SecureBERT: A Domain-Specific Language Model for Cybersecurity (2204.02685v3)

Abstract: NLP has recently gained wide attention in cybersecurity, particularly in Cyber Threat Intelligence (CTI) and cyber automation. Increased connection and automation have revolutionized the world's economic and cultural infrastructures, while they have introduced risks in terms of cyber attacks. CTI is information that helps cybersecurity analysts make intelligent security decisions, that is often delivered in the form of natural language text, which must be transformed to machine readable format through an automated procedure before it can be used for automated security measures. This paper proposes SecureBERT, a cybersecurity LLM capable of capturing text connotations in cybersecurity text (e.g., CTI) and therefore successful in automation for many critical cybersecurity tasks that would otherwise rely on human expertise and time-consuming manual efforts. SecureBERT has been trained using a large corpus of cybersecurity text.To make SecureBERT effective not just in retaining general English understanding, but also when applied to text with cybersecurity implications, we developed a customized tokenizer as well as a method to alter pre-trained weights. The SecureBERT is evaluated using the standard Masked LLM (MLM) test as well as two additional standard NLP tasks. Our evaluation studies show that SecureBERT\footnote{\url{https://github.com/ehsanaghaei/SecureBERT}} outperforms existing similar models, confirming its capability for solving crucial NLP tasks in cybersecurity.

• The paper introduces SecureBERT, which adapts RoBERTa by integrating a customized cybersecurity tokenizer and applying Gaussian noise to weights for capturing domain-specific nuances.
• SecureBERT significantly outperforms larger models like RoBERTa-large on MLM tasks while achieving competitive results in sentiment analysis and NER.
• The methodological advances offer promising strategies for developing specialized language models that enhance automation in cybersecurity threat intelligence.

Analysis of SecureBERT: A Domain-Specific LLM for Cybersecurity

The paper "SecureBERT: A Domain-Specific LLM for Cybersecurity" proposes a LLM tailored for extracting meaningful insights from cybersecurity-related texts, an advancement that addresses the crucial need for automation in cybersecurity practices. The foundation of SecureBERT is built on the architectural principles of RoBERTa, a variant of BERT known for its robust performance on general language tasks, adapted for specificity in cyber threat intelligence and automation.

Methodological Advances

SecureBERT introduces two primary methodological innovations to tailor its capabilities toward cybersecurity:

1. Customized Tokenizer:
   • Recognizing the distinct vocabulary within cybersecurity texts, SecureBERT employs a customized tokenizer. This approach preserves essential tokens from general English while integrating domain-specific tokens. Consequently, this enhances the model's ability to process cybersecurity terms effectively, which might otherwise be overlooked or misinterpreted by conventional LLMs.
2. Weight Adjustments Through Noise Introduction:
   • To counteract potential overfitting and enhance the learning of domain-specific nuances, the model applies Gaussian noise to pre-trained weights. This strategy facilitates more efficient weight optimization, allowing SecureBERT to balance general language understanding with domain-specific knowledge, especially when dealing with cybersecurity homographs.

Numerical Outcomes

The efficacy of SecureBERT was demonstrated through rigorous evaluations via the Masked LLM (MLM) task, sentiment analysis, and named entity recognition (NER):

• Masked LLM (MLM): SecureBERT significantly surpasses RoBERTa-large, a model with substantially more parameters, in predicting cybersecurity masked nouns and verbs. This illustrates its superior capability in contextual understanding within cybersecurity literature.
• Sentiment Analysis and NER: SecureBERT's performance on sentiment analysis using a general English dataset and on NER with a cybersecurity-focused dataset shows its versatility. The model performs competently, rivaling similar models trained specifically for such broader NLP tasks, underscoring its applicability beyond the cybersecurity domain.

Implications for Cybersecurity and NLP

SecureBERT represents a critical advancement in the automation of cybersecurity analysis, enhancing the precision and efficiency of threat intelligence extraction and vulnerability identification. This model not only facilitates the processing of cybersecurity texts but also exhibits adaptability, enabling its application in general language tasks.

The methodological refinements presented, particularly the domain-specific tokenizer and noise-adapted weight adjustments, offer insights for future research in creating specialized LLMs across various sectors. Subsequent advancements could further refine these techniques to optimize LLMs for other sensitive domains requiring robust NLP applications.

Future Prospects

The development of SecureBERT opens avenues for integrating LLMs more effectively within cybersecurity frameworks. Future research could explore enhanced data augmentation techniques to further improve model robustness and coverage over evolving threat landscapes. Additionally, expanding the corpus to include emerging cybersecurity threats could enhance the model's predictive capabilities. This work sets a precedent for similar initiatives in other specialized domains where language and terminologies significantly diverge from general English.