Crawl-based analysis of web applications: Prospects and challenges

Deursen, Arie van; Mesbah, Ali; Nederlof, Alex

doi:10.1016/j.scico.2014.09.005

Cited by 25 publications

(12 citation statements)

References 24 publications

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“…For mining huge datasets, web crawling is used to index information on a website utilizing a uniform resource locator (URL) and an application programming interface (API). Crawlers lead to a process of document sharing, more about interactive content, and even full-fledged apps as the web advances [4]. After Facebook and Instagram, Twitter is the world's third most popular online social network (OSN), with a simple data model and direct data access API.…”

Section: Methodsmentioning

confidence: 99%

Analysis of named-entity effect on text classification of traffic accident data using machine learning

Putra

Girsang

2022

IJEECS

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<span lang="EN-US">With the rising number of accidents in Indonesia, it is still necessary to evaluate and analyze accident data. The categorization of traffic accident data has been developed using word embedding, however additional work is needed to achieve better results. Several informative named entities are frequently sufficient to differentiate whether or not information on a traffic accident exists. Named-entities are informational characteristics that can offer details about a text. The influence of named-entities on thematic text categorization is examined in this paper. The information was collected using a Twitter social media crawl. Preprocessing is done at the beginning of the process to modify and delete useful text as well as label specified entities. On Support Vector Machine (SVM), scheme comparisons were performed for (i) Word Embedding, (ii) the number of occurrences of Named Entities, and (iii) the combination of the two is known as a Hybrid. The Hybrid scheme produced an improvement in classification accuracy of 90.27 percent when compared to Word Embedding scheme and occurrences of named entities scheme, according to tests conducted using 1.885 data consisting of 788 accident data and 1.067 non-accident data.</span>

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Section: Methodsmentioning

confidence: 99%

Analysis of named-entity effect on text classification of traffic accident data using machine learning

Putra

Girsang

2022

IJEECS

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“…This process is only possible if the webpage structure is known [30,31]. The problem is therefore the same, since it involves human intervention to analyze the structure of the page, inducing potential errors and a slow indexing process [32]. The page analysis therefore must be fully automated for the extraction of information on the LGs.…”

Section: Related Workmentioning

confidence: 99%

Information Extraction Model to Improve Learning Game Metadata Indexing

Morie¹,

Marfisi-Schottman²,

Tra³

2020

ISI

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The use of Learning Games (LGs) in schools is a success factor for students. The benefits they bring to the learning process should be widely disseminated at all levels of education. Currently, there are thousands of LGs that cover a large variety of educations fields. Despite this large choice of LGs, very few are used by teachers, due to the difficulty of finding and selecting suitable LGs. The aim of this paper is to propose an extraction model that will automatically collect the information about LGs directly from their web pages, in order to index them in a catalogue. The proposed ADEM (Automatic Description Extraction Model), browses the web pages describing LGs and does a first cleaning to remove any unnecessary information. Then a detection of description blocks, based on a certain number of criteria, identifies the regions containing the LG description text. Finally, an indexing on specific fields is performed. ADEM made it possible to automatically process 785 web pages to extract LG metadata indexing information. The results of this extraction process were validated by 20 teachers. This model therefore offers a promising starting point for better LG indexing and the creation of a complete catalogue.

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“…Parsing Java Script codes on the client-side can crawl pages in Ajax applications [31]. The crawled XSS vulnerabilities can be automatically repaired [32].Analyzing the source codes of Web applications may find more injection points than the crawling method [12].…”

Section: Key Technologies Of Xss Attack Detection Injection Point Anamentioning

confidence: 99%

Review of XSS Attack and Detection on Web Applications

Zhao¹,

Wang²,

Ding³

2017

Proceedings of the International Conference on Computer Networks and Communication Technology (CNCT 2016)

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Abstract. Aiming at the difficulties to prevent Web applications to be maliciously injected which are increased by all kinds of dynamic Web technologies applied, concentrate on XSS attack, this paper reviews the research progresses of Web application injection vulnerabilities detection in recent years. It summarizes the classification and causes of the XSS injection security vulnerabilities, analyzes the complexity of security vulnerabilities detection; then proposes the key technologies of the existing detection approached, including analyzing and identifying the injection points, injection detection by software analysis and testing, symbolic execution, taint analysis; finally presents its future development direction.

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Crawl-based analysis of web applications: Prospects and challenges

Cited by 25 publications

References 24 publications

Analysis of named-entity effect on text classification of traffic accident data using machine learning

Analysis of named-entity effect on text classification of traffic accident data using machine learning

Information Extraction Model to Improve Learning Game Metadata Indexing

Review of XSS Attack and Detection on Web Applications

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