Relevant bibliographies by topics / URLs

Academic literature on the topic 'URLs'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "URLs"

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Wani, Vipin, Aniket Paul, and Kaushik Kumar. "Optimised URL Shortener using Cloud Platform." International Journal of Computer Science and Mobile Computing 10, no. 5 (May 30, 2021): 95–101. http://dx.doi.org/10.47760/ijcsmc.2021.v10i05.010.

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Short URLs have become commonplace. Short URLs, which are especially popular in social networking services, have witnessed a significant increase in their use in recent years, largely due to Twitter's restriction on message length to 140 characters. We рrоvidе a first сhаrасterizаtiоn in the usage of short URLs in this рарer. Our specific goal is to create a system architecture for short URLs and have an optimized solution of how to use them and as well as their potential impact on web performance. Our project is made while keeping in mind that the generated URL has to be one third the size of a given url. This allows the user the functionality to share the URL with multiple individuals. Our system also incorporates the idea of tracking each individual shortened URL by the usage of a short code. It gives the user the control over their generated URLs.
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Lee, Ong Vienna, Ahmad Heryanto, Mohd Faizal Ab Razak, Anis Farihan Mat Raffei, Danakorn Nincarean Eh Phon, Shahreen Kasim, and Tole Sutikno. "A malicious URLs detection system using optimization and machine learning classifiers." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 3 (March 1, 2020): 1210. http://dx.doi.org/10.11591/ijeecs.v17.i3.pp1210-1214.

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<span>The openness of the World Wide Web (Web) has become more exposed to cyber-attacks. An attacker performs the cyber-attacks on Web using malware Uniform Resource Locators (URLs) since it widely used by internet users. Therefore, a significant approach is required to detect malicious URLs and identify their nature attack. This study aims to assess the efficiency of the machine learning approach to detect and identify malicious URLs. In this study, we applied features optimization approaches by using a bio-inspired algorithm for selecting significant URL features which able to detect malicious URLs applications. By using machine learning approach with static analysis technique is used for detecting malicious URLs applications. Based on this combination as well as significant features, this paper shows promising results with higher detection accuracy. The bio-inspired algorithm: particle swarm optimization (PSO) is used to optimized URLs features. In detecting malicious URLs, it shows that naïve Bayes and support vector machine (SVM) are able to achieve high detection accuracy with rate value of 99%, using URL as a feature.</span>
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Goel, Kavita, Jay Shankar Prasad, and Saba Hilal. "Removing Duplicate URLs based on URL Normalization and Query Parameter." International Journal of Engineering & Technology 7, no. 3.12 (July 20, 2018): 361. http://dx.doi.org/10.14419/ijet.v7i3.12.16107.

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Searching is the important requirement of the web user and results is based on crawler. Users rely on search engines to get desired information in various forms text, images, sound, Video. Search engine gives information on the basis of indexed database and this database is created by the URLs through crawler. Some URLs directly or indirectly leads to same page. Crawling and indexing similar contents URLs implies wastage of resources. Crawler gives such results because of bad crawling algorithm, poor quality Ranking algorithm or low level user experience. The challenge is to remove duplicate results, near duplicate document detection and elimination to improve the performance of any search engine. This paper proposes a Web Crawler which performs crawling in particular category to remove irrelevant URL and implements URL normalization for removing duplicate URLs within particular category. Results are analyzed on the basis of total URL Fetched, Duplicate URLs, and Query execution time.
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Rajesh, M., R. Abhilash, and R. Praveen Kumar. "URL ATTACKS: Classification of URLs via Analysis and Learning." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 980. http://dx.doi.org/10.11591/ijece.v6i3.7208.

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Social Networks such as Twitter, Facebook play a remarkable growth in recent years. The ratio of tweets or messages in the form of URLs increases day by day. As the number of URL increases, the probability of fabrication also gets increased using their HTML content as well as by the usage of tiny URLs. It is important to classify the URLs by means of some modern techniques. Conditional redirection method is used here by which the URLs get classified and also the target page that the user needs is achieved. Learning methods also introduced to differentiate the URLs and there by the fabrication is not possible. Also the classifiers will efficiently detect the suspicious URLs using link analysis algorithm.
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Rajesh, M., R. Abhilash, and R. Praveen Kumar. "URL ATTACKS: Classification of URLs via Analysis and Learning." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 3 (June 1, 2016): 980. http://dx.doi.org/10.11591/ijece.v6i3.pp980-985.

