Summary DarkBERT Language Model for Dark Web arxiv.org
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One Line
DarkBERT is a language model designed for the Dark Web that outperforms other models in detecting illegal activities, making it useful for law enforcement and cybersecurity researchers.
Key Points
- DarkBERT is a language model designed for the Dark Web to understand the unique language patterns of illegal online activities.
- The model outperforms other language models in specific use cases related to cybersecurity, such as threat detection, dark web forum thread detection, leak site detection, and ransomware activity.
- The model was trained on two datasets: DUTA and CoDA, which were preprocessed to remove empty pages and categories with low page counts.
- The model can be used for cybersecurity and CTI applications on the Dark Web, including ransomware leak site detection and identifying threats on the Dark Web using a fill-mask approach that captures semantically related keywords.
- The DarkBERT language model was evaluated on the CoDA cased dataset using confusion matrices and compared to BERT cased and RoBERTa models.
Summaries
234 word summary
The DarkBERT language model was developed for the Dark Web, using a pretraining corpus of 5.43 million pages and excluding low information density pages. It uses 10 predefined categories from CoDA to categorize pages and preprocesses text by masking IP addresses, URLs, and cryptocurrency addresses. The model can identify legal and illegal activities and outperforms existing language models. It was evaluated on several datasets achieving high performance and can be useful for law enforcement agencies and cybersecurity researchers to monitor and detect illegal activities in the dark web. The study evaluates precision at k for keyword sets related to drugs, and DarkBERT CoDA outperforms BERT Reddit for k ranging from 10 to 20. DarkBERT is a language model designed for the Dark Web that uses unsupervised and supervised learning techniques to understand the unique language patterns of illegal online activities. It outperforms other language models in specific use cases related to cybersecurity, such as threat detection, dark web forum thread detection, leak site detection, and ransomware activity. The model can be used for cybersecurity and CTI applications on the Dark Web and has potential applications for law enforcement agencies to monitor criminal activity. The construction process involved data collection, filtering, and text preprocessing, addressing ethical considerations such as removing sensitive information. The model was trained on two datasets: DUTA and CoDA, which were preprocessed to remove empty pages and categories with low page counts.
744 word summary
DarkBERT is a language model specifically designed for the Dark Web, using a combination of unsupervised and supervised learning techniques to understand the unique language patterns of illegal online activities. It has potential applications for law enforcement agencies to monitor criminal activity on the Dark Web. The linguistic differences between the Surface Web and the Dark Web are explored, and DarkBERT is shown to be capable of representing the language used in the Dark Web domain. The model outperforms other language models in specific use cases related to cybersecurity, such as threat detection, dark web forum thread detection, leak site detection, and ransomware activity. The construction process involved data collection, filtering, and text preprocessing, addressing ethical considerations such as removing sensitive information. Two variations of the text corpus were used for pretraining purposes, raw and preprocessed. The model was trained on two datasets: DUTA and CoDA, which were preprocessed to remove empty pages and categories with low page counts. The DarkBERT language model was evaluated for its ability to classify dark web activities, showing high similarity of pages in categories such as drugs, electronics, and gambling. The model outperformed other models for both datasets and performed similarly for cased and uncased models. The pre-trained language model can be used for cybersecurity and CTI applications on the Dark Web, including ransomware leak site detection, where the model's performance was found to be better than other language models. The model was also developed to detect noteworthy threads on the Dark Web by analyzing activities in hacking forums, outperforming other language models in this task. The model is effective in identifying threats on the Dark Web using a fill-mask approach that captures semantically related keywords. The dataset used for training includes activities targeting popular software or organizations, sharing sensitive or private information, and distributing critical malware or vulnerabilities. The DarkBERT Language Model for Dark Web compares three language models' ability to produce keyword sets related to drugs in the Dark Web. The study evaluates precision at k (P @k) for k ranging from 10 to 50 using ground truth data provided by Zhu et al. (2021). DarkBERT CoDA outperforms BERT Reddit in precision at k for k ranging from 10 to 20, but is overtaken for higher values of k. The model suggests more specific words related to drugs than BERT. DarkBERT is a language