MirGuard: Towards a Robust Provenance-based Intrusion Detection System Against Graph Manipulation Attacks

• URL: http://arxiv.org/abs/2508.10639v1
• Date: Thu, 14 Aug 2025 13:35:51 GMT
• Title: MirGuard: Towards a Robust Provenance-based Intrusion Detection System Against Graph Manipulation Attacks
• Authors: Anyuan Sang, Lu Zhou, Li Yang, Junbo Jia, Huipeng Yang, Pengbin Feng, Jianfeng Ma,
• Abstract summary: MirGuard is an anomaly detection framework that combines logic-aware multi-view augmentation with contrastive representation learning.<n>MirGuard significantly outperforms state-of-the-art detectors in robustness against various graph manipulation attacks.
• Score: 13.92935628832727
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning-based Provenance-based Intrusion Detection Systems (PIDSes) have become essential tools for anomaly detection in host systems due to their ability to capture rich contextual and structural information, as well as their potential to detect unknown attacks. However, recent studies have shown that these systems are vulnerable to graph manipulation attacks, where attackers manipulate the graph structure to evade detection. While some previous approaches have discussed this type of attack, none have fully addressed it with a robust detection solution, limiting the practical applicability of PIDSes. To address this challenge, we propose MirGuard, a robust anomaly detection framework that combines logic-aware multi-view augmentation with contrastive representation learning. Rather than applying arbitrary structural perturbations, MirGuard introduces Logic-Aware Noise Injection (LNI) to generate semantically valid graph views, ensuring that all augmentations preserve the underlying causal semantics of the provenance data. These views are then used in a Logic-Preserving Contrastive Learning framework, which encourages the model to learn representations that are invariant to benign transformations but sensitive to adversarial inconsistencies. Comprehensive evaluations on multiple provenance datasets demonstrate that MirGuard significantly outperforms state-of-the-art detectors in robustness against various graph manipulation attacks without sacrificing detection performance and efficiency. Our work represents the first targeted study to enhance PIDS against such adversarial threats, providing a robust and effective solution to modern cybersecurity challenges.

Related papers

• Robustness in AI-Generated Detection: Enhancing Resistance to Adversarial Attacks [4.179092469766417]
  This paper investigates the vulnerabilities of current AI-generated face detection systems.<n>We propose an approach that integrates adversarial training to mitigate the impact of adversarial examples.<n>We also provide an in-depth analysis of adversarial and benign examples, offering insights into the intrinsic characteristics of AI-generated content.
  arXiv  Detail & Related papers  (2025-05-06T11:19:01Z)
• Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection [21.03032944637112]
  Adrial attacks pose a critical security threat to real-world AI systems.<n>This paper proposes a lightweight adversarial detection framework based on the large-scale pre-trained vision-language model CLIP.
  arXiv  Detail & Related papers  (2025-04-01T05:21:45Z)
• Slot: Provenance-Driven APT Detection through Graph Reinforcement Learning [24.84110719035862]
  Advanced Persistent Threats (APTs) represent sophisticated cyberattacks characterized by their ability to remain undetected for extended periods.<n>We propose Slot, an advanced APT detection approach based on provenance graphs and graph reinforcement learning.<n>We show Slot's outstanding accuracy, efficiency, adaptability, and robustness in APT detection, with most metrics surpassing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-10-23T14:28:32Z)
• MASKDROID: Robust Android Malware Detection with Masked Graph Representations [56.09270390096083]
  We propose MASKDROID, a powerful detector with a strong discriminative ability to identify malware. We introduce a masking mechanism into the Graph Neural Network based framework, forcing MASKDROID to recover the whole input graph. This strategy enables the model to understand the malicious semantics and learn more stable representations, enhancing its robustness against adversarial attacks.
  arXiv  Detail & Related papers  (2024-09-29T07:22:47Z)
• A Survey and Evaluation of Adversarial Attacks for Object Detection [11.48212060875543]
  Deep learning models are vulnerable to adversarial examples that can deceive them into making confident but incorrect predictions.<n>This vulnerability pose significant risks in high-stakes applications such as autonomous vehicles, security surveillance, and safety-critical inspection systems.<n>This paper presents a novel taxonomic framework for categorizing adversarial attacks specific to object detection architectures.
  arXiv  Detail & Related papers  (2024-08-04T05:22:08Z)
• Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack [24.954755569786396]
  We propose a framework for a broader class of adversarial attacks, designed to perform minor perturbations in machine-generated content to evade detection. We consider two attack settings: white-box and black-box, and employ adversarial learning in dynamic scenarios to assess the potential enhancement of the current detection model's robustness. The empirical results reveal that the current detection models can be compromised in as little as 10 seconds, leading to the misclassification of machine-generated text as human-written content.
  arXiv  Detail & Related papers  (2024-04-02T12:49:22Z)
• FaultGuard: A Generative Approach to Resilient Fault Prediction in Smart Electrical Grids [53.2306792009435]
  FaultGuard is the first framework for fault type and zone classification resilient to adversarial attacks. We propose a low-complexity fault prediction model and an online adversarial training technique to enhance robustness. Our model outclasses the state-of-the-art for resilient fault prediction benchmarking, with an accuracy of up to 0.958.
  arXiv  Detail & Related papers  (2024-03-26T08:51:23Z)
• Spatial-Frequency Discriminability for Revealing Adversarial Perturbations [53.279716307171604]
  Vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. Current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. We propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition.
  arXiv  Detail & Related papers  (2023-05-18T10:18:59Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• Improving robustness of jet tagging algorithms with adversarial training [56.79800815519762]
  We investigate the vulnerability of flavor tagging algorithms via application of adversarial attacks. We present an adversarial training strategy that mitigates the impact of such simulated attacks.
  arXiv  Detail & Related papers  (2022-03-25T19:57:19Z)
• No Need to Know Physics: Resilience of Process-based Model-free Anomaly Detection for Industrial Control Systems [95.54151664013011]
  We present a novel framework to generate adversarial spoofing signals that violate physical properties of the system. We analyze four anomaly detectors published at top security conferences.
  arXiv  Detail & Related papers  (2020-12-07T11:02:44Z)
• Graph Backdoor [53.70971502299977]
  We present GTA, the first backdoor attack on graph neural networks (GNNs) GTA departs in significant ways: it defines triggers as specific subgraphs, including both topological structures and descriptive features. It can be instantiated for both transductive (e.g., node classification) and inductive (e.g., graph classification) tasks.
  arXiv  Detail & Related papers  (2020-06-21T19:45:30Z)

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.