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1
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
<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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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.
Повний текст джерелаАнотація:
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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Aljebreen, Abdullah, Weiyi Meng, and Eduard Dragut. "Segmentation of Tweets with URLs and its Applications to Sentiment Analysis." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 14 (May 18, 2021): 12480–88. http://dx.doi.org/10.1609/aaai.v35i14.17480.
Повний текст джерелаАнотація:
An important means for disseminating information in social media platforms is by including URLs that point to external sources in user posts. In Twitter, we estimate that about 21% of the daily stream of English-language tweets contain URLs. We notice that NLP tools make little attempt at understanding the relationship between the content of the URL and the text surrounding it in a tweet. In this work, we study the structure of tweets with URLs relative to the content of the Web documents pointed to by the URLs. We identify several segments classes that may appear in a tweet with URLs, such as the title of a Web page and the user's original content. Our goals in this paper are: introduce, define, and analyze the segmentation problem of tweets with URLs, develop an effective algorithm to solve it, and show that our solution can benefit sentiment analysis on Twitter. We also show that the problem is an instance of the block edit distance problem, and thus an NP-hard problem.
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Vyawhare, Chaitanya R., Reshma Y. Totare, Prashant S. Sonawane, and Purva B. Deshmukh. "Machine Learning System for Malicious Website Detection: A Literature Review." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 56–61. http://dx.doi.org/10.22214/ijraset.2022.42050.
Повний текст джерелаАнотація:
Abstract: Today the most important concern in the field of cyber security is finding the serious problems that make loss in secure information. It is mainly due to malicious URLs. Malicious URLs are generated daily. This URLs are having a short life span. Various techniques are used by researchers for detecting such threats in a timely manner. Blacklist method is famous among them. Researchers uses this blacklist method for easily identifying the harmful URLs. They are very simple and easy method. Due to their simplicity they are used as a traditional method for detecting such URLs. But this method suffers from many problems. The lack of ability in detecting newly generated malicious URLs is one of the main drawbacks of Blacklist method. Heuristic approach is also used for identifying some common attacks. It is an advanced technique of Blacklist method. But this method cannot be used for all type of attacks. So this method is used very shortly. For a good experience, the researchers introduce machine learning techniques. Machine Learning techniques go through several phases and detect the malicious URLs in an accurate manner. This method also gives the details about the false positive rate. This review paper studies the different phases such as feature extraction phase and feature representation phase of machine learning techniques for detecting malicious URLs. Different machine learning algorithms used for such detection is also discuss in this paper. And also gives a better understanding about the advantage of using machine learning over other techniques for detecting malicious URLs and problems it suffers. Keywords: Blacklist, Cyber Security, Malicious URL
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Punj, Deepika, and Ashutosh Dixit. "Design of a Migrating Crawler Based on a Novel URL Scheduling Mechanism using AHP." International Journal of Rough Sets and Data Analysis 4, no. 1 (January 2017): 95–110. http://dx.doi.org/10.4018/ijrsda.2017010106.
Повний текст джерелаАнотація:
In order to manage the vast information available on web, crawler plays a significant role. The working of crawler should be optimized to get maximum and unique information from the World Wide Web. In this paper, architecture of migrating crawler is proposed which is based on URL ordering, URL scheduling and document redundancy elimination mechanism. The proposed ordering technique is based on URL structure, which plays a crucial role in utilizing the web efficiently. Scheduling ensures that URLs should go to optimum agent for downloading. To ensure this, characteristics of both agents and URLs are taken into consideration for scheduling. Duplicate documents are also removed to make the database unique. To reduce matching time, document matching is made on the basis of their Meta information only. The agents of proposed migrating crawler work more efficiently than traditional single crawler by providing ordering and scheduling of URLs.
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Ambilwade, Miss Priyanka Vasant. "Detecting Malicious URLs using R-CNN and Cloud." International Journal for Research in Applied Science and Engineering Technology 9, no. VI (June 20, 2021): 1443–49. http://dx.doi.org/10.22214/ijraset.2021.35284.
Повний текст джерелаАнотація:
In today’s information age as use of websites, mobile apps and all forms of information sharing forms have increased which gave rise to malicious URL forms. These malicious URLs are forwarded and users attention is diverted from the main course for what he is searching to other non-necessary and harmful content, thus wasting a lot of time and money. Theses malicious URLs have given rise to authentication thefts, money thefts and bullying of a user who falls in to a trap set by hackers by accessing these URLs. To resolve and find a solution to this kind of menace there is need to detect and prevent users from accessing these URLs. So, while studying various techniques put forward by various authors in different research papers, we found a few techniques quite interesting and useful. The first is detecting malicious URLs using CNN and GRU. The second is where a text mining technique is proposed using Natural Language Processing (NLP) which can be used for classification. The third is a combination of CNN and NLP. By studying them we came to understand that there should be a combination of both NLP and CNN together to implement a successful malicious URL detection system. So, in our paper we are proposing a fusion of R-CNN, NLP and Cloud together. The main work in our paper is to collect malicious and healthy URL which will be done using internet and multiple sources and combined as one dataset. Thus, we will use Google cloud to create a blacklisted URL database of our own and not depend upon multiple sources internet for them. In our system first we will create a blacklist database on cloud and then apply classification on it using NLP and machine learning algorithm SVM. The second step will be to use same URL dataset to train a R-CNN AI algorithm and get an output in form of malicious identified URLs. Then in the final phase we will compare the final results from SVM and R-CNN and analyse which one is efficient and highs and lows of the technique.
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Peng, Yongfang, Shengwei Tian, Long Yu, Yalong Lv, and Ruijin Wang. "A Joint Approach to Detect Malicious URL Based on Attention Mechanism." International Journal of Computational Intelligence and Applications 18, no. 03 (September 2019): 1950021. http://dx.doi.org/10.1142/s1469026819500214.