model designed for the Dark Web available only for academic research purposes. It outperforms existing language models and can be used for tasks related to sensitive information in the Dark Web. The model can identify legal and illegal activities and was evaluated on several datasets, achieving high performance. The authors suggest that their model can be useful for law enforcement agencies and cybersecurity researchers to monitor and detect illegal activities in the dark web. The DarkBERT language model was created for the Dark Web, filtering out pages with low information density and excluding sensitive information to comply with ethical guidelines. The DarkBERT Language Model was developed for the Dark Web using a pretraining corpus that excluded pages with low information density. The model uses 10 predefined categories from CoDA to categorize pages in the Dark Web and excludes the “Others” category. Category balancing was addressed to avoid bias towards certain activities, and per-page character count statistics were measured to remove pages with low information density. The final pretraining corpus consisted of 5.43 million pages. The model preprocesses text by masking IP addresses, URLs, and cryptocurrency addresses. Lengthy words (over 100 characters) are excluded from the pretraining corpus, and email addresses are often masked using text preprocessing. The model removes non-ASCII characters and uncommon characters in contemporary English to reduce noise during tokenization. DarkBERT can identify phrases specific to the Dark Web and correctly classify pages that contain them. The model masks Bitcoin, Ethereum, and Litecoin addresses as these three cryptocurrencies are among the most popular in the Dark Web. The DarkBERT language model was evaluated on the CoDA cased dataset using confusion matrices and compared to BERT cased and RoBERTa models. Hyperparameters for ransomware leak site detection and noteworthy thread detection are in Table 12. The classification pipeline used k-fold cross-validation (k=5) and fully-connected classification layers with an early stopping strategy. Evaluation was performed on raw and preprocessed inputs. Repeated k-fold validation (k=5) was used for each model due to the limited dataset size. Noteworthy thread detection also used this method. A leak site page sample can be seen in Figure 7.
2104 word summary
The DarkBERT language model for the dark web was evaluated on the CoDA cased dataset using confusion matrices. The model was compared to BERT cased and RoBERTa models. The hyperparameters used in ransomware leak site detection and noteworthy thread detection can be found in Table 12. The same classification pipeline as activity classification was used with k-fold cross-validation (k=5) and fully-connected classification layers on top of the [CLS] token. An early stopping strategy was utilized to avoid overfitting. The evaluation was performed on both raw and preprocessed inputs. An example data sample used for this task can be seen in Figure 6 and additional details on results. Due to the limited size of the dataset, repeated k-fold validation (k=5) was used for each model and variations in performance per run were averaged. Noteworthy thread detection also adopted this method. A leak site page sample in the dataset can be seen in Figure 7. DarkBERT is a language model designed for the Dark Web that can correctly classify pages containing activity-specific terms. Unlike BERT and RoBERTa, DarkBERT can identify phrases specific to the Dark Web and correctly classify pages that contain them. The model contains many domain-specific jargons that most pages misclassified by BERT and RoBERTa are correctly classified by. DarkBERT is trained on a machine with four NVIDIA A100 80GB GPUs and takes about 15 days to run. The model masks Bitcoin, Ethereum, and Litecoin addresses as these three cryptocurrencies are among the most popular in the Dark Web. While cryptocurrencies are secure by design and provide pseudonymity, they have been involved in illegal underground operations in the Dark Web. The DarkBERT language model is designed for the dark web, where cryptocurrency addresses and non-standard characters are common. The model removes non-ASCII characters and uncommon characters in contemporary English to reduce noise during tokenization. Lengthy words, such as cryptocurrency addresses, are masked to prevent misidentification. The model also classifies words with hash-like values as lengthy and masks them. The pretraining corpus has a unique word length distribution, and manual inspection reveals specific word lengths appear more frequently at higher levels. Lengthy words are masked with an identifier mask token, and executable content is removed from the text. The DarkBERT language model was developed for the Dark Web. The model preprocesses text by masking IP addresses, URLs, and cryptocurrency addresses. File names are not processed separately. Lengthy words (over 100 characters) are excluded from the pretraining corpus. Email addresses are often masked using text preprocessing. Two identifier types for URLs are masked, onion domain and non-onion domain addresses. All email addresses are masked, and some may include strings that can be traced to a single individual. The DarkBERT Language Model for