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Social Networks such as Twitter, Facebook play a remarkable growth in recent years. The ratio of tweets or messages in the form of URLs increases day by day. As the number of URL increases, the probability of fabrication also gets increased using their HTML content as well as by the usage of tiny URLs. It is important to classify the URLs by means of some modern techniques. Conditional redirection method is used here by which the URLs get classified and also the target page that the user needs is achieved. Learning methods also introduced to differentiate the URLs and there by the fabrication is not possible. Also the classifiers will efficiently detect the suspicious URLs using link analysis algorithm.
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Khan, Firoz, Jinesh Ahamed, Seifedine Kadry, and Lakshmana Kumar Ramasamy. "Detecting malicious URLs using binary classification through adaboost algorithm." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 1 (February 1, 2020): 997. http://dx.doi.org/10.11591/ijece.v10i1.pp997-1005.

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Malicious Uniform Resource Locator (URL) is a frequent and severe menace to cybersecurity. Malicious URLs are used to extract unsolicited information and trick inexperienced end users as a sufferer of scams and create losses of billions of money each year. It is crucial to identify and appropriately respond to such URLs. Usually, this discovery is made by the practice and use of blacklists in the cyber world. However, blacklists cannot be exhaustive, and cannot recognize zero-day malicious URLs. So to increase the observation of malicious URL indicators, machine learning procedures should be incorporated. This study aims to discuss the exposure of malicious URLs as a binary classification problem using machine learning through an AdaBoost algorithm.
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Chen, Zuguo, Yanglong Liu, Chaoyang Chen, Ming Lu, and Xuzhuo Zhang. "Malicious URL Detection Based on Improved Multilayer Recurrent Convolutional Neural Network Model." Security and Communication Networks 2021 (May 26, 2021): 1–13. http://dx.doi.org/10.1155/2021/9994127.

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The traditional malicious uniform resource locator (URL) detection method excessively relies on the matching rules formulated by the network security personnel, which is hard to fully express the text information of the URL. Thus, an improved multilayer recurrent convolutional neural network model based on the YOLO algorithm is proposed to detect malicious URL in this paper. First, single characters are mapped to dense vectors using word embedding, and the dense vectors are participated in the training process of the whole model according to the structural characteristics of the URL in the method. Then, the CSPDarknet neural network model based on the improved YOLO algorithm is proposed to extract features of the URL. Finally, the extracted features are used to evaluate malicious URL by the bidirectional LSTM recurrent neural network algorithm. In order to verify the validity of the algorithm, a total of 200,000 URLs are collected, including 100,000 normal URLs labeled “good” and 100,000 malicious URLs labeled “bad”. The experimental results show that the method detects malicious URLs more quickly and effectively and has high accuracy, high recall rate, and high accuracy compared with Text-RCNN, BRNN, and other models.
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Habibzadeh, P. "Decay of References to Web sites in Articles Published in General Medical Journals: Mainstream vs Small Journals." Applied Clinical Informatics 04, no. 04 (2013): 455–64. http://dx.doi.org/10.4338/aci-2013-07-ra-0055.

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SummaryBackground: Over the last decade, Web sites (URLs) have been increasingly cited in scientific articles. However, the contents of the page of interest may change over the time.Objective: To investigate the trend of citation to URLs in five general medical journals since January 2006 to June 2013 and to compare the trends in mainstream journals with small journals.Methods: References of all original articles and review articles published between January 2006 and June 2013 in three regional journals – Archives of Iranian Medicine (AIM), Eastern Mediterranean Health Journal (EMHJ), and Journal of Postgraduate Medical Institute (JPMI) – and two mainstream journals – The Lancet and British Medical Journal (BMJ) – were reviewed. The references were checked to determine the frequency of citation to URLs as well as the rate of accessibility of the URLs cited.Results: A total of 2822 articles was studied. Since January 2006 onward, the number of citations to URLs increased in the journals (doubling time ranged from 4.2 years in EMHJ to 13.9 years in AIM). Overall, the percentage of articles citing at least one URL has increased from 24% in 2006 to 48.5% in 2013. Accessibility to URLs decayed as the references got old (half life ranged from 2.2 years in EMHJ to 5.3 years in BMJ). The ratio of citation to URLs in the studied mainstream journals, as well as the ratio of URLs accessible were significantly (p<0.001) higher than the small medical journals.Conclusion: URLs are increasingly cited, but their contents decay with time. The trend of citing and decaying URLs are different in mainstream journals compared to small medical journals. Decay of URL contents would jeopardize the accuracy of the references and thus, the body of evidence. One way to tackle this important obstacle is to archive URLs permanently.Citation: Habibzadeh P. Decay of references to web sites in articles published in general medical journals: Mainstream vs small journals. Appl Clin Inf 2013; 4: 455–464http://dx.doi.org/10.4338/ACI-2013-07-RA-0055
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Sife, Alfred Said, and Edda Tandi Lwoga. "Retrieving vanished Web references in health science journals in East Africa." Information and Learning Science 118, no. 7/8 (July 10, 2017): 385–92. http://dx.doi.org/10.1108/ils-04-2017-0030.