Повний текст джерелаАнотація:
To improve the accuracy and automation of malware Uniform Resource Locator (URL) recognition, a joint approach of Convolutional neural network (CNN) and Long-short term memory (LSTM) based on the Attention mechanism (JCLA) is proposed to identify and detect malicious URL. Firstly, the URL features including texture information, lexical information and host information are extracted and filtered, and pre-processed with encode. Then, the feature matrix more relevant to the output are chose according to the weight of the attention mechanism and input to the constructed parallel processing model called CNN_LSTM, combinating CNN and LSTM to get local features. Next, the extracted local features are merged to calculate the global features of the URLs to be detected. Finally, the URLs are classified by the SoftMax classifier using global features, the accuracy of the model in malicious URL recgonition is 98.26%. The experimental results show that the JCLA model proposed in this paper is better than the traditional deep learning model or CNN_LSTM combined model for detecting malicious URLs.
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Aljabri, Malak, Fahd Alhaidari, Rami Mustafa A. Mohammad, Samiha Mirza, Dina H. Alhamed, Hanan S. Altamimi, and Sara Mhd Bachar Chrouf. "An Assessment of Lexical, Network, and Content-Based Features for Detecting Malicious URLs Using Machine Learning and Deep Learning Models." Computational Intelligence and Neuroscience 2022 (August 25, 2022): 1–14. http://dx.doi.org/10.1155/2022/3241216.
Повний текст джерелаАнотація:
The World Wide Web services are essential in our daily lives and are available to communities through Uniform Resource Locator (URL). Attackers utilize such means of communication and create malicious URLs to conduct fraudulent activities and deceive others by creating deceptive and misleading websites and domains. Such threats open the doors for many critical attacks such as spams, spyware, phishing, and malware. Therefore, detecting malicious URL is crucially important to prevent the occurrence of many cybercriminal activities. In this study, we examined a set of machine learning (ML) and deep learning (DL) models to detect malicious websites using a dataset comprising 66,506 records of URLs. We engineered three different types of features including lexical-based, network-based and content-based features. To extract the most discriminative features in the dataset, we applied several features selection algorithms, namely, correlation analysis, Analysis of Variance (ANOVA), and chi-square. Finally, we conducted a comparative performance evaluation for several ML and DL models considering set of criteria commonly used to evaluate such models. Results depicted that Naïve Bayes (NB) was the best model for detecting malicious URLs using the applied data with an accuracy of 96%. This research has made contribution to the field by conducting significant features engineering and analysis to identify the best features for malicious URLs predictions, compare different models and achieve a high accuracy using a large new URL dataset.
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Germain, Carol Anne. "URLs: Uniform Resource Locators or Unreliable Resource Locators." College & Research Libraries 61, no. 4 (July 1, 2000): 359–65. http://dx.doi.org/10.5860/crl.61.4.359.
Повний текст джерелаАнотація:
As the use of citing electronic World Wide Web sites grows, the question arises as to whether this practice has scholarly limitations due to the fact that uniform resource locators (URLs) often become inaccessible. This research studies the accessibility of sixty-four URLs cited in thirty-one academic journal articles. Results of this longitudinal study found an increasing decline in the availability of URL citations.
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Wu, Tiefeng, Miao Wang, Yunfang Xi, and Zhichao Zhao. "Malicious URL Detection Model Based on Bidirectional Gated Recurrent Unit and Attention Mechanism." Applied Sciences 12, no. 23 (December 2, 2022): 12367. http://dx.doi.org/10.3390/app122312367.
Повний текст джерелаАнотація:
With the rapid development of Internet technology, numerous malicious URLs have appeared, which bring a large number of security risks. Efficient detection of malicious URLs has become one of the keys for defense against cyber attacks. Deep learning methods bring new developments to the identification of malicious web pages. This paper proposes a malicious URL detection method based on a bidirectional gated recurrent unit (BiGRU) and attention mechanism. The method is based on the BiGRU model. A regularization operation called a dropout mechanism is added to the input layer to prevent the model from overfitting, and an attention mechanism is added to the middle layer to strengthen the feature learning of URLs. Finally, the deep learning network DA-BiGRU model is formed. The experimental results demonstrate that the proposed method can achieve better classification results in malicious URL detection, which has high significance for practical applications.
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Kumi, Sandra, ChaeHo Lim, and Sang-Gon Lee. "Malicious URL Detection Based on Associative Classification." Entropy 23, no. 2 (January 31, 2021): 182. http://dx.doi.org/10.3390/e23020182.
Повний текст джерелаАнотація:
Cybercriminals use malicious URLs as distribution channels to propagate malware over the web. Attackers exploit vulnerabilities in browsers to install malware to have access to the victim’s computer remotely. The purpose of most malware is to gain access to a network, ex-filtrate sensitive information, and secretly monitor targeted computer systems. In this paper, a data mining approach known as classification based on association (CBA) to detect malicious URLs using URL and webpage content features is presented. The CBA algorithm uses a training dataset of URLs as historical data to discover association rules to build an accurate classifier. The experimental results show that CBA gives comparable performance against benchmark classification algorithms, achieving 95.8% accuracy with low false positive and negative rates.
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Suresh Babu, B., Thota Ravisankar, and Sriharsha A. "A URL Shortening Service by Using Flask Framework Based on Base-62 Algorithm." YMER Digital 21, no. 02 (February 15, 2022): 373–82. http://dx.doi.org/10.37896/ymer21.02/37.
Повний текст джерелаАнотація:
In this paper, we have shown the importance of URL shortening at the time of sharing on various platforms. URLs appear to be long, unattractive, on most of the social platforms. They often get broken when shared on social media platforms like e-mail and short URLs can come in handy during these times. To shorten an Internet Long URL, we proposed a URL shortening service that will take a long Web URL/address and creates a shorter URL that will not break when we share on different platforms and make them more manageable by using flask framework base62 algorithm. In this paper, we have also provided a way that helps during digital marketing, social campaigning, and posting by giving the stats of the number of clicks made to the shortened URL as it shows the number of people interested in clicking the URL.
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Federer, Lisa M. "Long-term availability of data associated with articles in PLOS ONE." PLOS ONE 17, no. 8 (August 24, 2022): e0272845. http://dx.doi.org/10.1371/journal.pone.0272845.
