Semantic Approach for Identifying Harmful Sites Using the Link Relations

Shin, Junghoon; Lee, Sang-Jun; Wang, Taehyung

doi:10.1109/icsc.2014.53

Cited by 4 publications

(3 citation statements)

References 2 publications

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“…The website contains rich information, such as text, images, links, cascading style sheets (CSS), HTML tags, web fingerprinting, etc. Some studies have utilized hyperlinks [10] or HTML tags [11]. Ma et al [12] proposed a lightweight graph-based method to detect pornographic and gambling websites by extracting the textual content in HTML.…”

Section: Single-modal Based Identification Methodsmentioning

confidence: 99%

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MEDAL: A Multimodality-Based Effective Data Augmentation Framework for Illegal Website Identification

Wen,

Zhang,

Wang

et al. 2024

Electronics

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The emergence of illegal (gambling, pornography, and attraction) websites seriously threatens the security of society. Due to the concealment of illegal websites, it is difficult to obtain labeled data with high quantity. Meanwhile, most illegal websites usually disguise themselves to avoid detection; for example, some gambling websites may visually resemble gaming websites. However, existing methods ignore the means of camouflage in a single modality. To address the above problems, this paper proposes MEDAL, a multimodality-based effective data augmentation framework for illegal website identification. First, we established an illegal website identification framework based on tri-training that combines information from different modalities (including image, text, and HTML) while making full use of numerous unlabeled data. Then, we designed a multimodal mutual assistance module that is integrated with the tri-training framework to mitigate the introduction of error information resulting from an unbalanced single-modal classifier performance in the tri-training process. Finally, the experimental results on the self-developed dataset demonstrate the effectiveness of the proposed framework, performing well on accuracy, precision, recall, and F1 metrics.

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Section: Single-modal Based Identification Methodsmentioning

confidence: 99%

“…The current methods for identifying these illegal websites can be divided into four main types: blacklist-based [4,5], URL-based [6][7][8][9], single modal-based [10][11][12][13][14][15][16], and multimodal fusion-based methods [17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

MEDAL: A Multimodality-Based Effective Data Augmentation Framework for Illegal Website Identification

Wen,

Zhang,

Wang

et al. 2024

Electronics

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show abstract

“…Later on, some approaches to detect illegal websites using more textual information, such as hyperlinks [ 19 , 20 ] and HTML tags [ 21 ], were developed. For example, Jain et al [ 20 ] proposed a phishing attack detection method based on hyperlinks.…”

Section: Related Workmentioning

confidence: 99%

Automatic Detection of Pornographic and Gambling Websites Based on Visual and Textual Content Using a Decision Mechanism

Chen

Zheng

Zhou

et al. 2020

Sensors

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Pornographic and gambling websites become increasingly stubborn via disguising, misleading, blocking, and bypassing, which hinder the construction of a safe and healthy network environment. However, most traditional approaches conduct the detection process through a single aspect of these sites, which would fail to handle the more intricate and challenging situations. To alleviate this problem, this study proposed an automatic detection system for porn and gambling websites based on visual and textual content using a decision mechanism (PG-VTDM). This system can be applied to the intelligent wireless router at home or school to realize the identification, blocking, and warning of ill-suited websites. First, Doc2Vec was employed to learn the textual features that can be used to represent the textual content in the hypertext markup language (HTML) source code of the websites. In addition, the traditional bag-of-visual-words (BoVW) was improved by introducing local spatial relationships of feature points for better representing the visual features of the website screenshot. Then, based on these two types of features, a text classifier and an image classifier were both trained. In the decision mechanism, a data fusion algorithm based on logistic regression (LR) was designed to obtain the final prediction result by measuring the contribution of the two classification results to the final category prediction. The efficiency of this proposed approach was substantiated via comparison experiments using gambling and porn website datasets crawled from the Internet. The proposed approach outperformed the approach based on a single feature and some state-of-the-art approaches, with accuracy, precision, and F-measure all over 99%.

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HinPage: Illegal and Harmful Webpage Identification Using Transductive Classification

Liu

2023

Information Security and Cryptology

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Semantic Approach for Identifying Harmful Sites Using the Link Relations

Cited by 4 publications

References 2 publications

MEDAL: A Multimodality-Based Effective Data Augmentation Framework for Illegal Website Identification

MEDAL: A Multimodality-Based Effective Data Augmentation Framework for Illegal Website Identification

Automatic Detection of Pornographic and Gambling Websites Based on Visual and Textual Content Using a Decision Mechanism

HinPage: Illegal and Harmful Webpage Identification Using Transductive Classification

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