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Purpose This study aims to examine the availability and persistence of universal resource locators (URLs) cited in scholarly articles published in selected health journals based in East Africa. Design/methodology/approach Four health sciences online journals in East Africa were selected for this study. In this study, all Web citations in the selected journal articles covering the 2001-2015 period were extracted. This study explored the number of URLs used as citations, determined the rate of URLs’ loss, identified error messages associated with inaccessible URLs, identified the top domain levels of decayed URLs, calculated the half-life of the Web citations and determined the proportion of recovered URL citations through the Internet Wayback Machine. Findings In total, 822 articles were published between 2001 and 2015. There were in total 17,609 citations of which, only 574 (3.3 per cent) were Web citations. The findings show that 253 (44.1 per cent) Web citations were inaccessible and the “404 File Not Found” error message was the most (88.9 per cent) encountered. Top-level domains with country endings had the most (23.7 per cent) missing URLs. The average half-life for the URLs cited in journal articles was 10.5 years. Only 36 (6.3 per cent) Web references were recovered through the Wayback Machine. Originality/value This is a comprehensive study of East African health sciences online journals that provides findings that raises questions as to whether URLs should continue to be included as part of bibliographic details in the lists of references. It also calls for concerted efforts from various actors in overcoming the problem of URL decay.
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Niveditha, B., and Mallinath Kumbar. "A Study of Availability and Recovery of URLs in Library and Information Science Scholarly Journals." Asian Journal of Information Science and Technology 10, no. 1 (May 5, 2020): 51–61. http://dx.doi.org/10.51983/ajist-2020.10.1.297.

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The present study examines the availability and recovery of web references cited in scholarly journals selected based on their high impact factor published between 2008 and 2017. A PHP script was used to crawl the Uniform Resource Locators (URL) collected from the references. A total of 5720 articles were downloaded and 237418 references were extracted. A total of 33512 URLs were checked for their availability. Further the lexical features of URLs like file extension, path depth, character length and top-level domain was determined. The research findings indicated that out of 33512 web references, 20218 contained URLs, DOIs were found in 12799 references and 495 references contained arXiv or WOS identifier. It was found that 29760 URLs were accessible and the remaining 3752 URLs were missing. Most errors were due to HTTP 404 error code (Not found error). The study also tried to recover the inaccessible URLs through Time Travel. Almost 60.55% of inaccessible URLs were archived in various web archives. The findings of the study will be helpful to authors, publishers, and editorial staff to ensure that web references will be accessible in future.
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Dissertations / Theses on the topic "URLs"

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Ortega, San Martin Luis. "Recommended URLs." Revista de Química, 2012. http://repositorio.pucp.edu.pe/index/handle/123456789/99737.

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Aquí encontrará un grupo de páginas web que podrían ser de su interés. La primera es de utilidad en el área de materiales, ya sea en docencia o investigación.  La segunda consiste en un blog de divulgación científica que seguro le descubrirá un lado de la química que desconoce. Finalmente, se muestra una página web que contiene vídeos que podríanserle útiles en el salón de su clase.
Aquí encontrará un grupo de páginas web que podrían ser de su interés. La primera es de utilidad en el área de materiales, ya sea en docencia o investigación.  La segunda consiste en un blog de divulgación científica que seguro le descubrirá un lado de la química que desconoce. Finalmente, se muestra una página web que contiene vídeos que podríanserle útiles en el salón de su clase.
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Zapata, Ramirez Victor. "Recommended URLs." Revista de Química, 2013. http://repositorio.pucp.edu.pe/index/handle/123456789/99893.

