Detecting Illegal Online Gambling (IOG) Services in the Mobile Environment

Min, Moohong; Lee, Jemin Justin; Lee, Kyung-Ho

doi:10.1155/2022/3286623

Cited by 9 publications

(3 citation statements)

References 37 publications

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“…Some studies have considered the statistical information within the text as well as the text itself, using text-mining methods such as word2vec or long short-term memory to detect spam [20,21]. Further, a method based on text mining was proposed to prevent illegal URLs from being disseminated via short-message services [22]. Recently, a spam detection technique utilizing large language models using contextualized embeddings such as BERT and ELMo has also been proposed [23].…”

Section: Misuse Detectionmentioning

confidence: 99%

Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection

Choi,

Jeon,

Jeon

2024

Sensors

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The growing popularity of social media has engendered the social problem of spam proliferation through this medium. New spam types that evade existing spam detection systems are being developed continually, necessitating corresponding countermeasures. This study proposes an anomaly detection-based framework to detect new Twitter spam, which works by modeling the characteristics of non-spam tweets and using anomaly detection to classify tweets deviating from this model as anomalies. However, because modeling varied non-spam tweets is challenging, the technique’s spam detection and false positive (FP) rates are low and high, respectively. To overcome this shortcoming, anomaly detection is performed on known spam tweets pre-detected using a trained decision tree while modeling normal tweets. A one-class support vector machine and an autoencoder with high detection rates are used for anomaly detection. The proposed framework exhibits superior detection rates for unknown spam compared to conventional techniques, while maintaining equivalent or improved detection and FP rates for known spam. Furthermore, the framework can be adapted to changes in spam conditions by adjusting the costs of detection errors.

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Section: Misuse Detectionmentioning

confidence: 99%

Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection

Choi,

Jeon,

Jeon

2024

Sensors

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“…They examined them across various dimensions, including webpage structure similarity, Search Engine Optimization (SEO) methods, abuse of Internet infrastructure, third-party online payment, and gambling groups. Min et al [10] proposed two automatic detection systems utilizing spam SMS to identify Illegal Online Gambling (IOG) websites. Other researchers propose a hybrid multimodal data fusion-based method for identifying gambling websites by extracting and fusing visual and semantic features of the website screenshots [11].…”

Section: Introductionmentioning

confidence: 99%

Detecting Hidden Illegal Online Gambling on .go.id Domains Using Web Scraping Algorithms

Nurseno,

Aditiawarman,

Al Qodri Maarif

et al. 2024

matrik

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The profitable gambling business has encouraged operators to promote online gambling using black hat SEO by targeting official sites such as government sites. Operators have used various techniques to prevent search engines from distinguishing between genuine and illegal content. This research aims to determine whether websites with the go.id domain have been compromised with hidden URLs affiliated with online gambling sites. The method used in this research is an experiment using a FOFA.info dataset containing a complete list of 450,000 .go.id domains. A web scraping algorithm developed in Python was used to identify potentially compromised websites from the targeted listby analyzing gambling-related keywords in local languages, such as ’slot,’ ’judi,’ ’gacor,’ and ’togel'. The results showed that 958 of the 1,482 suspected.go.id sites had been compromised with an accuracy rate of 99.1%. This implies that security gaps have been exploited by illegal online gambling sites, posing a reputational risk to the government. Lastly, the scrapping algorithm tool developed in this research can detect illegal online gambling hidden in domains such as .ac.id, .or.id, .sch.id, and help authorities take necessary action.

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“…To reduce cybercrime and purify cyberspace, a variety of methods have been proposed to identify gambling websites [1][2][3]. Malicious website identification methods can be classified into blacklist-based, URL-based, single-feature-based, and mixed-feature-based.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Multimodal Data Fusion-Based Method for Identifying Gambling Websites

et al. 2022

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With the development of network technology, the number of gambling websites has grown dramatically, causing a threat to social stability. There are many machine learning-based methods are proposed to identify gambling websites by analyzing the URL, the text, and the images of the websites. Nevertheless, most of the existing methods ignore one important piece of information, i.e., the text within the website images. Only the visual features of images are extracted for detection, while the semantic features of texts on the images are ignored. However, these texts have key information clearly pointing to gambling websites, which can help us identify such websites more accurately. Therefore, how to fuse image and text multimodal data is a challenge that should be met.Motivated by this, in this paper, we propose a hybrid multimodal data fusion-based method for identifying gambling websites by extracting and fusing visual and semantic features of the website screenshots. First, we fine tune the pretrained ResNet34 model to train an image classifier and to extract visual features of webpage screenshots. Second, we extract textual content from webpage screenshots through the optical character recognition (OCR) technique. We use pretrained Word2Vec word vectors as the initial embedding layer and use Bi-LSTM to train a text classifier and extract semantic features of textual content on the screenshots. Third, we use self-attention to fuse the visual and semantic features and train a multimodal classifier. The prediction results of image, text, and multimodal classifiers are fused by the late fusion method to obtain the final prediction result. To demonstrate the effectiveness of the proposed method, we conduct experiments on the webpage screenshot dataset we collected. The experimental results indicate that OCR text on the webpage screenshots has strong semantic features and the proposed hybrid multimodal data fusion based method can effectively improve the performance in identifying gambling websites, with accuracy, precision, recall, and F1-score all over 99%.

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Detecting Illegal Online Gambling (IOG) Services in the Mobile Environment

Cited by 9 publications

References 37 publications

Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection

Scalable Learning Framework for Detecting New Types of Twitter Spam with Misuse and Anomaly Detection

Detecting Hidden Illegal Online Gambling on .go.id Domains Using Web Scraping Algorithms

A Hybrid Multimodal Data Fusion-Based Method for Identifying Gambling Websites

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