Повний текст джерелаАнотація:
The adoption of journal policies requiring authors to include a Data Availability Statement has helped to increase the availability of research data associated with research articles. However, having a Data Availability Statement is not a guarantee that readers will be able to locate the data; even if provided with an identifier like a uniform resource locator (URL) or a digital object identifier (DOI), the data may become unavailable due to link rot and content drift. To explore the long-term availability of resources including data, code, and other digital research objects associated with papers, this study extracted 8,503 URLs and DOIs from a corpus of nearly 50,000 Data Availability Statements from papers published in PLOS ONE between 2014 and 2016. These URLs and DOIs were used to attempt to retrieve the data through both automated and manual means. Overall, 80% of the resources could be retrieved automatically, compared to much lower retrieval rates of 10–40% found in previous papers that relied on contacting authors to locate data. Because a URL or DOI might be valid but still not point to the resource, a subset of 350 URLs and 350 DOIs were manually tested, with 78% and 98% of resources, respectively, successfully retrieved. Having a DOI and being shared in a repository were both positively associated with availability. Although resources associated with older papers were slightly less likely to be available, this difference was not statistically significant, suggesting that URLs and DOIs may be an effective means for accessing data over time. These findings point to the value of including URLs and DOIs in Data Availability Statements to ensure access to data on a long-term basis.
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Wang, Yichen. "Malicious URL Detection An Evaluation of Feature Extraction and Machine Learning Algorithm." Highlights in Science, Engineering and Technology 23 (December 3, 2022): 117–23. http://dx.doi.org/10.54097/hset.v23i.3209.
Повний текст джерелаАнотація:
Cyber attacks are increasing rapidly today, and have a great influence on network security. Many of cyber attacks take place via malicious Uniform Resource Locators (URLs). As a result, various approaches have been developed to detect malicious URLs. One of the most competitive techniques is machine learning and deep learning. However, the detailed techniques concerning feature extraction for URLs and machine learning algorithm are still in the process of development. This paper aims to provide some references for screening out the methods of feature extraction and machine learning algorithm. In the designed experiment, the selected URLs are processed by two different methods of feature extraction, tokenization and vectorization, and lexical feature selection. The resultant constructs two different datasets (data1 and data2) for machine learning. Two traditional learning algorithms (Logistic Regression and SVM) and three ensemble learning algorithms (Random Forest, Gradient Boosting, and Bagging) are adopted as detection model for both datasets. The experimental results demonstrate that the method of tokenization and vectorization for feature extraction, together with ensemble learning algorithms can result in good predictive performance of malicious URL detection.
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M I, Shilpa. "Malicious Websites Classification Using Machine Learning Techniques: A Survey Paper." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (July 31, 2022): 3875–79. http://dx.doi.org/10.22214/ijraset.2022.45824.
Повний текст джерелаАнотація:
Abstract: With the rapid rise of online development, Malware detection is critical for determining if a URL is hazardous or not because hackers steal user information such as usernames, passwords, and credit card numbers by impersonating a trustworthy entity via the internet and use it for illegal activities without the user's knowledge. As a result of applying many classifiers to detect URLs and conducting some operations, the best classifier was chosen as having a good performance in detecting URLs as malicious or benign.
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Lam, Nguyen Tung. "Developing a Framework for Detecting Phishing URLs using Machine Learning." International Journal of Emerging Technology and Advanced Engineering 11, no. 11 (November 13, 2021): 61–67. http://dx.doi.org/10.46338/ijetae1121_08.
Повний текст джерелаАнотація:
The attack technique targeting end-users through phishing URLs is very dangerous nowadays. With this technique, attackers could steal user data or take control of the system, etc. Therefore, early detecting phishing URLs is essential. In this paper, we propose a method to detect phishing URLs based on supervised learning algorithms and abnormal behaviors from URLs. Finally, based on the research results, we build a framework for detecting phishing URLs through endusers. The novelty and advantage of our proposed method are that abnormal behaviors are extracted based on URLs which are monitored and collected directly from attack campaigns instead of using inefficient old datasets. Keywords— phishing URLs; detecting phishing URLs; abnormal behaviors of phishing URLs; Machine learning
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Mishra, Anshumaan, and Fancy. "Efficient Detection of Phising Hyperlinks using Machine Learning." International Journal on Cybernetics & Informatics 10, no. 2 (May 31, 2021): 23–33. http://dx.doi.org/10.5121/ijci.2021.100204.
Повний текст джерелаАнотація:
Phishing is a type of Social Engineering cyber-attack, hackers use it to gain access to confidential credentials like bank account credentials details, details of their personal life like debit card details, social media credentials, etc. Phishing website links seem to seem just like the genuine ones and it's a tedious and troublesome task to differentiate among those websites. In this paper, features are extracted from a separate dataset of phishing and benign website URLs and then using the Machine Learning method we determine the phishing websites. We also rank the features based on the contribution of each feature used in determining the outcome of a URL link using built python libraries. Most of the phishing URLs use a large URL length when used for an attack. Hence, we proposed three machine learning models Random Forest, Support Vector Machine (SVM), Decision trees models for the efficient detection of phishing using fake URLs. The performance of the models is also compared among themselves using a confusion matrix to determine the highest performance. The implemented models have shown an accuracy of 84.81 (for Random Forest and SVM),83.96 (Decision tree)
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Elsadig, Muna, Ashraf Osman Ibrahim, Shakila Basheer, Manal Abdullah Alohali, Sara Alshunaifi, Haya Alqahtani, Nihal Alharbi, and Wamda Nagmeldin. "Intelligent Deep Machine Learning Cyber Phishing URL Detection Based on BERT Features Extraction." Electronics 11, no. 22 (November 8, 2022): 3647. http://dx.doi.org/10.3390/electronics11223647.