Dark Web is discussed in this document. The implementation involves using identifier masks for text processing and preprocessing the pretraining corpus. Categories such as pornography make up a large fraction of all categories on the Dark Web. Deduplication and category balancing were performed to reduce the data size. Categories such as gambling and arms/weapons had deduplication rates of less than 10%. The pretraining corpus statistics after applying deduplication and category balancing are given in Table 9. The DarkBERT Language Model was developed for the Dark Web using a pretraining corpus that was filtered by character count to exclude pages with low information density. The model uses 10 predefined categories from CoDA to categorize pages in the Dark Web, and excludes the "Others" category due to misclassification errors. The model was implemented by finetuning the bert-base-uncased model from the Hugging Face library with the CoDA Dark Web text corpus. Category balancing was addressed to avoid bias towards certain activities, and per-page character count statistics were measured to remove pages with low information density. The final pretraining corpus consisted of 5.43 million pages. The DarkBERT language model was created for the Dark Web, filtering out pages with low information density and excluding sensitive information to comply with ethical guidelines. The model focuses on pages with abnormally high or low character counts, which are useful in representing the Dark Web. The document includes additional details on data filtering and references to related research. The DarkBERT Language Model for the Dark Web is a pre-trained language model that can identify and classify illegal activities in the dark web, such as drug trafficking, money laundering, and human trafficking. The model was trained on a large corpus of dark web data and uses a deep bidirectional transformer architecture similar to BERT. The model was evaluated on several datasets and achieved high performance in identifying illegal activities. The authors also compared their model to other state-of-the-art models and found that their model outperformed them in most cases. The authors suggest that their model can be useful for law enforcement agencies and cybersecurity researchers to monitor and detect illegal activities in the dark web. DarkBERT is a language model for the Dark Web that can identify legal and illegal activity. It was developed by Mhd Wesam Al-Nabki, Eduardo Fidalgo, and Enrique Alegre. DarkBERT requires task-specific data to fine-tune the model for specific tasks such as Ransomware Leak Site Detection and Thread Detection. The pretraining corpus for DarkBERT is primarily in English, making it limited for non-English tasks. The authors suggest building a multilingual language model for the Dark Web domain. The publicly available datasets for DarkBERT are limited, and additional research on tasks that do not have readily available datasets may be necessary. DarkBERT is a language model designed for the Dark Web and is available only for academic research purposes. The model has been trained on Dark Web datasets and is sensitive to ethical considerations. The preprocessed version of DarkBERT will be released during the conference, and both DUTA and CoDA are available upon request. The model has been tested on fill-mask and synonym inference tasks, and sensitive information has been masked to avoid any malpractices. The automated web crawler takes caution not to expose itself to any sensitive media. DarkBERT outperforms existing language models and can be used for tasks related to sensitive information in the Dark Web. DarkBERT Language Model for Dark Web is a study that compares three language models: DarkBERT CoDA, BERT CoDA, and BERT Reddit. The study evaluates how each language model produces keyword sets semantically related to drugs in the Dark Web. The evaluation is done using precision at k (P @k), where k ranges from 10 to 50. The ground truth data used in the study are from a sample dataset provided by Zhu et al. (2021). The dataset is composed of ground truth data (i.e., drug names and their euphemisms) and sentences containing the drug names. The study shows that DarkBERT CoDA outperforms BERT Reddit in precision at k for k ranging from 10 to 20, but is overtaken for higher values of k. The study also shows that DarkBERT CoDA suggests more specific words related to drugs while BERT suggests general words. In addition, the study provides a sample drug sales page from the Dark Web in which a user advertises a Dutch MDMA pill with a Philipp Plein logo. The DarkBERT language model is effective in identifying threats on the Dark Web using a fill-mask approach that captures semantically related keywords. The model's performance is influenced by the viewpoint of the language models and the noteworthiness of threads. The performance of DarkBERT is higher than other language models in detecting noteworthy threads, but thread detection is still a challenging task. The dataset used for training includes 249 positive and 1624 negative threads, and annotators achieve substantial agreement in selecting noteworthy threads. The study focuses on activities targeting popular software or organizations, sharing sensitive or private information, and distributing