“LSTM and BERT Based Transformers Models for Cyber Threat Intelligence for Intent Identification of Social Media Platforms Exploitation from Darknet Forums”, 2024-08-14 (; similar):
Cybercriminals, terrorists, political activists, whistleblowers, and others are drawn to the darknet market and its use for illicit purposes. Various methods are employed to identify the people who are behind these identities and websites. Since darknet marketplaces (DNMs) are more recent than other platforms, there are more unexplored research possibilities in this field. Research has been done to identify the buying and selling of products connected to hacking from dark net marketplaces, the promotion of cyber threats in hackers’ forums and DNMs, and the supply chain elements of content related to cyber threats.
The proposed research covers one of the most promising research areas: darknet markets and social media platforms exploitation tools and strategies. The research uses 6 DNMs’ publicly available data [Apollon, CannaHome, Cannazon, Cryptonia, Empire, and Samsara] and then identifies the most popular social media platform and intent of discussion based on the interaction available in the form of user remarks and comments. The research caters to the social media platform and cybercrimes or threats associated with them, with the help of machine learning algorithms such as Logistic Regression, Random Forest Classifier, Gradient Boosting Classifier, K-Neighbors Classifier, XGBoost Classifier, Voting Classifier, and a Deep Learning-based model with LSTM and Transformer-based models used.
In existing research, natural language processing techniques were employed to identify the kinds of commodities exchanged in these markets, while machine learning approaches were used to classify product descriptions. In the proposed research work, an advanced and lighter version of BERT and the LSTM model are used, yielding accuracies of 90.12% and 91.35% respectively. LSTM performed best to extract multiclass classification of actual intention of social media usage by intelligent analysis on hackers’ discussions.
Strategies on social media platforms such as Facebook, Twitter, Instagram, and Snapchat to exploit them using darknet platforms are also explored. This paper contributes to cyber threat intelligence that leverages social media applications to work proactively to save their assets based on the threats identified in the darknet.
[Keywords: social media crimes, CTI, BERT, LSTM, machine learning, Transformer based model]