Blackbox Android Malware Detection Using Machine Learning and Evasion Attacks Techniques
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Presented at DeepSec 2024 by
Over the past ten years, researchers have extensively explored the vulnerability of Android malware detectors to adversarial examples through the development of evasion attacks. Nevertheless, the feasability of these attacks in real-world use case scenarios is debatable. Most of the existing published papers are based on the assumptions that the attackers know the details of the target classifiers used for malware detection.Nevertheless, in reality, malicious actors have limited access to the target classifiers. This talk presents a problem-space adversarial attack designed to effectively evade blackbox Android malware detectors in real-world use case scenarios. The proposed approach constructs a collection of problem-space transformations derived from benign donors that share opcode-level similarity with malware applications through the consideration of an n-gram-based approach. These transformations are then used to present malware instances as legitimate entities through an iterative and incremental manipulation strategy. The presentation will describe a manipulation model that is based on a query-efficient optimization algorithm, which can identify and implement the required sequences of transformations into the malware applications. The model has already been evaluated relative to more than 1,000 malware applications. This demonstrates the effectiveness of the reported approach relative to the generation of real-world adversarial examples in both software and hardware-related scenarios. The experiments that we conducted demonstrate that the proposed model may effectively trick various malware detectors into believing that malware entities are legitimate. More precisely, the proposed model generates evasion rates of 90%–95% relative to data sets like DREBIN, Sec-SVM, ADE-MA, MaMaDroid, and Opcode-SVM. The average number of required computational operations belongs to the range [1..7]. Additionally, it is relevant to note that the proposed adversarial attack preserves its stealthiness against the virus detection core of three popular commercial antivirus softwares. The obtained evasion rate is 87%, which further proves the proposed model’s relevance for real-world use case scenarios.