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Periodic Table of Videos (http://www.periodicvideos.com)- MIT Open Courseware (http://www.ocw.mit.edu)- Cambridge Chemistry Challenge (www.c3l6.org)
- Periodic Table of Videos (http://www.periodicvideos.com)- MIT Open Courseware (http://www.ocw.mit.edu)- Cambridge Chemistry Challenge (www.c3l6.org)
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Ma, Justin Tung. "Learning to detect malicious URLs." Diss., [La Jolla] : University of California, San Diego, 2010. http://wwwlib.umi.com/cr/ucsd/fullcit?p3404292.

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Thesis (Ph. D.)--University of California, San Diego, 2010.
Title from first page of PDF file (viewed June 10, 2010). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (leaves 119-124).
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Cheng, Aidan. "Using Machine Learning to Detect Malicious URLs." Scholarship @ Claremont, 2017. http://scholarship.claremont.edu/cmc_theses/1567.

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There is a need for better predictive model that reduces the number of malicious URLs being sent through emails. This system should learn from existing metadata about URLs. The ideal solution for this problem would be able to learn from its predictions. For example, if it predicts a URL to be malicious, and that URL is deemed safe by the sandboxing environment, the predictor should refine its model to account for this data. The problem, then, is to construct a model with these characteristics that can make these predictions for the vast number of URLs being processed. Given that the current system does not employ machine learning methods, we intend to investigate multiple such models and summarize which of those might be worth pursuing on a large scale.
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Silva, Eduina Da. "The influence of URLs on customer perception and reaction." Thesis, University of Macau, 2005. http://umaclib3.umac.mo/record=b1636667.

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Bezerra, Maria Azevedo. "Uma investigação do uso de características na tetecção de URLs." Universidade Federal do Amazonas, 2015. http://tede.ufam.edu.br/handle/tede/4740.

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Malicious URLs have become a channel for criminal activities on the Internet, such as spam and phishing. Current solutions for validation and verification of malicious URLs are considered or are believed to be accurate, with well-adjusted results. However, is it really possible or feasible to obtain 100% of accuracy in these solutions? This work describes a simple and direct investigation of features, bases and URL formats, aiming to show that the results of validation and verification URLs are highly dependent on certain aspects/factors. The idea is to extract URL features (lexical, DNS and others) for obtain the maximum information from the URLs and employ machine learning algorithms to question their influence throughout the process. In order to prove this idea, were created four hypotheses that showed that it is possible to disagree with the results of several studies from the literature.
URLs maliciosas tornaram-se um canal para atividades criminosas na Internet, como spam e phishing. As atuais soluções para validação e verificação de URLs maliciosas se consideram ou são consideradas precisas, com resultados bem ajustados. Contudo, será que realmente é possível ou factível se obter percentuais beirando 100% de precisão nessas soluções? Neste sentido, esta dissertação descreve uma simples e direta investigação de características, bases e formatos de URLs, visando mostrar que os resultados de validação e verificação de URLs são bastante dependentes de certos aspectos/fatores. A ideia é extrair características (léxicas, DNS e outras) que permitam obter o máximo de informação das URLs e empregar algoritmos de aprendizagem de máquina para questionar a influência dessas características em todo o processo. Como forma de provar essa ideia, foram elaboramos quatro hipóteses, que ao final no trabalho, mostraram que é possível discordar do resultado de vários trabalhos já existentes na literatura.
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Egan, Shaun Peter. "A framework for high speed lexical classification of malicious URLs." Thesis, Rhodes University, 2014. http://hdl.handle.net/10962/d1011933.

