Adversarial Feature Selection against Evasion Attacks (2005.12154v1)

Abstract: Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion and malware detection, although their security against well-crafted attacks that aim to evade detection by manipulating data at test time has not yet been thoroughly assessed. While previous work has been mainly focused on devising adversary-aware classification algorithms to counter evasion attempts, only few authors have considered the impact of using reduced feature sets on classifier security against the same attacks. An interesting, preliminary result is that classifier security to evasion may be even worsened by the application of feature selection. In this paper, we provide a more detailed investigation of this aspect, shedding some light on the security properties of feature selection against evasion attacks. Inspired by previous work on adversary-aware classifiers, we propose a novel adversary-aware feature selection model that can improve classifier security against evasion attacks, by incorporating specific assumptions on the adversary's data manipulation strategy. We focus on an efficient, wrapper-based implementation of our approach, and experimentally validate its soundness on different application examples, including spam and malware detection.

• The paper introduces an adversary-aware feature selection model that enhances classifier resistance against evasion attacks.
• It shows that non-adversarial feature reduction can inadvertently simplify attackers' efforts to degrade performance.
• The wrapper-based implementation is validated on spam filtering and PDF malware detection, demonstrating sustained true positive rates under attack.

Overview of the Paper: Adversarial Feature Selection against Evasion Attacks

This paper presents an in-depth examination of feature selection in the context of adversarial settings, particularly focusing on evasion attacks. It recognizes an existing gap in literature concerning the impact of feature selection mechanisms on the robustness of classifiers subjected to adversarial manipulations. While prior efforts largely concentrated on creating adversary-aware classification techniques, this paper shifts the focus towards the role of feature selection in enhancing classifier security.

The authors introduce a novel adversary-aware feature selection model aimed at improving resistance against evasion attacks. This model is rooted in a wrapper-based methodology that integrates assumptions regarding the adversary's data manipulation strategies into the feature selection process.

Key Contributions

1. Adversary-aware Feature Selection Model: This model optimizes a trade-off between the typical generalization performance and the security of the classifier. The objective is constructed such that it maximizes classifier robustness against test-time manipulations by intelligent adversaries.
2. Effect of Reduced Feature Sets: The paper highlights a possibly counterintuitive insight that feature reduction, if not done adversarially, can more easily be exploited by attackers. This is because using fewer features can inadvertently lower the manipulative efforts required by adversaries to degrade classifier accuracy.
3. Wrapper-based Implementation: A wrapper-based implementation of the feature selection model is detailed, leveraging algorithms such as forward selection and backward elimination. The process involves simulating adversarial attacks to dynamically assess feature utility in the context of security as well as performance.
4. Empirical Validation: The model's performance is empirically validated on diverse applications including spam filtering and PDF malware detection. Experiments consistently demonstrate that the proposed adversarial feature selection strategy yields classifiers that maintain higher true positive rates under adversarial attack scenarios compared to traditional feature selection approaches.

Experimental Insights

The paper provides comprehensive evaluations of the adversarial feature selection model. A consistent observation is that while accuracy in benign settings may not drastically differ from traditional methods, the critical advantage lies in the enhanced robustness against attacks characterized by feature manipulations that aim to alter decision boundaries.

For instance, in spam filtering scenarios, classifiers configured through adversary-aware feature selection generally sustain effectiveness even when spams are strategically obfuscated. Similar results are echoed in PDF malware detection experiments, where the classifiers achieved greater evasion resistance.

Implications

The theoretical and practical implications are profound. For practitioners in adversarial learning, this work underscores the necessity of incorporating adversary considerations into the feature selection pipeline for applications where misclassification costs are substantial, particularly in security-sensitive contexts. The methodology facilitates crafting more resilient machine learning models by acknowledging and preparing for potential attack vectors at the feature selection stage itself.

On a theoretical plane, this paper further enriches the adversarial learning literature by detailing methodologies that systematically tackle vulnerabilities arising from conventional feature selection – a dimension not widely explored before.

Future Directions

This paper opens avenues for subsequent research, particularly in constructing filter-based adversarial feature selection methods that could offer computational efficiency by circumventing the arduous necessity for complete wrapper evaluations. Moreover, extending the presented frameworks to cover a broader spectrum of attacks – like data poisoning or model inversion – could make these methodologies more encompassing. Further exploration could also contemplate hybrid approaches that synergize the strengths of filter and wrapper feature selectors under adversarial threat models.

In conclusion, the paper presents a significant stride towards fortifying machine learning systems against adversarial evasion, a necessity in the increasingly security-centric landscape of contemporary AI applications.