- Introduction
- Key Features
- Project Objectives
- Dataset Description
- Project Methodology
- Results
- Conclusion
SecureBERT is a specialized language model specifically tailored for cybersecurity tasks. This repository documents the research, development, and implementation of SecureBERT in classifying malware samples using opcode sequences.
- Improved Accuracy: Discuss the high accuracy achieved in malware classification tasks, surpassing traditional machine learning techniques.
- Reduced Feature Engineering: Explain how SecureBERT automates feature extraction, reducing the need for manual engineering.
- Domain Expertise: Highlight SecureBERT's training on a vast corpus of cybersecurity text data, facilitating a deep understanding of the domain.
- Adaptability: Describe how SecureBERT can be fine-tuned for specific malware classification tasks, enhancing its real-world performance.
- Data Preprocessing: Explain the meticulous steps involved in data normalization, tokenization, and handling missing values to suit SecureBERT's requirements.
- Model Fine-tuning: Detail the process of adapting SecureBERT for opcode sequence classification, including parameter adjustments and training phases.
- Robustness Evaluation: Discuss the method used to assess the model's performance against diverse malware families and the metrics utilized for evaluation.
Describe the Malware Classification Dataset used, including the number of files, malware families, and their characteristics.
Explain the comprehensive process of extracting opcode sequences from assembly (.asm) files and the subsequent filtering and merging steps.
Detail the meticulous steps taken to fine-tune SecureBERT for opcode sequence classification, including model initialization, adjustment of the final layer, tokenization, dataset preparation, and training setup.
Describe the evaluation process involving loss and accuracy metrics across epochs, highlighting observed improvements and potential limitations due to computational resources.
Discuss the observed improvements in training accuracy, validation loss, and overall model performance despite limitations in processing a fraction of the available malware files.
Summarize the research findings, emphasizing SecureBERT's potential as a pivotal asset in cybersecurity and the anticipated advancements with improved resources and optimizations.