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Phishing attacks employ social engineering to target end-users, with the goal of stealing identifying or sensitive information. This information is used in activities such as identity theft or financial fraud. During a phishing campaign, attackers distribute URLs which; along with false information, point to fraudulent resources in an attempt to deceive users into requesting the resource. These URLs are made obscure through the use of several techniques which make automated detection difficult. Current methods used to detect malicious URLs face multiple problems which attackers use to their advantage. These problems include: the time required to react to new attacks; shifts in trends in URL obfuscation and usability problems caused by the latency incurred by the lookups required by these approaches. A new method of identifying malicious URLs using Artificial Neural Networks (ANNs) has been shown to be effective by several authors. The simple method of classification performed by ANNs result in very high classification speeds with little impact on usability. Samples used for the training, validation and testing of these ANNs are gathered from Phishtank and Open Directory. Words selected from the different sections of the samples are used to create a `Bag-of-Words (BOW)' which is used as a binary input vector indicating the presence of a word for a given sample. Twenty additional features which measure lexical attributes of the sample are used to increase classification accuracy. A framework that is capable of generating these classifiers in an automated fashion is implemented. These classifiers are automatically stored on a remote update distribution service which has been built to supply updates to classifier implementations. An example browser plugin is created and uses ANNs provided by this service. It is both capable of classifying URLs requested by a user in real time and is able to block these requests. The framework is tested in terms of training time and classification accuracy. Classification speed and the effectiveness of compression algorithms on the data required to distribute updates is tested. It is concluded that it is possible to generate these ANNs in a frequent fashion, and in a method that is small enough to distribute easily. It is also shown that classifications are made at high-speed with high-accuracy, resulting in little impact on usability.
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Henrique, Wallace Favoreto. "Verificação de unicidade de URLs em coletores de páginas Web." Universidade Federal de Minas Gerais, 2011. http://hdl.handle.net/1843/SLSS-8GQJNA.

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One of the main difficulties in the development of a web crawler is in the component for verifying URL uniqueness, since complex data structures are required to ensure that the identification of URLs still not collected will be performed effectively and efficiently.If the component for verifying URL uniqueness is not effective and efficient, the performance of the other web crawler components will be affected.In this work we present a new algorithm for verifying URLs uniqueness, referred to as VEUNI (VErificador de UNIcidade de URLs).The algorithm VEUNI was compared with the best known algorithm in the literature, which was considered a baseline in the experiments.The comparative study between the algorithm VEUNI and the baseline was performed through a simulation of a collection of approximately 350 million pages, using a reference collection called ClueWeb09.Experimental results show that the proposed algorithm is an alternative that can be successfully used in web crawlers designed to be scalable to the entire Web.
Uma das principais dificuldades existentes no desenvolvimento de um coletor de páginas web está no componente verificador de unicidade de URLs, pois estruturas de dados complexas são exigidas para garantir que a identificação das URLs ainda não coletadas seja feita de forma eficaz e eficiente. Caso o verificador de unicidade de URLs não ofereça eficácia e eficiência, os outros componentes do coletor serão prejudicados. Neste trabalho, apresentamos um novo algoritmo para verificar unicidade de URLs chamado VEUNI (VErificador de UNIcidade de URLs).O algoritmo VEUNI foi comparado com o melhor algoritmo conhecido na literatura, o qual foi considerado um baseline nos experimentos. O estudo comparativo entre o algoritmo VEUNI e o algoritmo baseline foi realizado por meio de uma simulação de uma coleta de aproximadamente 350 milhões de páginas, utilizando uma coleção de referência chamada ClueWeb09. Os resultados experimentais mostram que o algoritmo proposto é uma alternativa que pode ser utilizada com êxito em coletores de páginas que visam ser escaláveis para toda a Web.
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Li, Sirui. "A Study of Corrosion Monitoring Techniques Used in URLs for Metals." Scholar Commons, 2017. http://scholarcommons.usf.edu/etd/6624.

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With the increasing use of fission-type nuclear power generation, particularly high-levels radioactive nuclear waste are generated, so the safe use of nuclear energy requires proper disposal of high-level radioactive nuclear waste. The selected treatment method is deep geological disposal. Therefore, underground research laboratory (URL) to prepare for deep geological disposal will also be carried out. Corrosion of metallic materials, which are closely related to the safety of URL, is the focus of this research project. This study selected monitoring techniques for URL and developed a rough monitoring scheme for temperature and resistivity in URL. In this study, corrosion-temperature and corrosion-resistivity monitoring experiments were carried out in different bentonite samples to simulate the experiments in URL. The results show that the self-compensating high-precision inductance corrosion monitoring system and multifunction soil corrosion rate measurer proved to be a good system for monitoring the corrosion-temperature and corrosion-resistivity of metals. However, the life span limitation makes them unable to meet the requirements of URL. The results also show that the corrosion rate of metal in bentonite is positively correlated with temperature. The existing electrochemical probes are suitable for monitoring the corrosion rate, but not suitable for soil corrosion rate monitoring.
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Al-Janabi, Mohammed Fadhil Zamil. "Detection of suspicious URLs in online social networks using supervised machine learning algorithms." Thesis, Keele University, 2018. http://eprints.keele.ac.uk/5581/.

