NoiSec: Harnessing Noise for Security against Adversarial and Backdoor Attacks

• URL: http://arxiv.org/abs/2406.13073v1
• Date: Tue, 18 Jun 2024 21:44:51 GMT
• Title: NoiSec: Harnessing Noise for Security against Adversarial and Backdoor Attacks
• Authors: Md Hasan Shahriar, Ning Wang, Y. Thomas Hou, Wenjing Lou,
• Abstract summary: Malicious data manipulation attacks against machine learning jeopardize its reliability in safety-critical applications. NoiSec is a reconstruction-based detector that disentangles the noise from the test input, extracts the underlying features from the noise, and leverages them to recognize systematic malicious manipulation. NoiSec maintains a high detection performance, keeping the false positive rate within only 1%.
• Score: 24.583175914095783
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The exponential adoption of machine learning (ML) is propelling the world into a future of intelligent automation and data-driven solutions. However, the proliferation of malicious data manipulation attacks against ML, namely adversarial and backdoor attacks, jeopardizes its reliability in safety-critical applications. The existing detection methods against such attacks are built upon assumptions, limiting them in diverse practical scenarios. Thus, motivated by the need for a more robust and unified defense mechanism, we investigate the shared traits of adversarial and backdoor attacks and propose NoiSec that leverages solely the noise, the foundational root cause of such attacks, to detect any malicious data alterations. NoiSec is a reconstruction-based detector that disentangles the noise from the test input, extracts the underlying features from the noise, and leverages them to recognize systematic malicious manipulation. Experimental evaluations conducted on the CIFAR10 dataset demonstrate the efficacy of NoiSec, achieving AUROC scores exceeding 0.954 and 0.852 under white-box and black-box adversarial attacks, respectively, and 0.992 against backdoor attacks. Notably, NoiSec maintains a high detection performance, keeping the false positive rate within only 1\%. Comparative analyses against MagNet-based baselines reveal NoiSec's superior performance across various attack scenarios.

Related papers

• AdvQDet: Detecting Query-Based Adversarial Attacks with Adversarial Contrastive Prompt Tuning [93.77763753231338]
  Adversarial Contrastive Prompt Tuning (ACPT) is proposed to fine-tune the CLIP image encoder to extract similar embeddings for any two intermediate adversarial queries. We show that ACPT can detect 7 state-of-the-art query-based attacks with $>99%$ detection rate within 5 shots. We also show that ACPT is robust to 3 types of adaptive attacks.
  arXiv  Detail & Related papers  (2024-08-04T09:53:50Z)
• EaTVul: ChatGPT-based Evasion Attack Against Software Vulnerability Detection [19.885698402507145]
  Adversarial examples can exploit vulnerabilities within deep neural networks. This study showcases the susceptibility of deep learning models to adversarial attacks, which can achieve 100% attack success rate.
  arXiv  Detail & Related papers  (2024-07-27T09:04:54Z)
• BEEAR: Embedding-based Adversarial Removal of Safety Backdoors in Instruction-tuned Language Models [57.5404308854535]
  Safety backdoor attacks in large language models (LLMs) enable the stealthy triggering of unsafe behaviors while evading detection during normal interactions. We present BEEAR, a mitigation approach leveraging the insight that backdoor triggers induce relatively uniform drifts in the model's embedding space. Our bi-level optimization method identifies universal embedding perturbations that elicit unwanted behaviors and adjusts the model parameters to reinforce safe behaviors against these perturbations.
  arXiv  Detail & Related papers  (2024-06-24T19:29:47Z)
• Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells [14.710331873072146]
  Living-off-the-land (LOTL) techniques pose a significant challenge to security operations. We present a robust augmentation framework for cyber defense systems as Security Information and Event Management (SIEM) solutions.
  arXiv  Detail & Related papers  (2024-02-28T13:49:23Z)
• Malicious Agent Detection for Robust Multi-Agent Collaborative Perception [52.261231738242266]
  Multi-agent collaborative (MAC) perception is more vulnerable to adversarial attacks than single-agent perception. We propose Malicious Agent Detection (MADE), a reactive defense specific to MAC perception. We conduct comprehensive evaluations on a benchmark 3D dataset V2X-sim and a real-road dataset DAIR-V2X.
  arXiv  Detail & Related papers  (2023-10-18T11:36:42Z)
• Demystifying Poisoning Backdoor Attacks from a Statistical Perspective [35.30533879618651]
  Backdoor attacks pose a significant security risk due to their stealthy nature and potentially serious consequences. This paper evaluates the effectiveness of any backdoor attack incorporating a constant trigger. Our derived understanding applies to both discriminative and generative models.
  arXiv  Detail & Related papers  (2023-10-16T19:35:01Z)
• DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness [58.23214712926585]
  We develop a certified defense, DRSM (De-Randomized Smoothed MalConv), by redesigning the de-randomized smoothing technique for the domain of malware detection. Specifically, we propose a window ablation scheme to provably limit the impact of adversarial bytes while maximally preserving local structures of the executables. We are the first to offer certified robustness in the realm of static detection of malware executables.
  arXiv  Detail & Related papers  (2023-03-20T17:25:22Z)
• Policy Smoothing for Provably Robust Reinforcement Learning [109.90239627115336]
  We study the provable robustness of reinforcement learning against norm-bounded adversarial perturbations of the inputs. We generate certificates that guarantee that the total reward obtained by the smoothed policy will not fall below a certain threshold under a norm-bounded adversarial of perturbation the input.
  arXiv  Detail & Related papers  (2021-06-21T21:42:08Z)
• Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning [71.17774313301753]
  We explore the robustness of self-supervised learned high-level representations by using them in the defense against adversarial attacks. Experimental results on the ASVspoof 2019 dataset demonstrate that high-level representations extracted by Mockingjay can prevent the transferability of adversarial examples.
  arXiv  Detail & Related papers  (2020-06-05T03:03:06Z)
• Adversarial Feature Selection against Evasion Attacks [17.98312950660093]
  We propose a novel adversary-aware feature selection model that can improve classifier security against evasion attacks. We focus on an efficient, wrapper-based implementation of our approach, and experimentally validate its soundness on different application examples.
  arXiv  Detail & Related papers  (2020-05-25T15:05:51Z)

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.