Повний текст джерелаАнотація:
Recently, phishing attacks have been a crucial threat to cyberspace security. Phishing is a form of fraud that attracts people and businesses to access malicious uniform resource locators (URLs) and submit their sensitive information such as passwords, credit card ids, and personal information. Enormous intelligent attacks are launched dynamically with the aim of tricking users into thinking they are accessing a reliable website or online application to acquire account information. Researchers in cyberspace are motivated to create intelligent models and offer secure services on the web as phishing grows more intelligent and malicious every day. In this paper, a novel URL phishing detection technique based on BERT feature extraction and a deep learning method is introduced. BERT was used to extract the URLs’ text from the Phishing Site Predict dataset. Then, the natural language processing (NLP) algorithm was applied to the unique data column and extracted a huge number of useful data features in terms of meaningful text information. Next, a deep convolutional neural network method was utilised to detect phishing URLs. It was used to constitute words or n-grams in order to extract higher-level features. Then, the data were classified into legitimate and phishing URLs. To evaluate the proposed method, a famous public phishing website URLs dataset was used, with a total of 549,346 entries. However, three scenarios were developed to compare the outcomes of the proposed method by using similar datasets. The feature extraction process depends on natural language processing techniques. The experiments showed that the proposed method had achieved 96.66% accuracy in the results, and then the obtained results were compared to other literature review works. The results showed that the proposed method was efficient and valid in detecting phishing websites’ URLs.
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Yamaguchi, Yuto, Toshiyuki Amagasa, and Hiroyuki Kitagawa. "Recommending Fresh URLs Using Twitter Lists." Proceedings of the International AAAI Conference on Web and Social Media 7, no. 1 (August 3, 2021): 733–36. http://dx.doi.org/10.1609/icwsm.v7i1.14449.
Повний текст джерелаАнотація:
Recommender systems for social media have attracted considerable attentions due to its inherent features, such as a huge amount of information, social networks, and real-time features. In microblogs, which have been recognized as one of the most popular social media, most of URLs posted by users are considered to be fresh (i.e., shortly after creation). Hence, it is important to recommend URLs in microblogs for appropriate users because users become able to obtain such fresh URLs immediately. In this paper, we propose a URL recommender system using Twitter user lists. Twitter user list is the official functionality to group users into a list along with the name of it. Since it is expected that the members of a list (i.e., users included in the list) have similar characteristics, we utilize this feature to capture the user interests. Experimental results show that our proposed method achieves higher effectiveness than other methods based on the follow-followed network which does not offer user interests explicitly.
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Niveditha, B., and Mallinath Kumbar. "Recovery of Web Citations using Time Travel in Journal of Informetrics." Indian Journal of Information Sources and Services 11, no. 1 (May 19, 2021): 16–21. http://dx.doi.org/10.51983/ijiss-2021.11.1.2809.
Повний текст джерелаАнотація:
The present paper explores the accessibility and recovery of web citations using Time Travel in the Journal of Informetrics during the year 2008-2017. A total of 647 articles were downloaded and 24901 references were extracted. Out of 3546 web citations2084 references contained URLs, DOIs were found in 1298 references and 164 references contained arXiv, WOS article identifier, etc. It was found that 3163 web citations were accessible and the remaining 383 web citations were missing. The study also investigated the characteristic features of display and destination URLs like the path depth, URL length, file format, and top-level domain.
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Pohane, Miss Mayuri, and Dr A. A. Bardekar. "Review Paper on Detection of Malicious URLs Using Machine Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 10, no. 3 (March 31, 2022): 2313–14. http://dx.doi.org/10.22214/ijraset.2022.41065.
Повний текст джерелаАнотація:
Abstract: Malicious websites are most serious threats over the Web. Ever since the inception of the internet, there has been a rise in malicious content over the web such has terrorism, financial fraud, phishing and hacking that targets user’s personal information. Till today the various systems have been invent for the detection of a malicious website based on keywords and data content of the websites. This existing method have some drawbacks results into numbers of victims to increase. Hence we developed a system which helps the user to identify whether the website is malicious or not. Our system identifies whether the site is malicious or not through URL. The proposed system is fast and more accurate compared to current system. The classifier is trained with datasets of 1000 malicious sites and 1000 legitimate site URLs. Trained classifier is used for detection of malicious URLs. Keywords: Malicious URLs, Classifier, Feature Extraction, ID3 Algorithm
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Edward Phillips, Mark, Daniel Gelaw Alemneh, and Brenda Reyes Ayala. "Analysis of URL references in ETDs: a case study at the University of North Texas." Library Management 35, no. 4/5 (June 3, 2014): 293–307. http://dx.doi.org/10.1108/lm-08-2013-0073.
Повний текст джерелаАнотація:
Purpose – Increasingly, higher education institutions worldwide are accepting only electronic versions of their students’ theses and dissertations. These electronic theses and dissertations (ETDs) frequently feature embedded URLs in body, footnote and references section of the document. Additionally the web as ETD subject appears to be on an upward trajectory as the web becomes an increasingly important part of everyday life. The paper aims to discuss these issues. Design/methodology/approach – The authors analyzed URL references in 4,335 ETDs in the UNT ETD collection. Links were extracted from the full-text documents, cleaned and canonicalized, deconstructed in the subparts of a URL and then indexed with the full-text indexer Solr. Queries to aggregate and generate overall statistics and trends were generated against the Solr index. The resulting data were analyzed for patterns and trends within a variety of groupings. Findings – ETDs at the University of North Texas that include URL references have increased over the past 14 years from 23 percent in 1999 to 80 percent in 2012. URLs are being included into ETDs in the majority of cases: 62 percent of the publications analyzed in this work contained URLs. Originality/value – This research establishes that web resources are being widely cited in UNT's ETDs and that growth in citing these resources has been observed. Further it provides a preliminary framework for technical methods appropriate for approaching analysis of similar data that may be applicable to other sets of documents or subject areas.
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Whitman, Michael E., and Humayun Zafar. "URL Manipulation and the Slippery Slope." International Journal of Interdisciplinary Telecommunications and Networking 5, no. 2 (April 2013): 43–50. http://dx.doi.org/10.4018/jitn.2013040104.