critical malware or vulnerabilities. The DarkBERT Language Model was developed to detect noteworthy threads on the Dark Web, which can potentially cause damage to victims, by analyzing activities in hacking forums. To create a dataset of noteworthy threads, two researchers were recruited from the cybersecurity industry to annotate threads on the Dark Web. The detection of noteworthy threads is a highly subjective task, and DarkBERT outperforms other language models in this task. DarkBERT uses RoBERTa as a base model and performs better with preprocessed input data than raw input data. The training data consists of 105 positive and 679 negative examples, and the model is trained using 5-fold cross validation. The training data only includes Dark Web pages that are classified as Cryptocurrency, Financial, and Others. The DarkBERT Language Model for Dark Web is a pre-trained language model that can be used for cybersecurity and CTI applications on the Dark Web. One use case is ransomware leak site detection, where the model can identify whether a given page is a leak site or not. The model's performance was compared to other language models such as BERT and RoBERTa. Leak sites are mostly classified under categories like Pornography and Gambling, and pages with content similar to that of leak sites were selected to create negative data for training the model. The model's effectiveness was demonstrated in various experiments, and its performance was found to be better than other language models. The DarkBERT language model was evaluated for its ability to classify dark web activities. The model showed high similarity of pages in categories such as drugs, electronics, and gambling, but some categories had varying classification accuracy. The model was compared to other language models and performed relatively well. The evaluation was conducted on two datasets, DUTA and CoDA, with two variants each: cased and uncased. DarkBERT outperformed other models for both datasets. The experiment also tested the effect of letter case on classification performance and found that an uncased model performed similarly to a cased model. The DarkBERT language model was developed for text classification on the Dark Web. The model was trained on two datasets: DUTA and CoDA, which were preprocessed to remove empty pages and categories with low page counts. The distribution of various activities on the Dark Web was studied, and a benchmark experiment was conducted to evaluate the model's performance. The pretraining text corpus used for DarkBERT was fed to RoBERTa, and the training losses for the two models were compared. Two versions of DarkBERT were built, one with raw text data and one with preprocessed text. The DarkBERT language model was created for the Dark Web. The construction process involved data collection, filtering, and text preprocessing. The pretraining corpus was filtered and preprocessed to address ethical considerations, such as removing sensitive information. The model was trained using the English texts collected from the Dark Web. Two variations of the text corpus were used for pretraining purposes, raw and preprocessed. The pretraining process took approximately 15 days. A domain-specific pretrained language model like DarkBERT may effectively reduce performance issues in Dark Web tasks. The article presents DarkBERT, a language model pretrained on a Dark Web corpus, which outperforms other pretrained language models on Dark Web-specific tasks related to cybersecurity. The article provides new datasets and potential use cases for DarkBERT and demonstrates its effectiveness in detecting threats, dark web forum thread detection, leak site detection, and ransomware activity. The linguistic differences between the Surface Web and the Dark Web are explored, and DarkBERT is shown to be capable of representing the language used in the Dark Web domain. The article compares DarkBERT to other widely used pretrained language models and illustrates the DarkBERT pretraining process. The article concludes that DarkBERT would prove valuable in ongoing efforts to handle cyber threats in the Dark Web domain. The DarkBERT language model has been developed specifically for the Dark Web due to the linguistic differences compared to the Surface Web. The use of natural language processing (NLP) techniques has become an integral part of cybersecurity and threat intelligence (CTI) research. The Dark Web is a valuable resource for CTI research, but it requires specialized models to handle the extreme lexical and structural diversity of the data. DarkBERT outperforms other language models in specific use cases and offers valuable insights to researchers. The model was trained on Dark Web data and takes into account the steps taken to filter and compile the text. DarkBERT is a language model designed for the Dark Web. It is specifically trained to understand the language used in illegal online activities such as drug trafficking, weapons sales, and human trafficking. The model uses a combination of unsupervised and supervised learning techniques to achieve a high level of accuracy in understanding the unique language patterns of the Dark Web. DarkBERT has the potential to be used by law enforcement agencies to monitor and detect criminal activity on the Dark Web.