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This thesis proposes the use of several supervised machine learning classification models that were built to detect the distribution of malicious content in OSNs. The main focus was on ensemble learning algorithms such as Random Forest, gradient boosting trees, extra trees, and XGBoost. Features were used to identify social network posts that contain malicious URLs derived from several sources, such as domain WHOIS record, web page content, URL lexical and redirection data, and Twitter metadata. The thesis describes a systematic analysis of the hyper-parameters of tree-based models. The impact of key parameters, such as the number of trees, depth of trees and minimum size of leaf nodes on classification performance, was assessed. The results show that controlling the complexity of Random Forest classifiers applied to social media spam is essential to avoid overfitting and optimise performance. The model complexity could be reduced by removing uninformative features, as the complexity they add to the model is greater than the advantages they give to the model to make decisions. Moreover, model-combining methods were tested, which are the voting and stacking methods. Both show advantages and disadvantages; however, in general, they appear to provide a statistically significant improvement in comparison to the highest singular model. The critical benefit of applying the stacking method to automate the model selection process is that it is effective in giving more weight to more topperforming models and less affected by weak ones. Finally, 'SuspectRate', an online malicious URL detection system, was built to offer a service to give a suspicious probability of tweets with attached URLs. A key feature of this system is that it can dynamically retrain and expand current models.
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Books on the topic "URLs"

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Ackermann, Ernest C. Learning to use the Internet and World Wide Web with revitalized URLs. Wilsonville, OR: Franklin, Beedle & Associates, 2004.

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Best practices in policies and procedures: Includes table of contents examples and policy/procedure URLs. Westerville, Ohio: Process Improvement Publishing, 2001.

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Nikić, Momir. Urla. Beograd: Geopoetika, 2000.

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Ḥijāzī, Aḥmad ʻAbd al-Muʻṭī. Urās. [Cairo]: Akhbār al-Yawm, 1989.

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Mićović, Milovan. Bademi, urbs. Novi Sad: Prometej, 1998.

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URSS-Russia. Milano: Rizzoli, 1985.

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Jovanović, Miodrag. Uros Predic. Novi Sad: Matiza srpska, 1998.

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José Carlos de Figueiredo Ferraz. Urbs nostra. São Paulo, SP, Brasil: Edusp, 1991.

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Urbs: Roman. Paris]: Le Tripode, 2013.

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Moretti, Massimo. L' urlo. Venezia: Marsilio, 1993.

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Book chapters on the topic "URLs"

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Kern, Thorsten A. "URLs." In Engineering Haptic Devices, 437–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-88248-0_15.

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Rocher, Graeme, and Jeff Brown. "Mapping URLs." In The Definitive Guide to Grails, 143–58. Berkeley, CA: Apress, 2009. http://dx.doi.org/10.1007/978-1-4302-0871-6_6.

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Brown, Jeff Scott, and Graeme Rocher. "Mapping URLs." In The Definitive Guide to Grails 2, 139–53. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-4378-6_6.

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Alchin, Marty. "URLs and Views." In Pro Django, 83–106. Berkeley, CA: Apress, 2013. http://dx.doi.org/10.1007/978-1-4302-5810-0_4.

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Pusch, Roland. "Underground Laboratories (URLs)." In Geological Storage of Highly Radioactive Waste, 275–319. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77333-7_6.

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Good, Nathan A. "URLs and Paths." In Regular Expression Recipes for Windows Developers, 91–125. Berkeley, CA: Apress, 2005. http://dx.doi.org/10.1007/978-1-4302-0028-4_2.

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Sanderson, Steven. "URLs and Routing." In Pro ASP.NET MVC 2 Framework, 235–81. Berkeley, CA: Apress, 2010. http://dx.doi.org/10.1007/978-1-4302-2887-5_8.

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Watson, Brent. "Mapping URLs to Methods." In Stripes by Example, 23–27. Berkeley, CA: Apress, 2015. http://dx.doi.org/10.1007/978-1-4842-0980-6_4.

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Freeman, Adam, and Steven Sanderson. "URLs, Routing, and Areas." In Pro ASP.NET MVC 3 Framework, 325–83. Berkeley, CA: Apress, 2011. http://dx.doi.org/10.1007/978-1-4302-3405-0_11.

