Evasion Attacks against Machine Learning at Test Time (1708.06131v1)

Abstract: In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack scenario, an adversary may attempt to evade a deployed system at test time by carefully manipulating attack samples. In this work, we present a simple but effective gradient-based approach that can be exploited to systematically assess the security of several, widely-used classification algorithms against evasion attacks. Following a recently proposed framework for security evaluation, we simulate attack scenarios that exhibit different risk levels for the classifier by increasing the attacker's knowledge of the system and her ability to manipulate attack samples. This gives the classifier designer a better picture of the classifier performance under evasion attacks, and allows him to perform a more informed model selection (or parameter setting). We evaluate our approach on the relevant security task of malware detection in PDF files, and show that such systems can be easily evaded. We also sketch some countermeasures suggested by our analysis.

• The paper demonstrates that gradient-based evasion attacks, enhanced with a mimicry term, can effectively generate adversarial examples that resemble legitimate data.
• The study shows that both linear and nonlinear classifiers are vulnerable, with experiments on MNIST and PDF malware detection confirming significant misclassification rates.
• The findings highlight the urgent need for robust countermeasures such as adversarial training and ensemble methods to safeguard security-critical machine learning systems.

Evasion Attacks Against Machine Learning at Test Time

The paper authored by Battista Biggio et al. explores the critical subject of evasion attacks on machine learning systems at test time. This research tackles a systematic approach to evaluate the robustness of widely-used classification algorithms under adversarial conditions. The context of this work lies within security-sensitive applications like spam filtering, malware detection, and network intrusion detection, where the data distribution is often non-stationary due to deliberate manipulations by an adversary.

The intrinsic adversarial nature of these applications necessitates proactive measures to anticipate potential attacks and evaluate classifier security. Traditional machine learning evaluation techniques fall short in these scenarios. This paper introduces a gradient-based approach to assess classifier security against evasion attacks, following a security evaluation framework that simulates varying levels of adversary knowledge and manipulation capabilities.

Methodology

The work leverages a gradient-descent algorithm to optimize attack strategies by minimizing the classifier’s discriminant function subject to constraints reflective of real-world manipulation difficulties. The constraints ensure practical applicability, such as limiting the extent of feature modification based on a specified distance metric. The attack scenarios considered range from perfect knowledge (PK) of the targeted system to limited knowledge (LK), where the adversary approximates the classifier using a surrogate model trained on a limited dataset.

A notable aspect of their methodology is the introduction of a mimicry term to the gradient-descent optimization, which promotes the resemblance of the attack sample to legitimate samples based on a kernel density estimation. This addition is theorized to improve the likelihood of successful evasion by biasing the attack towards regions densely populated by legitimate samples.

Experimental Results

The experiments were conducted on two domains: handwritten digit recognition and PDF malware detection. The results depict that the proposed gradient-based evasion attacks are highly effective across both linear and non-linear classifiers. Key observations include:

1. Handwritten Digit Recognition: On the MNIST dataset, digit "3" was efficiently transformed into a misclassified "7" using gradient attacks. When incorporating the mimicry component, the attack samples more closely resembled legitimate "7" digits, demonstrating the mimicry term’s efficacy in creating visually plausible attack instances.
2. PDF Malware Detection: In a more realistic scenario, the detection of malicious PDF files was tested. Here, linear SVMs were easily circumvented with minimal feature modifications. SVMs with RBF kernels and neural networks showed more resilience but were still susceptible to attacks, particularly under the PK scenario. The simulation also demonstrated that even with limited surrogate data in the LK scenario, attackers could achieve high evasion rates, showcasing the attack strategy’s robustness.

Implications

The results highlight significant vulnerabilities in commonly deployed machine learning classifiers and the potential for practical adversarial attacks. This underscores the necessity for improved security measures in classifier design. Potential countermeasures include regularization terms to enforce tighter enclosures of legitimate samples, model ensemble methods, and adversarial training techniques where generated attack samples are incorporated into the training dataset.

Moreover, the paper indicates the feasibility of extending these attack strategies to classifiers with non-differentiable discriminant functions by employing alternative search heuristics.

Future Work

The paper suggests several avenues for further research:

• Extending the attack to classifiers with non-differentiable discriminant functions, such as decision trees, and exploring the potential of heuristic-based search methods.
• Enhancing the models used to estimate the targeted classifier’s discriminant function in the limited knowledge scenario.
• Evaluating the impact of different surrogate data collection strategies on the success of the attacks.

Conclusion

This paper presents a rigorous analysis of evasion attacks against machine learning systems. By detailing effective attack methodologies and demonstrating their practical implications, it provides a crucial foundation for developing more secure and resilient classifiers. The research serves as a pertinent reminder of the pressing need to integrate security considerations into machine learning model design and evaluation mechanisms.