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Hybridized Feature Set for Accurate Arabic Dark Web Pages Classification

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Intelligent Software Methodologies, Tools and Techniques (SoMeT 2015)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 532))

Abstract

Security informatics and computational intelligence are gaining more importance in detecting terrorist activities as the extremist groups are misusing many of the available Internet services to incite violence and hatred. However, inadequate performance of statistical based computational intelligence methods reduces intelligent techniques efficiency in supporting counterterrorism efforts, and limits the early detection opportunities of potential terrorist activities. In this paper, we propose a feature set hybridization method, based on feature selection and extraction methods, for accurate content classification in Arabic dark web pages. The proposed method hybridizes the feature sets so that the generated feature set contains less number of features that capable of achieving higher classification performance. A selected dataset from Dark Web Forum Portal (DWFP) is used to test the performance of the proposed method that based on Term Frequency - Inverse Document Frequency (TFIDF) as feature selection method on one hand, while Random Projection (RP) and Principal Component Analysis (PCA) feature selection methods on the other hand. Classification results using the Support Vector Machine (SVM) classifier show that a high classification performance has been achieved base on the hybridization of TFIDF and PCA, where 99 % of F1 and accuracy performance has been achieved.

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Notes

  1. 1.

    Downloaded from http://arabicstopwords.sourceforge.net/.

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Acknowledgment

The Universiti Teknologi Malaysia (UTM) and Ministry of Education Malaysia under Research University grants 00M19, 02G71 and 4F550 are hereby acknowledged for some of the facilities that were utilized during the course of this research work. Moreover, The Al-Quds Open University – Palestine is acknowledged for supporting and funding the first author during his PhD study.

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Authors and Affiliations

  1. Faculty of Computing, Universiti Teknologi Malaysia (UTM), Skudai, Johor, Malaysia

    Thabit Sabbah & Ali Selamat

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  1. Thabit Sabbah
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  2. Ali Selamat
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Correspondence to Ali Selamat .

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Editors and Affiliations

  1. Iwate Prefectural University, Takizawa, Japan

    Hamido Fujita

  2. University of Naples "Federico II", Napoli, Italy

    Guido Guizzi

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Sabbah, T., Selamat, A. (2015). Hybridized Feature Set for Accurate Arabic Dark Web Pages Classification. In: Fujita, H., Guizzi, G. (eds) Intelligent Software Methodologies, Tools and Techniques. SoMeT 2015. Communications in Computer and Information Science, vol 532. Springer, Cham. https://doi.org/10.1007/978-3-319-22689-7_13

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  • DOI: https://doi.org/10.1007/978-3-319-22689-7_13

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  • Online ISBN: 978-3-319-22689-7

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