Повний текст джерелаАнотація:
While computer ethics and information security courses try to teach computer misuse and unauthorized access as clear black and white examples, when examining the use and potentially misuse of URLs the discussion becomes less clear. This paper examines a number of computer use ethical scenarios focusing on the modification of URLs within Web browsers. Using the documented case of applicants to several Ivy-league schools as a discussion point, this paper presents a survey of U.S. students enrolled in information security and computer ethics classes, asking at what point does modifying the URL become hacking, and at what point does it become unethical. The findings of this study are discussed.
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Grossman, Wendy M. "URLs in Urdu?" Scientific American 286, no. 6 (June 2002): 21–25. http://dx.doi.org/10.1038/scientificamerican0602-21.
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Prithviraj, K. R., and B. T. Sampath Kumar. "Corrosion of URLs." IFLA Journal 40, no. 1 (March 2014): 35–47. http://dx.doi.org/10.1177/0340035214526529.
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Azeez, N. A., and A. A. Ajayi. "Performance Evaluation of Machine Learning Techniques for Identifying Forged and Phony Uniform Resource Locators (URLs)." Nigerian Journal of Technological Development 16, no. 4 (November 22, 2019): 155–69. http://dx.doi.org/10.4314/njtd.v16i4.2.
Повний текст джерелаАнотація:
Since the invention of Information and Communication Technology (ICT), there has been a great shift from the erstwhile traditional approach of handling information across the globe to the usage of this innovation. The application of this initiative cut across almost all areas of human endeavours. ICT is widely utilized in education and production sectors as well as in various financial institutions. It is of note that many people are using it genuinely to carry out their day to day activities while others are using it to perform nefarious activities at the detriment of other cyber users. According to several reports which are discussed in the introductory part of this work, millions of people have become victims of fake Uniform Resource Locators (URLs) sent to their mails by spammers. Financial institutions are not left out in the monumental loss recorded through this illicit act over the years. It is worth mentioning that, despite several approaches currently in place, none could confidently be confirmed to provide the best and reliable solution. According to several research findings reported in the literature, researchers have demonstrated how machine learning algorithms could be employed to verify and confirm compromised and fake URLs in the cyberspace. Inconsistencies have however been noticed in the researchers’ findings and also their corresponding results are not dependable based on the values obtained and conclusions drawn from them. Against this backdrop, the authors carried out a comparative analysis of three learning algorithms (Naïve Bayes, Decision Tree and Logistics Regression Model) for verification of compromised, suspicious and fake URLs and determine which is the best of all based on the metrics (F-Measure, Precision and Recall) used for evaluation. Based on the confusion metrics measurement, the result obtained shows that the Decision Tree (ID3) algorithm achieves the highest values for recall, precision and f-measure. It unarguably provides efficient and credible means of maximizing the detection of compromised and malicious URLs. Finally, for future work, authors are of the opinion that two or more supervised learning algorithms can be hybridized to form a single effective and more efficient algorithm for fake URLs verification.Keywords: Learning-algorithms, Forged-URL, Phoney-URL, performance-comparison
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Monneret, Denis, Martin Gellerstedt, and Dominique Bonnefont-Rousselot. "Determination of age- and sex-specific 99th percentiles for high-sensitive troponin T from patients: an analytical imprecision- and partitioning-based approach." Clinical Chemistry and Laboratory Medicine (CCLM) 56, no. 5 (April 25, 2018): 685–96. http://dx.doi.org/10.1515/cclm-2017-0256.
Повний текст джерелаАнотація:
AbstractBackground:Detection of acute myocardial infarction (AMI) is mainly based on a rise of cardiac troponin with at least one value above the 99th percentile upper reference limit (99th URL). However, circulating high-sensitive cardiac troponin T (hs-cTnT) concentrations depend on age, sex and renal function. Using an analytical imprecision-based approach, we aimed to determine age- and sex-specific hs-cTnT 99th URLs for patients without chronic kidney disease (CKD).Methods:A 3.8-year retrospective analysis of a hospital laboratory database allowed the selection of adult patients with concomitant plasma hs-cTnT (<300 ng/L) and creatinine concentrations, both assayed twice within 72 h with at least 3 h between measurements. Absence of AMI was assumed when the variation between serial hs-cTnT values was below the adjusted-analytical change limit calculated according to the inverse polynomial regression of analytical imprecision. Specific URLs were determined using Clinical and Laboratory Standards Institute (CLSI) methods, and partitioning was tested using the proportion method, after adjustment for unequal prevalences.Results:After outlier removal (men: 8.7%; women: 6.6%), 1414 men and 1082 women with estimated glomerular filtration rate (eGFR) ≥60 mL/min/1.73 m2were assumed as non-AMI. Partitioning into age groups of 18–50, 51–70 and 71–98 years, the hs-cTnT 99th URLs adjusted on French prevalence were 18, 33, 66 and 16, 30, 84 ng/L for men and women, respectively. Age-partitioning was clearly required. However, sex-partitioning was not justified for subjects aged 18–50 and 51–70 years for whom a common hs-cTnT 99th URLs of about 17 and 31 ng/L could be used.Conclusions:Based on a laboratory approach, this study supports the need for age-specific hs-cTnT 99th URLs.
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De Villiers, John E., and André P. Calitz. "A Supplementary Tool for Web-archiving Using Blockchain Technology." African Journal of Information and Communication, no. 25 (June 30, 2020): 1–14. http://dx.doi.org/10.23962/10539/29194.
Повний текст джерелаАнотація:
The usefulness of a uniform resource locator (URL) on the World Wide Web is reliant on the resource being hosted at the same URL in perpetuity. When URLs are altered or removed, this results in the resource, such as an image or document, being inaccessible. While web-archiving projects seek to prevent such a loss of online resources, providing complete backups of the web remains a formidable challenge. This article outlines the initial development and testing of a decentralised application (DApp), provisionally named Repudiation Chain, as a potential tool to help address these challenges presented by shifting URLs and uncertain web-archiving. Repudiation Chain seeks to make use of a blockchain smart contract mechanism in order to allow individual users to contribute to web-archiving. Repudiation Chain aims to offer unalterable assurance that a specific file and its URL existed at a given point in time—by generating a compact, non-reversible representation of the file at the time of its non-repudiation. If widely adopted, such a tool could contribute to decentralisation and democratisation of web-archiving.
