A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference
- URL: http://arxiv.org/abs/2304.00083v4
- Date: Thu, 25 Apr 2024 01:43:03 GMT
- Title: A Generative Framework for Low-Cost Result Validation of Machine Learning-as-a-Service Inference
- Authors: Abhinav Kumar, Miguel A. Guirao Aguilera, Reza Tourani, Satyajayant Misra,
- Abstract summary: Fides is a novel framework for real-time integrity validation of ML-as-a-Service (ML) inference.
Fides features a client-side attack detection model that uses statistical analysis and divergence measurements to identify, with a high likelihood, if the service model is under attack.
We devised a generative adversarial network framework for training the attack detection and re-classification models.
- Score: 4.478182379059458
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The growing popularity of Machine Learning (ML) has led to its deployment in various sensitive domains, which has resulted in significant research focused on ML security and privacy. However, in some applications, such as Augmented/Virtual Reality, integrity verification of the outsourced ML tasks is more critical--a facet that has not received much attention. Existing solutions, such as multi-party computation and proof-based systems, impose significant computation overhead, which makes them unfit for real-time applications. We propose Fides, a novel framework for real-time integrity validation of ML-as-a-Service (MLaaS) inference. Fides features a novel and efficient distillation technique--Greedy Distillation Transfer Learning--that dynamically distills and fine-tunes a space and compute-efficient verification model for verifying the corresponding service model while running inside a trusted execution environment. Fides features a client-side attack detection model that uses statistical analysis and divergence measurements to identify, with a high likelihood, if the service model is under attack. Fides also offers a re-classification functionality that predicts the original class whenever an attack is identified. We devised a generative adversarial network framework for training the attack detection and re-classification models. The evaluation shows that Fides achieves an accuracy of up to 98% for attack detection and 94% for re-classification.
Related papers
- Multi-agent Reinforcement Learning-based Network Intrusion Detection System [3.4636217357968904]
Intrusion Detection Systems (IDS) play a crucial role in ensuring the security of computer networks.
We propose a novel multi-agent reinforcement learning (RL) architecture, enabling automatic, efficient, and robust network intrusion detection.
Our solution introduces a resilient architecture designed to accommodate the addition of new attacks and effectively adapt to changes in existing attack patterns.
arXiv Detail & Related papers (2024-07-08T09:18:59Z) - Enhancing Security in Federated Learning through Adaptive
Consensus-Based Model Update Validation [2.28438857884398]
This paper introduces an advanced approach for fortifying Federated Learning (FL) systems against label-flipping attacks.
We propose a consensus-based verification process integrated with an adaptive thresholding mechanism.
Our results indicate a significant mitigation of label-flipping attacks, bolstering the FL system's resilience.
arXiv Detail & Related papers (2024-03-05T20:54:56Z) - FreqFed: A Frequency Analysis-Based Approach for Mitigating Poisoning
Attacks in Federated Learning [98.43475653490219]
Federated learning (FL) is susceptible to poisoning attacks.
FreqFed is a novel aggregation mechanism that transforms the model updates into the frequency domain.
We demonstrate that FreqFed can mitigate poisoning attacks effectively with a negligible impact on the utility of the aggregated model.
arXiv Detail & Related papers (2023-12-07T16:56:24Z) - Client-side Gradient Inversion Against Federated Learning from Poisoning [59.74484221875662]
Federated Learning (FL) enables distributed participants to train a global model without sharing data directly to a central server.
Recent studies have revealed that FL is vulnerable to gradient inversion attack (GIA), which aims to reconstruct the original training samples.
We propose Client-side poisoning Gradient Inversion (CGI), which is a novel attack method that can be launched from clients.
arXiv Detail & Related papers (2023-09-14T03:48:27Z) - Avoid Adversarial Adaption in Federated Learning by Multi-Metric
Investigations [55.2480439325792]
Federated Learning (FL) facilitates decentralized machine learning model training, preserving data privacy, lowering communication costs, and boosting model performance through diversified data sources.
FL faces vulnerabilities such as poisoning attacks, undermining model integrity with both untargeted performance degradation and targeted backdoor attacks.
We define a new notion of strong adaptive adversaries, capable of adapting to multiple objectives simultaneously.
MESAS is the first defense robust against strong adaptive adversaries, effective in real-world data scenarios, with an average overhead of just 24.37 seconds.
arXiv Detail & Related papers (2023-06-06T11:44:42Z) - Robustness, Evaluation and Adaptation of Machine Learning Models in the
Wild [4.304803366354879]
We study causes of impaired robustness to domain shifts and present algorithms for training domain robust models.
A key source of model brittleness is due to domain overfitting, which our new training algorithms suppress and instead encourage domain-general hypotheses.
arXiv Detail & Related papers (2023-03-05T21:41:16Z) - RelaxLoss: Defending Membership Inference Attacks without Losing Utility [68.48117818874155]
We propose a novel training framework based on a relaxed loss with a more achievable learning target.
RelaxLoss is applicable to any classification model with added benefits of easy implementation and negligible overhead.
Our approach consistently outperforms state-of-the-art defense mechanisms in terms of resilience against MIAs.
arXiv Detail & Related papers (2022-07-12T19:34:47Z) - RoFL: Attestable Robustness for Secure Federated Learning [59.63865074749391]
Federated Learning allows a large number of clients to train a joint model without the need to share their private data.
To ensure the confidentiality of the client updates, Federated Learning systems employ secure aggregation.
We present RoFL, a secure Federated Learning system that improves robustness against malicious clients.
arXiv Detail & Related papers (2021-07-07T15:42:49Z) - Federated Learning with Unreliable Clients: Performance Analysis and
Mechanism Design [76.29738151117583]
Federated Learning (FL) has become a promising tool for training effective machine learning models among distributed clients.
However, low quality models could be uploaded to the aggregator server by unreliable clients, leading to a degradation or even a collapse of training.
We model these unreliable behaviors of clients and propose a defensive mechanism to mitigate such a security risk.
arXiv Detail & Related papers (2021-05-10T08:02:27Z) - Leveraging Siamese Networks for One-Shot Intrusion Detection Model [0.0]
Supervised Machine Learning (ML) to enhance Intrusion Detection Systems has been the subject of significant research.
retraining the models in-situ renders the network susceptible to attacks owing to the time-window required to acquire a sufficient volume of data.
Here, a complementary approach referred to as 'One-Shot Learning', whereby a limited number of examples of a new attack-class is used to identify a new attack-class.
A Siamese Network is trained to differentiate between classes based on pairs similarities, rather than features, allowing to identify new and previously unseen attacks.
arXiv Detail & Related papers (2020-06-27T11:40:01Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.