BERT-Based Sentiment Forensics Analysis for Intrusion Detection

The need for effective intrusion detection and user behavior analytics in cybersecurity has reached unprecedented levels. By leveraging the power of BERT, a pre-trained language model known for its contextual understanding, the goal is to uncover latent patterns and insights within textual data to identify potential threats and anomalous user behavior. This study aims to further advance network analysis capabilities by integrating BERT (Bidirectional Encoder Representations from Transformers) for fine-tuning an Long Short-Term Memory (LSTM) model with attention mechanisms. By incorporating attention mechanisms, these models intelligently prioritize and focus on relevant parts of the input data, allowing them to capture corpus in our linguistic resource and improve overall performance. The study explores advanced modeling techniques to gain the emotional tone in proactive intrusion detection. We achieved 92% accuracy and precision of 89% on our sentiment-based model into traffic analysis and network passive attacks.