli.com online application by combining neuromarketing theory according to Hausel's theory. This research uses quantitative methods with questionnaire data collection techniques and is processed through Microsoft Excel and SPSS applications. The results showed that marketplace consumers according to Hausel's Limbic Type are Connoisseurs, Hedonists and Harmonists, while Blibli's Limbic Map is balance. The domain name, Blibli.com, has a positive and significant effect on consumer preferences in the Blibli.com market place, although other variables do not have a significant effect, but overall the components have a positive effect on the total Y. The research explains that consumer preferences for the Blibli.com market place or e-commerce get a percentage of 77.7%.

PhishHunter: Detecting camouflaged IDN-based phishing attacks via Siamese neural network

Phishing is one of the significant threats to cybersecurity today, especially when attackers create Internationalized Domain Names (IDN) homographs to engage in phishing activities. IDN homograph takes advantage of some characters in different native languages in internationalized domain names that look similar to legitimate ones. Although researchers have proposed several enlightening detection methods, most of them focused on detecting typosquatting domain names. The ones focused on IDN homograph attack detection either need to enhance the generalization ability or improve detection performance caused by data imbalance. In this paper, we devised a Generative Adversarial Network with a Gradient Penalty (WGAN-GP) algorithm to solve the data imbalance problem. We transform domain names into images and calculate their similarity by Siamese neural networks. Our work can identify whether a domain name is IDN homograph or not effectively. We use the dataset generated based on Unicode tables, publicly available homograph tools, and the Internet traffic captured from the China Education Research Network backbone (CERNET) to evaluate the performance. Experimental results show that the proposed method improves the accuracy and reduces the false positive rate in detecting homograph domain names. In addition, it can also accurately identify typosquatting in phishing pages.

A Pragmatic Method to Integrate Data From Preexisting Cohort Studies Using the Clinical Data Interchange Standards Consortium (CDISC) Study Data Tabulation Model: Case Study.

In recent years, many researchers have focused on the use of legacy data, such as pooled analyses that collect and reanalyze data from multiple studies. However, the methodology for the integration of preexisting databases whose data were collected for different purposes has not been established. Previously, we developed a tool to efficiently generate Study Data Tabulation Model (SDTM) data from hypothetical clinical trial data using the Clinical Data Interchange Standards Consortium (CDISC) SDTM. This study aimed to design a practical model for integrating preexisting databases using the CDISC SDTM. Data integration was performed in three phases: (1) the confirmation of the variables, (2) SDTM mapping, and (3) the generation of the SDTM data. In phase 1, the definitions of the variables in detail were confirmed, and the data sets were converted to a vertical structure. In phase 2, the items derived from the SDTM format were set as mapping items. Three types of metadata (domain name, variable name, and test code), based on the CDISC SDTM, were embedded in the Research Electronic Data Capture (REDCap) field annotation. In phase 3, the data dictionary, including the SDTM metadata, was outputted in the Operational Data Model (ODM) format. Finally, the mapped SDTM data were generated using REDCap2SDTM version 2. SDTM data were generated as a comma-separated values file for each of the 7 domains defined in the metadata. A total of 17 items were commonly mapped to 3 databases. Because the SDTM data were set in each database correctly, we were able to integrate 3 independently preexisting databases into 1 database in the CDISC SDTM format. Our project suggests that the CDISC SDTM is useful for integrating multiple preexisting databases.

Analysis of adversarial attacks on the machine learning models of cyberprotection systems.

Modern trends in the development of cyber protection systems are associated with the widespread use of machine learning technologies to detect and prevent cyber threats. At the same time, attackers are looking for ways to evade detection by such systems, using both traditional attack methods and new ones aimed exclusively at countering artificial intelligence - adversarial attacks. Therefore, finding ways to counteract adversarial attacks is an urgent scientific and technical task. Adversarial Machine Learning (AML) is used to study them, which consists in simulating such attacks. The purpose of research is to determine ways to increase the resilience of cyber defense systems operating with the use of machine learning technologies to the impact of attacks based on AML models. The article provides examples of the application of machine learning methods in cyber protection systems. The models of adversarial attacks are described, namely: models of evasion, poisoning, functional extraction, inversion, and models of membership inference attack. Possible scenarios of their implementation are considered. Examples of adversarial attacks on machine learning models for recognizing images and text messages, detecting domain name generation algorithms, HTTP traffic malware, malicious content in e-mails, bypassing antivirus software are analyzed. Studies have shown that even without access to the algorithms of machine learning models, it is possible to bypass the cyber protection system. Therefore, to ensure the security of networks and services by means of cyber protection with artificial intelligence, it is necessary to take into account the need to counter adversarial attacks. For this purpose, it is proposed to: collect and aggregate training data for each machine learning model individually, instead of obtaining them from publicly available sources; optimize the content of event logs, taking into account the possibility of using the information contained in them to create adversarial attacks; to ensure the protection of training data and algorithms of the functioning of models; in the case of deploying cyber protection systems on critical infrastructure objects, use specially developed machine learning models that are not publicly available, which will complicate the possibility of creating a functional extraction attack.