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Rubio, Daniel. "Django Urls and Views." In Beginning Django, 31–72. Berkeley, CA: Apress, 2017. http://dx.doi.org/10.1007/978-1-4842-2787-9_2.

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Conference papers on the topic "URLs"

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Soon, Lay-Ki, and Sang Ho Lee. "Identifying Equivalent URLs Using URL Signatures." In 2008 IEEE International Conference on Signal Image Technology and Internet Based Systems (SITIS). IEEE, 2008. http://dx.doi.org/10.1109/sitis.2008.21.

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Kannan, Natarajan, and Toufeeq Hussain. "Live URLs." In the 15th international conference. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1135777.1135924.

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Han, Xiaogang, Wenjun Zhou, Xing Jiang, Hengjie Song, Ming Zhong, and Toyoaki Nishida. "Utilizing URLs Position to Estimate Intrinsic Query-URL Relevance." In 2013 IEEE International Conference on Data Mining (ICDM). IEEE, 2013. http://dx.doi.org/10.1109/icdm.2013.20.

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Hinne, Max, Wessel Kraaij, Stephan Raaijmakers, Suzan Verberne, Theo van der Weide, and Maarten van der Heijden. "Annotation of URLs." In the 32nd international ACM SIGIR conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1571941.1572051.

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Ma, Justin, Lawrence K. Saul, Stefan Savage, and Geoffrey M. Voelker. "Identifying suspicious URLs." In the 26th Annual International Conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1553374.1553462.

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Gonçalves, Diego Luiz Nunes, and Dionsio Machado Leite Filho. "URLYZER: sistema de identificação de URLs maliciosas utilizando IA como suporte à tomada de decisão." In Anais Estendidos do Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/sbrc_estendido.2021.17180.

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Neste trabalho é apresentado o URLYZER, um sistema para identificação de URLs maliciosas. Como a maioria dos recursos na Web é acessado via URL, a mesma pode ser modificada ou fraudada para uso indevido. Com isso, o URLYZER visa analisar a URL, a partir da extração das características léxicas, e utilizando um classificador Random Forest determinar se uma determinada URL é benigna ou maligna. O classificador obteve resultados satisfatórios com uma acurácia de 86%, 79% de precisão, 98% de revocação e 88% em seu F1-score.
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Sendi, Razan. "The Classification and Detection of Malicious URLs Using a Machine Learning Based Approach." In International Petroleum Technology Conference. IPTC, 2022. http://dx.doi.org/10.2523/iptc-22661-ms.

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Abstract The world wide web has become the most cardinal and salient source of information, for which in today's technological era, web pages are incessantly being utilized to broadcast pernicious activity over the web. Hence, the perpetual technological advancements have enthralled cybercriminals to easily exploit the web environment by embedding malicious code on these web pages which permits oblivious and innocent users into becoming victims by visiting these harmful pages with a click of a button. Such detrimental and malicious web pages are created by cyber-attackers to promote and host viruses and exploit frauds, attacks, and scams. Consequently, within an organization, emails containing Uniform Resource Locators (URLs) cause a considerable amount of risk towards an organization as these links become instrumental in terms of giving systematic control to the cyber-attackers. There is a critical need of filtering employees’ incoming emails in accordance with the maliciousness of the URL to protect the web environment users from such threats. Accordingly, the premier objective of this proposed paper is to constructively classify and detect malicious URLs while utilizing a machine learning based approach. This will lead towards sufficiently decreasing the number of URL-based attacks that may target organizations via email, protecting the users of the web from phishing, scam, defacement, and malware web attacks. Hence, the conceptualization behind the proposed AI framework is to construct a multi-classification model to effectively thwart and protect organizations from detrimental and malicious web-based attacks by classifying a URL as either a defacement, benign, malware, spam, or a phishing-based URL. The general framework for the proposed malicious URL detection system is illustratively displayed in Figure 1.
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Pham, Tuan Dung, Thi Thanh Thuy Pham, Sy Tuong Hoang, and Viet Cuong Ta. "Exploring Efficiency of GAN-based Generated URLs for Phishing URL Detection." In 2021 International Conference on Multimedia Analysis and Pattern Recognition (MAPR). IEEE, 2021. http://dx.doi.org/10.1109/mapr53640.2021.9585287.

