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With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topic. Backdoor attacks are a form of adversarial attacks on deep networks where the attacker provides poisoned data to the victim to train the model with, and then activates the attack by showing a specific small trigger pattern at the test time. Most state-of-the-art backdoor attacks either provide mislabeled poisoning data that is possible to identify by visual inspection, reveal the trigger in the poisoned data, or use noise to hide the trigger. We propose a novel form of backdoor attack where poisoned data look natural with correct labels and also more importantly, the attacker hides the trigger in the poisoned data and keeps the trigger secret until the test time. We perform an extensive study on various image classification settings and show that our attack can fool the model by pasting the trigger at random locations on unseen images although the model performs well on clean data. We also show that our proposed attack cannot be easily defended using a state-of-the-art defense algorithm for backdoor attacks.
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On the Robustness of Machine Learning Training in Security Sensitive Environments
Modern machine learning underpins a large variety of commercial software products, including many cybersecurity solutions. Widely different models, from large transformers trained for auto-regressive natural language modeling to gradient boosting forests designed to recognize malicious software, all share a common element: they are trained on an ever increasing quantity of data to achieve impressive performance levels in their tasks. Consequently, the training phase of modern machine learning systems holds dual significance: it is pivotal in achieving the expected high-performance levels of these models, and concurrently, it presents a prime attack surface for adversaries striving to manipulate the behavior of the final trained system. This dissertation explores the complexities and hidden dangers of training supervised machine learning models in an adversarial setting, with a particular focus on models designed for cybersecurity tasks. Guided by the belief that an accurate understanding of the offensive capabilities of the adversary is the cornerstone on which to found any successful defensive strategy, the bulk of this thesis is composed by the introduction of novel training-time attacks. We start by proposing training-time attack strategies that operate in a clean-label regime, requiring minimal adversarial control over the training process, allowing the attacker to subvert the victim model’s prediction through simple poisoned data dissemination. Leveraging the characteristics of the data domain and model explanation techniques, we craft training data perturbations that stealthily subvert malicious software classifiers. We then shift the focus of our analysis on the long-standing problem of network flow traffic classification. In this context we develop new poisoning strategies that work around the constraints of the data domain through different strategies, including generative modeling. Finally, we examine unusual attack vectors, when the adversary is capable of tampering with different elements of the training process, such as the network connections during a federated learning protocol. We show that such an attacker can induce targeted performance degradation through strategic network interference, while maintaining stable the performance of the victim model on other data instances. We conclude by investigating mitigation techniques designed to target these insidious clean-label backdoor attacks in the cybersecurity domain.
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- Award ID(s):
- 2331081
- PAR ID:
- 10623868
- Publisher / Repository:
- https://repository.library.northeastern.edu/files/neu:ms35v291c
- Date Published:
- Format(s):
- Medium: X
- Institution:
- Northeastern University
- Sponsoring Org:
- National Science Foundation
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