While the Malicious Discriminative Score (MDS) technique offers significant benefits in improving the precision of malware detection by distinguishing between benign and malicious behaviors, there are potential limitations and challenges when applying this technique to real-world scenarios with highly imbalanced benign-to-malware ratios. Some of the key challenges include:
Imbalanced Dataset: In scenarios where the number of benign instances far outweighs the number of malware instances, the MDS-based technique may struggle to effectively differentiate between common benign behaviors and unique malware behaviors. The model may become biased towards benign instances, leading to a higher false positive rate.
Labeling Accuracy: The accuracy of labeling instances as benign or malware is crucial for the effectiveness of the MDS-based technique. In real-world scenarios, labeling errors or inconsistencies can impact the performance of the model, especially when dealing with imbalanced datasets.
Generalization: The MDS technique may face challenges in generalizing to new and unseen malware behaviors in highly imbalanced datasets. The model's ability to adapt to evolving malware threats and variations may be limited by the imbalance in the dataset.
Computational Complexity: Calculating MDS values for a large number of instances in imbalanced datasets can be computationally intensive and may require significant resources, especially in real-time detection scenarios.
Addressing these limitations and challenges requires careful dataset curation, robust labeling strategies, model tuning for imbalanced data, and continuous monitoring and adaptation of the MDS-based technique to evolving threat landscapes.