Web3 Metaverse: State-of-the-Art and Vision

The metaverse, as a rapidly evolving socio-technical phenomenon, exhibits significant potential across diverse domains by leveraging Web3 (a.k.a. Web 3.0) technologies such as blockchain, smart contracts, and non-fungible tokens (NFTs). This survey aims to provide a comprehensive overview of the Web3 metaverse from a human-centered perspective. We (i) systematically review the development of the metaverse over the past 30 years, highlighting the balanced contributions from its core components: Web3, immersive convergence, and crowd intelligence communities, (ii) define the metaverse that integrates the Web3 community as the Web3 metaverse and propose an analysis framework from the community, society, and human layers to describe the features, missions, and relationships for each community and their overlapping sections, (iii) survey the state-of-the-art of the Web3 metaverse from a human-centered perspective, namely, the identity, field, and behavior aspects, and (iv) provide supplementary technical reviews. To the best of our knowledge, this work represents the first systematic, interdisciplinary survey on the Web3 metaverse. Specifically, we commence by discussing the potential for establishing decentralized identities (DID) utilizing mechanisms such as profile picture (PFP) NFTs, domain name NFTs, and soulbound tokens (SBTs). Subsequently, we examine land, utility, and equipment NFTs within the Web3 metaverse, highlighting interoperable and full on-chain solutions for existing centralization challenges. Lastly, we spotlight current research and practices about individual, intra-group, and inter-group behaviors within the Web3 metaverse, such as Creative Commons Zero license (CC0) NFTs, decentralized education, decentralized science (DeSci), and decentralized autonomous organizations (DAO). Furthermore, we share our insights into several promising directions, encompassing three key socio-technical facets of Web3 metaverse development.

A novel deep-learning based approach to DNS over HTTPS network traffic detection

<div align="center"><span lang="EN-US">Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a <a name="_Hlk143263730"></a>DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification.</span></div>

How Polynomial Regression Improves DeNATing

The ubiquity of Network Address Translation (NAT) and mobile hotspots that aggregate source IP addresses of connected devices to a single IP address makes it difficult for an observer in the Internet to learn anything about the internal network. The IP Identification header field of Domain Name System requests and the TCP Timestamp (TCP TS) header field of TCP SYN packets are the main features for counting devices in the internal network and association of packets to these devices, also known as DeNATing. This paper introduces a new method that relies on polynomial least-squares curve fitting for DeNATing. Evaluation of our model is performed on multiple real-world datasets containing Windows and Unix devices behind a router using NAT and a mobile hotspot. The proposed method outperforms other state-of-the-art methods for all of the used datasets on all types of devices. Successful DeNATing may help in cybersecurity, anti-fraud, and other use cases.

The Closed Resolver Project: Measuring the Deployment of Inbound Source Address Validation

Ingress filtering, commonly referred to as Source Address Validation (SAV), is a practice aimed at discarding packets with spoofed source IP addresses at the network periphery. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Outbound</i> SAV, i.e., dropping traffic with spoofed source IP addresses as it leaves its source network, has received widespread attention in operational and research communities. It is one of the most effective ways to prevent Reflection-based Distributed Denial-of-Service (DDoS) attacks. Contrariwise, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inbound</i> SAV, i.e., dropping incoming spoofed traffic at the destination network edge, has received less attention, even though it provides protection for the deploying network. In this paper, we present the results of the Closed Resolver Project, our initiative aimed at finding networks without inbound SAV and raising awareness of the issue. We perform the first Internet-wide active measurement study to enumerate networks that enforce (or not) inbound SAV. We reach open and closed Domain Name System (DNS) resolvers in tested networks and determine whether they resolve requests with spoofed source IP addresses. Our method provides unprecedented insight into <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">inbound</i> SAV deployment by network operators, revealing 49% IPv4 and 26% IPv6 Autonomous Systems (AS) that suffer from a consistent or partial absence of inbound filtering. By identifying dual-stack DNS resolvers and ASes, we further show that inbound filtering is generally deployed consistently across IPv4 and IPv6. Finally, the lack of inbound SAV exposes 2.5M IPv4 and 100K IPv6 purportedly closed DNS resolvers to many types of external attacks, including NXNSAttack, zone poisoning, or zero-day vulnerabilities in DNS software.

