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Register a new userOn Training Robust PDF Malware Classifiers
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Added by
Yizheng Chen
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Although state-of-the-art PDF malware classifiers can be trained with almost perfect test accuracy (99%) and extremely low false positive rate (under 0.1%), it has been shown that even a simple adversary can evade them. A practically useful malware classifier must be robust against evasion attacks. However, achieving such robustness is an extremely challenging task. In this paper, we take the first steps towards training robust PDF malware classifiers with verifiable robustness properties. For instance, a robustness property can enforce that no matter how many pages from benign documents are inserted into a PDF malware, the classifier must still classify it as malicious. We demonstrate how the worst-case behavior of a malware classifier with respect to specific robustness properties can be formally verified. Furthermore, we find that training classifiers that satisfy formally verified robustness properties can increase the evasion cost of unbounded (i.e., not bounded by the robustness properties) attackers by eliminating simple evasion attacks. Specifically, we propose a new distance metric that operates on the PDF tree structure and specify two classes of robustness properties including subtree insertions and deletions. We utilize state-of-the-art verifiably robust training method to build robust PDF malware classifiers. Our results show that, we can achieve 92.27% average verified robust accuracy over three properties, while maintaining 99.74% accuracy and 0.56% false positive rate. With simple robustness properties, our robust model maintains 7% higher robust accuracy than all the baseline models against unrestricted whitebox attacks. Moreover, the state-of-the-art and new adaptive evolutionary attackers need up to 10 times larger $L_0$ feature distance and 21 times more PDF basic mutations (e.g., inserting and deleting objects) to evade our robust model than the baselines.
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Modern commercial antivirus systems increasingly rely on machine learning to keep up with the rampant inflation of new malware. However, it is well-known that machine learning models are vulnerable to adversarial examples (AEs). Previous works have shown that ML malware classifiers are fragile to the white-box adversarial attacks. However, ML models used in commercial antivirus products are usually not available to attackers and only return hard classification labels. Therefore, it is more practical to evaluate the robustness of ML models and real-world AVs in a pure black-box manner. We propose a black-box Reinforcement Learning (RL) based framework to generate AEs for PE malware classifiers and AV engines. It regards the adversarial attack problem as a multi-armed bandit problem, which finds an optimal balance between exploiting the successful patterns and exploring more varieties. Compared to other frameworks, our improvements lie in three points. 1) Limiting the exploration space by modeling the generation process as a stateless process to avoid combination explosions. 2) Due to the critical role of payload in AE generation, we design to reuse the successful payload in modeling. 3) Minimizing the changes on AE samples to correctly assign the rewards in RL learning. It also helps identify the root cause of evasions. As a result, our framework has much higher black-box evasion rates than other off-the-shelf frameworks. Results show it has over 74%--97% evasion rate for two state-of-the-art ML detectors and over 32%--48% evasion rate for commercial AVs in a pure black-box setting. We also demonstrate that the transferability of adversarial attacks among ML-based classifiers is higher than the attack transferability between purely ML-based and commercial AVs.
For the dramatic increase of Android malware and low efficiency of manual check process, deep learning methods started to be an auxiliary means for Android malware detection these years. However, these models are highly dependent on the quality of datasets, and perform unsatisfactory results when the quality of training data is not good enough. In the real world, the quality of datasets without manually check cannot be guaranteed, even Google Play may contain malicious applications, which will cause the trained model failure. To address the challenge, we propose a robust Android malware detection approach based on selective ensemble learning, trying to provide an effective solution not that limited to the quality of datasets. The proposed model utilizes genetic algorithm to help find the best combination of the component learners and improve robustness of the model. Our results show that the proposed approach achieves a more robust performance than other approaches in the same area.
Malware scanners try to protect users from opening malicious documents by statically or dynamically analyzing documents. However, malware developers may apply evasions that conceal the maliciousness of a document. Given the variety of existing evasions, systematically assessing the impact of evasions on malware scanners remains an open challenge. This paper presents a novel methodology for testing the capability of malware scanners to cope with evasions. We apply the methodology to malicious Portable Document Format (PDF) documents and present an in-depth study of how current PDF evasions affect 41 state-of-the-art malware scanners. The study is based on a framework for creating malicious PDF documents that use one or more evasions. Based on such documents, we measure how effective different evasions are at concealing the maliciousness of a document. We find that many static and dynamic scanners can be easily fooled by relatively simple evasions and that the effectiveness of different evasions varies drastically. Our work not only is a call to arms for improving current malware scanners, but by providing a large-scale corpus of malicious PDF documents with evasions, we directly support the development of improved tools to detect document-based malware. Moreover, our methodology paves the way for a quantitative evaluation of evasions in other kinds of malware.
Malware analysis has been extensively investigated as the number and types of malware has increased dramatically. However, most previous studies use end-to-end systems to detect whether a sample is malicious, or to identify its malware family. In this paper, we propose a neural network framework composed of an embedder, an encoder, and a filter to learn malware representations from characteristic execution sequences for malware family classification. The embedder uses BERT and Sent2Vec, state-of-the-art embedding modules, to capture relations within a single API call and among consecutive API calls in an execution trace. The encoder comprises gated recurrent units (GRU) to preserve the ordinal position of API calls and a self-attention mechanism for comparing intra-relations among different positions of API calls. The filter identifies representative API calls to build the malware representation. We conduct broad experiments to determine the influence of individual framework components. The results show that the proposed framework outperforms the baselines, and also demonstrates that considering Sent2Vec to learn complete API call embeddings and GRU to explicitly preserve ordinal information yields more information and thus significant improvements. Also, the proposed approach effectively classifies new malicious execution traces on the basis of similarities with previously collected families.
We present BPFroid -- a novel dynamic analysis framework for Android that uses the eBPF technology of the Linux kernel to continuously monitor events of user applications running on a real device. The monitored events are collected from different components of the Android software stack: internal kernel functions, system calls, native library functions, and the Java API framework. As BPFroid hooks these events in the kernel, a malware is unable to trivially bypass monitoring. Moreover, using eBPF doesnt require any change to the Android system or the monitored applications. We also present an analytical comparison of BPFroid to other malware detection methods and demonstrate its usage by developing novel signatures to detect suspicious behavior that are based on it. These signatures are then evaluated using real apps. We also demonstrate how BPFroid can be used to capture forensic artifacts for further investigation. Our results show that BPFroid successfully alerts in real time when a suspicious behavioral signature is detected, without incurring a significant runtime performance overhead.
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