Cyber Security of Sensor Systems for State Sequence Estimation: an AI Approach

• URL: http://arxiv.org/abs/2506.06572v1
• Date: Fri, 06 Jun 2025 22:51:44 GMT
• Title: Cyber Security of Sensor Systems for State Sequence Estimation: an AI Approach
• Authors: Xubin Fang, Rick S. Blum, Ramesh Bharadwaj, Brian M. Sadler,
• Abstract summary: This paper develops the first methods that accurately identify/eliminate only the problematic attacked sensor data.<n>The approach does not assume a known form for the statistical model of the sensor data.
• Score: 19.125720311271508
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sensor systems are extremely popular today and vulnerable to sensor data attacks. Due to possible devastating consequences, counteracting sensor data attacks is an extremely important topic, which has not seen sufficient study. This paper develops the first methods that accurately identify/eliminate only the problematic attacked sensor data presented to a sequence estimation/regression algorithm under a powerful attack model constructed based on known/observed attacks. The approach does not assume a known form for the statistical model of the sensor data, allowing data-driven and machine learning sequence estimation/regression algorithms to be protected. A simple protection approach for attackers not endowed with knowledge of the details of our protection approach is first developed, followed by additional processing for attacks based on protection system knowledge. In the cases tested for which it was designed, experimental results show that the simple approach achieves performance indistinguishable, to two decimal places, from that for an approach which knows which sensors are attacked. For cases where the attacker has knowledge of the protection approach, experimental results indicate the additional processing can be configured so that the worst-case degradation under the additional processing and a large number of sensors attacked can be made significantly smaller than the worst-case degradation of the simple approach, and close to an approach which knows which sensors are attacked, for the same number of attacked sensors with just a slight degradation under no attacks. Mathematical descriptions of the worst-case attacks are used to demonstrate the additional processing will provide similar advantages for cases for which we do not have numerical results. All the data-driven processing used in our approaches employ only unattacked training data.

Related papers

• Indiscriminate Data Poisoning Attacks on Pre-trained Feature Extractors [26.36344184385407]
  In this paper, we explore the threat of indiscriminate attacks on downstream tasks that apply pre-trained feature extractors. We propose two types of attacks: (1) the input space attacks, where we modify existing attacks to craft poisoned data in the input space; and (2) the feature targeted attacks, where we find poisoned features by treating the learned feature representations as a dataset. Our experiments examine such attacks in popular downstream tasks of fine-tuning on the same dataset and transfer learning that considers domain adaptation.
  arXiv  Detail & Related papers  (2024-02-20T01:12:59Z)
• Protecting Model Adaptation from Trojans in the Unlabeled Data [120.42853706967188]
  This paper explores the potential trojan attacks on model adaptation launched by well-designed poisoning target data.<n>We propose a plug-and-play method named DiffAdapt, which can be seamlessly integrated with existing adaptation algorithms.
  arXiv  Detail & Related papers  (2024-01-11T16:42:10Z)
• Pre-trained Encoders in Self-Supervised Learning Improve Secure and Privacy-preserving Supervised Learning [63.45532264721498]
  Self-supervised learning is an emerging technique to pre-train encoders using unlabeled data. We perform first systematic, principled measurement study to understand whether and when a pretrained encoder can address the limitations of secure or privacy-preserving supervised learning algorithms.
  arXiv  Detail & Related papers  (2022-12-06T21:35:35Z)
• Label Flipping Data Poisoning Attack Against Wearable Human Activity Recognition System [0.5284812806199193]
  This paper presents the design of a label flipping data poisoning attack for a Human Activity Recognition (HAR) system. Due to high noise and uncertainty in the sensing environment, such an attack poses a severe threat to the recognition system. This paper shades light on how to carry out the attack in practice through smartphone-based sensor data collection applications.
  arXiv  Detail & Related papers  (2022-08-17T17:52:13Z)
• Robust Trajectory Prediction against Adversarial Attacks [84.10405251683713]
  Trajectory prediction using deep neural networks (DNNs) is an essential component of autonomous driving systems. These methods are vulnerable to adversarial attacks, leading to serious consequences such as collisions. In this work, we identify two key ingredients to defend trajectory prediction models against adversarial attacks.
  arXiv  Detail & Related papers  (2022-07-29T22:35:05Z)
• RobustSense: Defending Adversarial Attack for Secure Device-Free Human Activity Recognition [37.387265457439476]
  We propose a novel learning framework, RobustSense, to defend common adversarial attacks. Our method works well on wireless human activity recognition and person identification systems.
  arXiv  Detail & Related papers  (2022-04-04T15:06:03Z)
• 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)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• Gradient-based Data Subversion Attack Against Binary Classifiers [9.414651358362391]
  In this work, we focus on label contamination attack in which an attacker poisons the labels of data to compromise the functionality of the system. We exploit the gradients of a differentiable convex loss function with respect to the predicted label as a warm-start and formulate different strategies to find a set of data instances to contaminate. Our experiments show that the proposed approach outperforms the baselines and is computationally efficient.
  arXiv  Detail & Related papers  (2021-05-31T09:04:32Z)
• 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)
• Active Fuzzing for Testing and Securing Cyber-Physical Systems [8.228859318969082]
  We propose active fuzzing, an automatic approach for finding test suites of packet-level CPS network attacks. Key to our solution is the use of online active learning, which iteratively updates the models by sampling payloads. We evaluate the efficacy of active fuzzing by implementing it for a water purification plant testbed, finding it can automatically discover a test suite of flow, pressure, and over/underflow attacks.
  arXiv  Detail & Related papers  (2020-05-28T16:19:50Z)

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.