With the rising sophistication of cyber threats, the utilization of machine learning algorithms for cyber threat intelligence (CTI) has become increasingly crucial. This research presents a comprehensive comparative analysis of various deep learning (DL) algorithms, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Generative Adversarial Networks (GAN), and Bidirectional Encoder Representations from Transformers (BERT), in the context of CTI using open-source intelligence (OSINT) data. A specific dataset encompassing NER, sentiment analysis, text classification, and information extraction tasks was employed to evaluate the algorithms' performance. The comparison was based on a set of well-established metrics, such as task flexibility, training data requirements, training time, accuracy, precision, and F1-score. The paper results unveiled that while CNN, RNN, and LSTM demonstrated competitive performance in certain tasks, BERT consistently outperformed the other algorithms across multiple metrics and NLP tasks. BERT's superior performance can be attributed to its contextualized word embeddings and advanced attention mechanisms that effectively capture intricate relationships in text.

Comparative analysis, deep learning algorithms, cyber threat intelligence, OSINT, NER, LSTM, information extraction, training time, accuracy, BERT.