Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs (2505.06477v1)

Abstract: Safety-critical applications such as healthcare and autonomous vehicles use deep neural networks (DNN) to make predictions and infer decisions. DNNs are susceptible to evasion attacks, where an adversary crafts a malicious data instance to trick the DNN into making wrong decisions at inference time. Existing defenses that protect DNNs against evasion attacks are either static or dynamic. Static defenses are computationally efficient but do not adapt to the evolving threat landscape, while dynamic defenses are adaptable but suffer from an increased computational overhead. To combine the best of both worlds, in this paper, we propose a novel risk profiling framework that uses a risk-aware strategy to selectively train static defenses using victim instances that exhibit the most resilient features and are hence more resilient against an evasion attack. We hypothesize that training existing defenses on instances that are less vulnerable to the attack enhances the adversarial detection rate by reducing false negatives. We evaluate the efficacy of our risk-aware selective training strategy on a blood glucose management system that demonstrates how training static anomaly detectors indiscriminately may result in an increased false negative rate, which could be life-threatening in safety-critical applications. Our experiments show that selective training on the less vulnerable patients achieves a recall increase of up to 27.5\% with minimal impact on precision compared to indiscriminate training.

An Essay on "Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs"

The paper "Learning from the Good Ones: Risk Profiling-Based Defenses Against Evasion Attacks on DNNs" proposes a novel framework for enhancing the resilience of deep neural networks (DNNs) within safety-critical applications against evasion attacks. This work is of significant interest to researchers in the domain of security and machine learning, especially those focused on the robust deployment of DNNs in environments such as healthcare and autonomous vehicles.

The susceptibility of DNNs to adversarial attacks, particularly evasion attacks, poses challenges in systems where errors can lead to severe or even life-threatening consequences. Evasion attacks are known for deceiving DNNs during inference by covertly altering inputs, seriously impacting model accuracy—often without any indication of abnormal inputs to traditional anomaly detectors.

The authors tackle the inefficiencies of existing static DNN defenses, which, while computationally efficient, remain inflexible to evolving threat landscapes. Conversely, dynamic defenses adapt to various attack strategies but at the cost of increased computational overhead. The paper introduces a risk-aware selective training strategy to leverage the robustness of static defenses by evaluating and training on data instances less prone to adversarial manipulation. The key proposition is that a defense mechanism trained with instances demonstrating smaller normal-to-adversarial deviations can achieve better generalization, subsequently improving attack detection rates.

For empirical evaluation, the paper employs a case paper centered around a blood glucose management system, a vital healthcare application where incorrect predictions can have grave consequences. Utilizing the OhioT1DM dataset, the authors demonstrate that indiscriminately trained detectors result in varying false negative rates among different patients. Their proposed framework clusters individuals based on their vulnerability to evasion attacks, thus providing a basis for selective training.

A hierarchical clustering approach is utilized to identify less vulnerable patients, underlining the importance of training anomaly detectors using data from these more resilient patients. Results show that selective training enhances recall by up to 27.5% with minimal impact on precision, thereby reducing false negatives—a crucial metric in safety-critical systems.

Furthermore, the implications of risk-aware selective training extend beyond practical improvements to highlight theoretical insights into tailored defense strategies. By minimizing false negatives, this methodology underscores the importance of dataset robustness, aligning model performance with real-world adversarial dynamics. Future developments may include adapting this framework for other sensitive domains, exploring proactive detection under concept drifts, and refining risk metrics with more nuanced severity coefficients.

The paper's limitations include its reliance on offline training, which may miss potential future dataset shifts and concept drift. Additionally, determining risk metrics such as severity coefficients and comprehensive validation across diverse datasets and attack algorithms are potential areas for refinement.

Overall, this paper offers notable contributions to defending DNNs against evasion attacks, showcasing a promising paradigm shift in security strategy through adaptive yet efficient profiling and training techniques. Continued exploration and broader application of such methodologies are expected to propel advancements in both anomaly detection precision and systemic resilience across varied domains.