The use of Machine Learning for malware detection is essential to counter the massive growth in malware types compared with the traditional signature-based detection system. However, machine learning models could also be extremely vulnerable and sensible to transferable adversarial example (AE) attacks. The transfer AE attack does not require extra information from the victim model such as gradient information. Researchers explore mainly 2 lines of transfer-based adversarial example attacks: ensemble models and ensemble samples. \\ Although comprehensive innovations and progress have been achieved in transfer AE attacks, few works have investigated how these techniques perform in malware data. Besides, generating adversarial examples on an android APK file is not as easy and convenient as it is on image data since the generated AE of malware should also remain its functionality and executability after perturbation. Therefore, it is urgent to validate whether previous methodologies could still have their effect on malware considering the differences compared to image data. \\ In this thesis, we first have a thorough literature review for the AE attacks on malware data and general transfer AE attacks. Then we design our algorithm for the transfer AE attack. We formulate the optimization problem based on the intuition that the contribution evenness of features towards the final prediction result is highly correlated to the AE transferability. We then solve the optimization problem by gradient descent and evaluate it through extensive experiments. Implementing and experimenting with the state-of-the-art AE algorithms and transferability enhancement techniques, we analyze and summarize the weaknesses and strengths of each method. / Master of Science / Machine learning models have been widely applied to malware detection systems in recent years due to the massive growth in malware types. However, these models are vulnerable to adversarial attacks. Malicious attackers can add some small imperceptible perturbations to the original testing samples and mislead the classification results at a very low cost. Research on adversarial attacks would help us gain a better understanding of the attacker's side and inspire defenses against them.
Among all adversarial attacks, the transfer-based adversarial example attack is one of the most devastating attacks since it does not require extra information from the targeted victim model such as gradient information or query from the model.
Although plenty of researchers has explored the transfer AE attack lately, few works focus on malware (e.g., Android) data. Compared with image data, perturbing malware is more complicated and challenging since the generated adversarial examples of malware need to remain functionality and executability. To validate how transfer AE attack methods perform on malware, we implement the state-of-the-art (SOTA) works in this thesis and experiment with them on real Android data. Besides, we develop a new transfer-based AE attack method based on the contribution of each feature for generating AE. We then do comprehensive evaluations and draw comparisons between SOTA works and our proposed method.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/110070 |
| Date | 12 May 2022 |
| Creators | Hu, Yang |
| Contributors | Computer Science, Lou, Wenjing, Lu, Chang Tien, Chen, Yimin |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | In Copyright, http://rightsstatements.org/vocab/InC/1.0/ |
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