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Aldwairi, Monther, and Rami Alsalman. "MALURLs: Malicious URLs Classification System." In Annual International Conference on Information Technology and Applications & Annual International Conference on Control, Automation and Robotics. Global Science and Technology Forum, 2011. http://dx.doi.org/10.5176/978-981-08-8113-9_ita29.

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Bai, Xiao, B. Barla Cambazoglu, and Flavio P. Junqueira. "Discovering URLs through user feedback." In the 20th ACM international conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2063576.2063592.

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Reports on the topic "URLs"

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Vaha-Sipila, A. URLs for Telephone Calls. RFC Editor, April 2000. http://dx.doi.org/10.17487/rfc2806.

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Mealling, M., and R. Denenberg, eds. Report from the Joint W3C/IETF URI Planning Interest Group: Uniform Resource Identifiers (URIs), URLs, and Uniform Resource Names (URNs): Clarifications and Recommendations. RFC Editor, August 2002. http://dx.doi.org/10.17487/rfc3305.

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Richardson, M., J. Latour, and H. Habibi Gharakheili. Loading Manufacturer Usage Description (MUD) URLs from QR Codes. RFC Editor, May 2022. http://dx.doi.org/10.17487/rfc9238.

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MacDonald, Stuart, Connor Rees, and Joost S. Remove, Impede, Disrupt, Redirect: Understanding & Combating Pro-Islamic State Use of File-Sharing Platforms. RESOLVE Network, April 2022. http://dx.doi.org/10.37805/ogrr2022.1.

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In the face of content takedown and account suspensions on the biggest social media platforms, terrorist groups and their supporters have resorted to the use of file-sharing sites to ensure stable access to their propaganda. Amongst those to have employed this strategy are supporters of the so-called Islamic State (IS). Yet, while studies have repeatedly highlighted the key role that file-sharing platforms play in the dissemination of IS propaganda, there has been little investigation of the strategic considerations that may influence the choice of file-sharing sites from the many available. To address this, this report uses data gathered from 13 public IS Telegram channels over a 45-day period in July - September 2021 to assess three possible strategic considerations: the features offered by different file-sharing sites (such as data storage capacity, maximum upload size, and password file protection); a platform’s enforcement activity; and the ability to generate large banks of URLs quickly and conveniently. Based on these findings, the report proposes a four-pronged strategy to combat the exploitation of file-sharing sites by supporters of IS and other terrorist groups: remove terrorist content at the point of upload; impede the automated generation and dissemination of banks of URLs; disrupt the posting of these URLs on other platforms; and redirect users to other content and support services.
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Neufeld, G., and J. Baer. The Use of URLs as Meta-Syntax for Core Mail List Commands and their Transport through Message Header Fields. RFC Editor, July 1998. http://dx.doi.org/10.17487/rfc2369.

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Macdonald, Stuart, Kamil Yilmaz, Chamin Herath, J. M. Berger, Suraj Lakhani, Lella Nouri, and Maura Conway. The European Far-Right Online: An Exploratory Twitter Outlink Analysis of German & French Far-Right Online Ecosystems. RESOLVE Network, May 2022. http://dx.doi.org/10.37805/remve2022.2.

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Seeking to explore the nature of European far-right online ecosystems, this research report examines the outlinking activity of identified pro-far-right users among the followers of the official Twitter accounts of two prominent far-right European political parties, Germany’s Alternative für Deutschland (AfD) and France’s Rassemblement National (RN). Employing a three-layered analysis, the report explores not just the top-level domains outlinked to by its sample of AfD and RN Twitter followers but combines this with analysis of the technical specifications of the content types outlinked to and treatment of the socio-political nature of the content arrived at by clicking on the most tweeted URLs. This results in the provision of a more thorough and cohesive view of this online ecosystem than contained in other similar studies.
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Daigle, L., P. Deutsch, B. Heelan, C. Alpaugh, and M. Maclachlan. Uniform Resource Agents (URAs). RFC Editor, October 1996. http://dx.doi.org/10.17487/rfc2016.

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Saint-Andre, P., and J. Klensin. Uniform Resource Names (URNs). RFC Editor, April 2017. http://dx.doi.org/10.17487/rfc8141.

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Mavrakis, D., H. Layec, and K. Kartmann. VEMMI URL Specification. RFC Editor, March 1997. http://dx.doi.org/10.17487/rfc2122.

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Newman, C. IMAP URL Scheme. RFC Editor, September 1997. http://dx.doi.org/10.17487/rfc2192.

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