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Lai, Chun-Ming, Hung-Jr Shiu, and Jon Chapman. "Quantifiable Interactivity of Malicious URLs and the Social Media Ecosystem." Electronics 9, no. 12 (November 30, 2020): 2020. http://dx.doi.org/10.3390/electronics9122020.
Повний текст джерелаАнотація:
Online social network (OSN) users are increasingly interacting with each other via articles, comments, and responses. When access control mechanisms are weak or absent, OSNs are perceived by attackers as rich environments for influencing public opinions via fake news posts or influencing commercial transactions via practices such as phishing. This has led to a body of research looking at potential ways to predict OSN user behavior using social science concepts such as conformity and the bandwagon effect. In this paper, we address the question of how social recommendation systems affect the occurrence of malicious URLs on Facebook, based on the assumption that there are no differences among recommendation systems in terms of delivering either legitimate or harmful information to users. Next, we use temporal features to build a prediction framework with >75% accuracy to predict increases in certain user group behaviors. Our effort involves the demarcation of URL classes, from malicious URLs viewed as causing significant damage to annoying spam messages and advertisements. We offer this analysis to better understand OSN user sensors reactions to various categories of malicious URLs in order to mitigate their effects.
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Brown, Christopher C. "Knowing Where They Went: Six Years of Online Access Statistics via the Online Catalog for Federal Government Information." College & Research Libraries 72, no. 1 (January 1, 2011): 43–61. http://dx.doi.org/10.5860/crl-68r1.
Повний текст джерелаАнотація:
As federal government information is increasingly migrating to online formats, libraries are providing links to this content via URLs or persistent URLs (PURLs) in their online public access catalogs (OPACs). Clickthrough statistics that accumulated as users visited links to online content in the University of Denver’s library OPAC were gathered over a six-year period and were analyzed. Among the conclusions were that DU users prefer online content over print for both newer and older documents and that there is great benefit in adding URLs above and beyond the URLs supplied by GPO cataloging.
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Abiodun, Orunsolu, Sodiya A.S, and Kareem S.O. "LINKCALCULATOR – AN EFFICIENT LINK-BASED PHISHING DETECTION TOOL." Acta Informatica Malaysia 4, no. 2 (October 2, 2020): 37–44. http://dx.doi.org/10.26480/aim.02.2020.37.44.
Повний текст джерелаАнотація:
The problem of phishing attacks continues to demand new solutions as existing solutions are limited by various challenges such as high computational requirements, zero-day attacks, needs for updates, complex ruled-based, etc. Besides, the emerging mobile market demands simple solutions to phishing due to several factors such as memory, fragmentation, etc. In response to the above challenges, a simple anti-phishing tool called LinkCalculator is presented. The proposed LinkCalculator anti-phishing scheme is based on an algorithm designed to extract link characteristics from loading URLs to determine their legitimacy. Unlike the other link-based extraction approaches, the proposed approach introduced the concept of weight to represent the different links found in a URL. This is because certain link information within parsed webpages or requests is sufficient to classify them as phishing without loss of generality. The approach is experimented using a dataset of 300 instances consisting of 150 legitimate URLs and 150 phishing URLs from openly-available research datasets. The experimental results indicate a significance performance of 100%. True Negative Rate and 0.00% False Positive Rate for legitimate instances and True Positive Rate of 96.67% with 0.03 % False Negative Rate for phishing instances which indicate that the approach offers a more efficient lightweight approach to phishing detection.
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Bu, Seok-Jun, and Hae-Jung Kim. "Optimized URL Feature Selection Based on Genetic-Algorithm-Embedded Deep Learning for Phishing Website Detection." Electronics 11, no. 7 (March 30, 2022): 1090. http://dx.doi.org/10.3390/electronics11071090.
Повний текст джерелаАнотація:
Deep learning models for phishing URL classification based on character- and word-level URL features achieve the best performance in terms of accuracy. Various improvements have been proposed through deep learning parameters, including the structure and learning strategy. However, the existing deep learning approach shows a degradation in recall according to the nature of a phishing attack that is immediately discarded after being reported. An additional optimization process that can minimize the false negatives by selecting the core features of phishing URLs is a promising avenue of improvement. To search the optimal URL feature set and to fully exploit it, we propose a combined searching and learning strategy that effectively models the URL classifier for recall. By incorporating the deep-learning-based URL classifier with the genetic algorithm to search the optimal feature set that minimizing the false negatives, an optimized classifier that guarantees the best performance was obtained. Extensive experiments on three real-world datasets consisting of 222,541 URLs showed the highest recall among the deep learning models. We demonstrated the superiority of the method by 10-fold cross-validation and confirmed that the recall improved compared to the latest deep learning method. In particular, the accuracy and recall were improved by 4.13%p and 7.07%p, respectively, compared to the convolutional–recurrent neural network in which the feature selection optimization was omitted.
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Ford, Charlotte E., and Stephen P. Harter. "The Downside of Scholarly Electronic Publishing: Problems in Accessing Electronic Journals through Online Directories and Catalogs." College & Research Libraries 59, no. 4 (July 1, 1998): 335–46. http://dx.doi.org/10.5860/crl.59.4.335.
Повний текст джерелаАнотація:
This article reports the results of a study on the usefulness of four online e-journal directories and two online union catalogs in accessing electronic journals. The coverage, accuracy, currency, and overlap among the six sources are compared. Multiple uniform resource locators (URLs) were found for most of the e-journals. Directories were found to include fewer URLs per title than the union catalogs, with a higher percentage of current, functioning URLs; the catalogs offered the highest number of working, current URLs. The findings point to different functions served by directories and catalogs, and highlight the difficulties involved in maintaining these reference sources in the Internet environment. Strategies for improving the accuracy and currency of the catalogs and directories are suggested.
