Web classification using support vector machine

Sun, Aixin; Lim, Ee-Peng; Ng, Wee-Keong

doi:10.1145/584948.584952

Cited by 38 publications

(54 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the recent years, many researchers have investigated the classification of the Surface Web (Dumais and Chen, 2000;Sun et al, 2002;Kan, 2004;Kan and Thi, 2005;Kaur, 2014), and the Deep Web (Su et al, 2006;Xu et al, 2007;Barbosa et al, 2007;Lin et al, 2008;Zhao et al, 2008;Xian et al, 2009;Khelghati, 2016). However, the Darknet classification literature is still in its early stages and specifically the classification of the illegal activities (Graczyk and Kinningham, 2015;Moore and Rid, 2016).…”

Section: Related Workmentioning

confidence: 99%

Classifying Illegal Activities on Tor Network Based on Web Textual Contents

Nabki¹,

Fidalgo

Alegre

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

View full text Add to dashboard Cite

The freedom of the Deep Web offers a safe place where people can express themselves anonymously but they also can conduct illegal activities. In this paper, we present and make publicly available 1 a new dataset for Darknet active domains, which we call it "Darknet Usage Text Addresses" (DUTA). We built DUTA by sampling the Tor network during two months and manually labeled each address into 26 classes. Using DUTA, we conducted a comparison between two well-known text representation techniques crossed by three different supervised classifiers to categorize the Tor hidden services. We also fixed the pipeline elements and identified the aspects that have a critical influence on the classification results. We found that the combination of TF-IDF words representation with Logistic Regression classifier achieves 96.6% of 10 folds cross-validation accuracy and a macro F1 score of 93.7% when classifying a subset of illegal activities from DUTA. The good performance of the classifier might support potential tools to help the authorities in the detection of these activities.

show abstract

Section: Related Workmentioning

confidence: 99%

Classifying Illegal Activities on Tor Network Based on Web Textual Contents

Nabki¹,

Fidalgo

Alegre

et al. 2017

Proceedings of the 15th Conference of the European Chapter of The Association for Computational Linguistics: Volume 1

View full text Add to dashboard Cite

show abstract

“…Among the learning and data classification algorithms, we use SVM because SVM-based classifiers have shown promising results in the classification of text and web pages as reported in the literature [3,26].…”

Section: Support Vector Machine Learningmentioning

confidence: 99%

New use of the HITS algorithm for fast web page classification

Meadi¹,

Babahenini²,

Ahmed³

2017

Turk J Elec Eng & Comp Sci

View full text Add to dashboard Cite

Abstract:The immense number of documents published on the web requires the utilization of automatic classifiers that allow organizing and obtaining information from these large resources. Typically, automatic web pages classifiers handle millions of web pages, tens of thousands of features, and hundreds of categories. Most of the classifiers use the vector space model to represent the dataset of web pages. The components of each vector are computed using the term frequency inversed document frequency (TFIDF) scheme. Unfortunately, TFIDF-based classifiers face the problem of the large-scale size of input data that leads to a long processing time and an increase in resource requests. Therefore, there is an increasing demand to alleviate these problems by reducing the size of the input data without influencing the classification results. In this paper, we propose a novel approach that improves web page classifiers by reducing the size of the input data (i.e. web pages and feature reduction) by using the hypertext induced topic search (HITS) algorithm.We employ HITS results for weighting remaining features. We evaluate the performance of the proposed approach by comparing it with the TFIDF-based classifier. We demonstrate that our approach significantly reduces the time needed for classification.

show abstract

“…Another type of classification is based on how the assignment to classes is made: hard or soft classification (Qi & Davison, 2009). Based on the relations between classes, there are two types of classification: flat and hierarchical (Sun, et al 2002).…”

Section: Automatic Classification Typesmentioning

confidence: 99%