FedDef: Defense Against Gradient Leakage in Federated Learning-based Network Intrusion Detection Systems
Deep learning (DL) methods have been widely applied to anomaly-based network intrusion detection system (NIDS) to detect malicious traffic. To expand the usage scenarios of DL-based methods, federated learning (FL) allows multiple users to train a global model on the basis of respecting individual data privacy. However, it has not yet been systematically evaluated how robust FL-based NIDSs are against existing privacy attacks under existing defenses. To address this issue, we propose two privacy evaluation metrics designed for FL-based NIDSs, including (1) privacy score that evaluates the similarity between the original and recovered traffic features using reconstruction attacks, and (2) evasion rate against NIDSs using adversarial attack with the recovered traffic. We conduct experiments to illustrate that existing defenses provide little protection and the corresponding adversarial traffic can even evade the SOTA NIDS Kitsune. To defend against such attacks and build a more robust FL-based NIDS, we further propose FedDef, a novel optimization-based input perturbation defense strategy with theoretical guarantee. It achieves both high utility by minimizing the gradient distance and strong privacy protection by maximizing the input distance. We experimentally evaluate four existing defenses on four datasets and show that our defense outperforms all the baselines in terms of privacy protection with up to 7 times higher privacy score, while maintaining model accuracy loss within 3% under optimal parameter combination.
Code (0)
등록된 구현이 없습니다.
Tasks
Adversarial AttackFederated LearningIntrusion DetectionNetwork Intrusion DetectionSimilar Papers 제목 키워드 기반
FedDefender: Backdoor Attack Defense in Federated Learning
Federated Learning (FL) is a privacy-preserving distributed machine learning technique that enables individual clients (e.g., user participants, edge devices, or organizations) to train a model on their local data in a s…
Backdoor AttackData PoisoningFederated LearningImage Classification+2FedDefender: Client-Side Attack-Tolerant Federated Learning
Federated learning enables learning from decentralized data sources without compromising privacy, which makes it a crucial technique. However, it is vulnerable to model poisoning attacks, where malicious clients interfer…
Federated LearningKnowledge DistillationModel PoisoningDefense Against Gradient Leakage Attacks via Learning to Obscure Data
Federated learning is considered as an effective privacy-preserving learning mechanism that separates the client's data and model training process. However, federated learning is still under the risk of privacy leakage b…
Federated LearningPrivacy PreservingCAFE: Catastrophic Data Leakage in Federated Learning
Private training data can be leaked through the gradient sharing mechanism deployed in machine learning systems, such as federated learning (FL). Increasing batch size is often viewed as a promising defense strategy agai…
Federated LearningCatastrophic Data Leakage in Vertical Federated Learning
Recent studies show that private training data can be leaked through the gradients sharing mechanism deployed in distributed machine learning systems, such as federated learning (FL). Increasing batch size to complicate …
Federated LearningVertical Federated Learning