Presentation Material

AI Generated Summary^{may contain errors}

The speaker is discussing the use of machine learning algorithms, , specifically Support Vector Machines (SVMs), for malware analysis. The goal is to classify applications as malicious or non-malicious based on their functional call graphs.

Here are the key points:

1. Classification challenges: The speaker notes that there are high possibilities of misclassification due to the hard classification nature of SVMs, and suggests using soft classification methods to reduce misclassifications.
2. Margin definition: The margin around a plane is defined as the distance between the plane and the nearest data point (support vector). Expanding this margin can improve classification accuracy.
3. Mechanism used: The speaker explains that they used SVMs to classify applications based on their functional call graphs, achieving an accuracy of 78% with a 3% false positive rate.
4. Challenges and limitations: The speaker notes that there are cases where the margins are small, making it difficult to classify applications accurately. Additionally, officiation techniques can affect the accuracy of the SVM model.
5. Conclusion: Machine learning algorithms like SVMs can be used for malware analysis, but they should be considered as a complementary feature to dynamic analysis, rather than a standalone solution.

Overall, the speaker highlights the importance of careful feature selection and parameter tuning in machine learning-based malware analysis, and notes that further research is needed to improve accuracy and address limitations.