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Wu, Tiefeng, Yunfang Xi, Miao Wang, and Zhichao Zhao. "Classification of Malicious URLs by CNN Model Based on Genetic Algorithm." Applied Sciences 12, no. 23 (November 24, 2022): 12030. http://dx.doi.org/10.3390/app122312030.
Повний текст джерелаАнотація:
Researchers have proposed many models for the identification of malicious URLs in network security, but they have not achieved good results. In order to improve this defect, the current popular machine learning algorithm is combined to train the model, thus improving the accuracy of malicious URL classification. This paper proposes a model of a convolutional neural network based on genetic algorithm optimization. Firstly, the genetic algorithm was used to reduce the data dimension of the grammatical features, structural features, and probabilistic features in the extracted malicious URL text, and then the convolutional neural network was used to establish the model and classify the malicious URL. Through experimental verification, the model has achieved good results. Compared with the traditional machine learning model, it improves the accuracy of malicious URL recognition and provides a reference for malicious URL recognition.
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Haigh, Susan. "Comparing the Use of Books with Enhanced Records versus Those Without Enhancements: Methodology Leads to Questionable Conclusions." Evidence Based Library and Information Practice 2, no. 2 (June 6, 2007): 110. http://dx.doi.org/10.18438/b82g6g.
Повний текст джерелаАнотація:
A review of:
Madarash-Hill, Cherie and J.B. Hill. “Electronically Enriched Enhancements in Catalog Records: A Use Study of Books Described on Records With URL Enhancements Versus Those Without.” Technical Services Quarterly 23.2 (2005): 19-31.
Abstract
Objective – To compare the use of books described by catalogue records that are enhanced with URL links to such information as dust jackets, tables of contents, sample text, and publishers’ descriptions, with the use of books described by records that are not enhanced with such links.
Design – Use study.
Setting – Academic library (Southeastern Louisiana University, Sims Memorial Library).
Subjects – 180 records with enhancements and 180 records (different titles) without enhancements.
Methods – The study identified the sample of unenhanced records by conducting searches of the broad subject terms “History”, “United States”, “Education”, and “Social” and limiting the searches to books. The enhanced sample was derived in the same manner, but with additional search limiters to identify only those records that had URL enhancements. An equal sample of enhanced and unenhanced records (50 or 30 of each) was tracked for each of four search terms. Only records for books that could be checked out were included, as use statistics were based on whether or not a book was borrowed. While half of the enhanced records had full-text elements (such as descriptions) that were indexed and thus searchable, the rate of use for these records was not tracked separately from the enhanced records that only had URL enhancements.
Main results – Books described on records with URL enhancements for publisher descriptions, tables of contents, book reviews, or sample text had higher use than those without URL enhancements. Only 7% of titles with URLs, compared with 21% of those without, had not been borrowed. 74.67% of titles with URLs had been checked out one or two times, compared with 69.5% of those without URLs. The number of titles with enhanced records that had 3 or more checkouts was almost double the rate of unenhanced titles (18% to 9.5%).
Conclusion – The authors conclude that catalogue records that have electronic links to book reviews, cover jackets, tables of contents, or publisher descriptions can lead to higher use of books, particularly if textual enhancements such as descriptions are also searchable.
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Zittrain, Jonathan, Kendra Albert, and Lawrence Lessig. "Perma: Scoping and Addressing the Problem of Link and Reference Rot in Legal Citations." Legal Information Management 14, no. 2 (June 2014): 88–99. http://dx.doi.org/10.1017/s1472669614000255.
Повний текст джерелаАнотація:
AbstractIt has become increasingly common for a reader to follow a URL cited in a court opinion or a law review article, only to be met with an error message because the resource has been moved from its original online address. This form of reference rot, commonly referred to as ‘linkrot’, has arisen from the disconnect between the transience of online materials and the permanence of legal citation, and will only become more prevalent as scholarly materials move online. The present paper*, written by Jonathan Zittrain, Kendra Albert and Lawrence Lessig, explores the pervasiveness of linkrot in academic and legal citations, finding that more than 70% of the URLs within the Harvard Law Review and other journals, and 50% of the URLs within United States Supreme Court opinions, do not link to the originally cited information. In light of these results, a solution is proposed for authors and editors of new scholarship that involves libraries undertaking the distributed, long-term preservation of link contents.
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Patel, Rutul, Sanjay Kshetry, Sanket Berad, and Justin Zirthantlunga. "Phishing URL Detection using Machine Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 3467–72. http://dx.doi.org/10.22214/ijraset.2022.39979.
Повний текст джерелаАнотація:
Abstract: As we have moved the majority of our monetary, business related, and other day by day exercises to the web, we are presented to more serious dangers as cybercrimes. URL-based phishing assaults are quite possibly the most widely recognized dangers to web client. In this kind of assault, the aggressor takes advantage of the human weakness rather than programming defects. It targets the two people and associations, instigates them to tap on URLs that look secure, and take private data or infuse malware on our framework. Diverse AI calculations are being utilized for the identification of phishing URLs, that is, to group a URL as phishing or real. Analysts are continually attempting to work on the presentation of existing models and increment their exactness. In this work, we expect to audit different AI strategies utilized for this reason, alongside datasets and URL highlights used to prepare the AI models. The presentation of various AI calculations and the strategies used to build their exactness measures are talked about and investigated. The objective is to make an overview asset for scientists to become familiar with the current advancements in the field and add to making phishing discovery models that yield more precise outcomes. Keywords: Phishing, Phishing websites, Machine Learning, anti-phishing, phishing attack, security and privacy, phishing approaches
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Wu, Shaomei, Chenhao Tan, Jon Kleinberg, and Michael Macy. "Does Bad News Go Away Faster?" Proceedings of the International AAAI Conference on Web and Social Media 5, no. 1 (August 3, 2021): 646–49. http://dx.doi.org/10.1609/icwsm.v5i1.14196.