글로벌 마켓플레이스 오픈씨(Opensea)의 유형 구분에 따른 NFT 활용 사례 연구

This paper analyzed NFT cases for each type based on the type classification provided by Opensea the world's No. 1 NFT marketplace, and explored the possibility of NFT being used in the entertainment industry. Among the nine types in total, including Art, Photography, Music, Gaming, Sports Collectibles, Memberships, Profile Pictures (PFPs), Virtual Worlds, and Domain Names, eight of them showed potential for integration with the entertainment industry. Each type could be categorized into three directions: NFT as digital merchandise, NFT as a new method of content creation and production, and NFT as a means to expand and enhance fandom and intellectual property (IP). We hope that the various cases presented and the analysis in this study can inspire various genres of entertainment businesses, such as music, film, musicals, and performances. We also hope that ongoing research from various perspectives on technology, content, and industry markets will contribute academically and industrially.

Cybersecurity: a tool for curbing examination breaches and improvement of the quality of large-scale educational assessments

ABSTRACT This study investigated how cybersecurity can be integrated to curb examination breaches among large-scale educational assessment bodies in Nigeria. Descriptive research designed guided the study with 1067 participants drawn through multistage sampling procedure. Cybersecurity and examination breaches on large-scale educational assessment questionnaire (CEBLSEA-Q) was used for data collection. The internal consistency reliability index of the instrument was evaluated to be .78 using Cronbach alpha method. The findings of the study showed that hacking of websites, colluding via social media (WhatsApp, Facebook, Twitter etc.), sending of malicious/phishing links, using man-in-the-middle (MITM) attack are some of the cyberattacks on large-scale educational assessments. The proportion of examination breaches experienced by the various examination bodies was significantly different with WAEC being the highest, followed by NECO, JAMB, and NABTEB. Cybersecurity measures such as cloud computing, securing domain name server (DNS), encrypting computers and applications containing assessment database, cybersecurity awareness, among others could curb cyberspace aided examination breaches on educational assessments. Cybersecurity measures should be adopted by large-scale educational agencies in order to curb examination breaches that are aided through the cyberspace.

The Construction of Network Domain Name Security Access Identification System Based on Artificial Intelligence

With the popularization of the internet, cybercrime continues to increase, and traditional blacklist methods have difficulty in coping with new threats. To address this challenge, the authors propose a web domain name security access recognition algorithm based on bidirectional recurrent neural networks, aiming to more effectively combat domain name generation technology. This algorithm extracts richer semantic features at each layer through bidirectional recurrent neural networks to more accurately describe domain name features, thus effectively handling SGD problems in abnormal network traffic detection. The results show that compared with the other three algorithms, the model trained by HCA-BAGD has better performance and higher accuracy, successfully solving the problem of network security detection. This study emphasizes the importance of cybersecurity and emphasizes continuous innovation and the adoption of new technological tools to ensure the safe operation of the internet ecosystem, bringing new perspectives and solutions to research and applications in the field of cybersecurity.

Asymptotics of K-Fold Cross Validation

This paper investigates the asymptotic distribution of the K-fold cross validation error in an i.i.d. setting. As the number of observations n goes to infinity while keeping the number of folds K fixed, the K-fold cross validation error is √ n-consistent for the expected out-of-sample error and has an asymptotically normal distribution. A consistent estimate of the asymptotic variance is derived and used to construct asymptotically valid confidence intervals for the expected out-of-sample error. A hypothesis test is developed for comparing two estimators’ expected out-of-sample errors and a subsampling procedure is used to obtain critical values. Monte Carlo simulations demonstrate the asymptotic validity of our confidence intervals for the expected out-of-sample error and investigate the size and power properties of our test. In our empirical application, we use our estimator selection test to compare the out-of-sample predictive performance of OLS, Neural Networks, and Random Forests for predicting the sale price of a domain name in a GoDaddy expiry auction.