Revisiting Backdoor Attacks on Time Series Classification in the Frequency Domain

2025-03-12 · Yuanmin Huang, Mi Zhang, Zhaoxiang Wang, Wenxuan Li, Min Yang

Time series classification (TSC) is a cornerstone of modern web applications, powering tasks such as financial data analysis, network traffic monitoring, and user behavior analysis. In recent years, deep neural networks (DNNs) have greatly enhanced the performance of TSC models in these critical domains. However, DNNs are vulnerable to backdoor attacks, where attackers can covertly implant triggers into models to induce malicious outcomes. Existing backdoor attacks targeting DNN-based TSC models remain elementary. In particular, early methods borrow trigger designs from computer vision, which are ineffective for time series data. More recent approaches utilize generative models for trigger generation, but at the cost of significant computational complexity. In this work, we analyze the limitations of existing attacks and introduce an enhanced method, FreqBack. Drawing inspiration from the fact that DNN models inherently capture frequency domain features in time series data, we identify that improper perturbations in the frequency domain are the root cause of ineffective attacks. To address this, we propose to generate triggers both effectively and efficiently, guided by frequency analysis. FreqBack exhibits substantial performance across five models and eight datasets, achieving an impressive attack success rate of over 90%, while maintaining less than a 3% drop in model accuracy on clean data.

📄 PDF Abstract BibTeX arXiv:2503.09712

Code (0)

등록된 구현이 없습니다.

Tasks

Time SeriesTime Series Classification

End-to-End Anti-Backdoor Learning on Images and Time Series

2024-01-06 · Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani, Yige Li 외

Backdoor attacks present a substantial security concern for deep learning models, especially those utilized in applications critical to safety and security. These attacks manipulate model behavior by embedding a hidden t…

image-classification Image Classification Time Series

Backdoor Attacks on Time Series: A Generative Approach

2022-11-15 · Yujing Jiang, Xingjun Ma, Sarah Monazam Erfani, James Bailey

Backdoor attacks have emerged as one of the major security threats to deep learning models as they can easily control the model's test-time predictions by pre-injecting a backdoor trigger into the model at training time.…

Time Series Time Series Analysis

Revisiting Personalized Federated Learning: Robustness Against Backdoor Attacks

2023-02-03 · Zeyu Qin, Liuyi Yao, Daoyuan Chen, Yaliang Li 외

In this work, besides improving prediction accuracy, we study whether personalization could bring robustness benefits to backdoor attacks. We conduct the first study of backdoor attacks in the pFL framework, testing 4 wi…

Backdoor Attack Federated Learning Personalized Federated Learning

TrojanTime: Backdoor Attacks on Time Series Classification

2025-02-02 · Chang Dong, Zechao Sun, Guangdong Bai, Shuying Piao 외

Time Series Classification (TSC) is highly vulnerable to backdoor attacks, posing significant security threats. Existing methods primarily focus on data poisoning during the training phase, designing sophisticated trigge…

Classification Data Poisoning Time Series Time Series Classification

Model-agnostic clean-label backdoor mitigation in cybersecurity environments

2024-07-11 · Giorgio Severi, Simona Boboila, John Holodnak, Kendra Kratkiewicz 외

The training phase of machine learning models is a delicate step, especially in cybersecurity contexts. Recent research has surfaced a series of insidious training-time attacks that inject backdoors in models designed fo…

backdoor defense Classification Malware Classification