Повний текст джерелаАнотація:
We study the relationship between content and temporal dynamics of information on Twitter, focusing on the persistence of information. We compare two extreme temporal patterns in the decay rate of URLs embedded in tweets, defining a prediction task to distinguish between URLs that fade rapidly following their peak of popularity and those that fade more slowly. Our experiments show a strong association between the content and the temporal dynamics of information: given unigram features extracted from corresponding HTML webpages, a linear SVM classifier can predict the temporal pattern of URLs with high accuracy. We further explore the content of URLs in the two temporal classes using various textual analysis techniques (via LIWC and trend detection). We find that the rapidly-fading information contains significantly more words related to negative emotion, actions, and more complicated cognitive processes, whereas the persistent information contains more words related to positive emotion, leisure, and lifestyle.
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Vyawhare, Chaitanya R., Reshma Y. Totare, Prashant S. Sonawane, and Purva B. Deshmukh. "Machine Learning System for Malicious Website Detection using Concept Drift Detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (May 31, 2022): 47–55. http://dx.doi.org/10.22214/ijraset.2022.42048.
Повний текст джерелаАнотація:
Abstract: The rampant increase in the number of available cyber attack vectors and the frequency of cyber attacks necessitates the implementation of robust cybersecuritysystems. Malicious websites are a significant threat to cybersecurity. Miscreants and hackers use malicious websites for illegal activities such as disrupting the functioning of the systems by implanting malware, gaining unauthorized access to systems, or illegally collecting personal information. We propose and implement an approach for classifying malicious and benign websites given their Uniform Resource Locator(URL) as input. Using the URL provided by the user, we collect Lexical, Host-Based, and Content-Based features for the website. These features are fed into a supervised Machine Learning algorithm as input that classifies the URL as malicious or benign. The models are trained on a dataset consisting of multiple malicious and benign URLs. We have evaluated the accuracy of classification for Random forests, Gradient Boosted Decision Trees and Deep Neural Network classifiers. One loophole in the use of Machine learning for detection is the availability of the same training data to the attackers. This data is exploited by the miscreants to alter the features associated with the Malicious URLs, which will be classified as benign by the supervised learning algorithms. Further, owing to the dynamic nature of the malicious websites, we also propose a paradigm for detecting and countering these manually induced concept drifts. IndexTerms—URL Feature Extraction, Malicious Website Detection, Concept Drifts, Feature Vectors, Gradient Boosted Trees, Random Forest, Feedforward Neural Networks Keywords: (cyber attack, URL, Supervised machine learning, DNN, extraction)
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Pandian, Asha, Sumit Kumar, and Satish Kumar. "URL Sand Virus." Journal of Computational and Theoretical Nanoscience 17, no. 8 (August 1, 2020): 3553–57. http://dx.doi.org/10.1166/jctn.2020.9230.
Повний текст джерелаАнотація:
This study investigates on running a program in a very controlled environment where various URLs are blocked while the program is running. The program selects the HTML content from certain websites, extracts the target program from the HTML content and runs them automatically on your system. While the process is running, every URL that is been accessed is actively monitored at every 5 seconds until the browser is closed.
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Loan, Fayaz Ahmad, and Ufaira Yaseen Shah. "The decay and persistence of web references." Digital Library Perspectives 36, no. 2 (May 9, 2020): 157–66. http://dx.doi.org/10.1108/dlp-02-2020-0013.
Повний текст джерелаАнотація:
Purpose The purpose of this study is to identify the persistence and decay of uniform resource locator (URLs) associated with Web references. The decaying of Web references is analyzed in relation to their age, domain, technical errors and error codes. Design/methodology/approach The Web references of the Journal of Informetrics were selected for analysis and interpretation to fulfill the set objectives. The references of all the scholarly articles, excluding editorials and reviews published in the Journal of Informetrics for five years from 2007 to 2011 were recorded in a text file. Later, the URLs were extracted from the articles to verify their accessibility in terms of persistence and decay. The collected data were then transferred into an excel file and tabulated for further analysis and interpretation using simple statistical techniques. Findings The results showed that of the total 7,409 citations retrieved from 221 articles, 358 citations (4.8%) were Web citations. These Web citations were assessed to find their persistence and decay. The results reveal that 115 (32.12%) Web references were missing or dead. The most common error associated with the missing Web citations was Error 404 Page not found, contributing 60% of the total missing citations, followed by 400 Bad Request Error (35.65%). The domain analysis of missing Web citations depicts that most of the missing URLs were associated with the .gov domain (40%), followed by .edu (29.58%) and .com (26.04%). Research limitations/implications The Web references of a single journal, namely, Journal of Informetrics, were analyzed for five years, and hence, the generalization of findings needs to be cautioned. Practical implications The URL decay is becoming a major problem in the preservation and citation of the Web resources, and collaborative efforts are needed to reduce the decaying of URLs. Originality/value A good number of studies have been conducted to analyze the persistence and decay of Web references, as it is the hot topic of research across disciplines, and this study is a step further in the same direction.
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Kumar, Harish, Anshal Prasad, Ninad Rane, Nilay Tamane, and Anjali Yeole. "Dr. Phish: Phishing Website Detector." E3S Web of Conferences 297 (2021): 01032. http://dx.doi.org/10.1051/e3sconf/202129701032.
Повний текст джерелаАнотація:
Phishing is a common attack on credulous people by making them disclose their unique information. It is a type of cyber-crime where false sites allure exploited people to give delicate data. This paper deals with methods for detecting phishing websites by analyzing various features of URLs by Machine learning techniques. This experimentation discusses the methods used for detection of phishing websites based on lexical features, host properties and page importance properties. We consider various data mining algorithms for evaluation of the features in order to get a better understanding of the structure of URLs that spread phishing. To protect end users from visiting these sites, we can try to identify the phishing URLs by analyzing their lexical and host-based features.A particular challenge in this domain is that criminals are constantly making new strategies to counter our defense measures. To succeed in this contest, we need Machine Learning algorithms that continually adapt to new examples and features of phishing URLs.