Energy-Latency Attacks: A New Adversarial Threat to Deep Learning

• URL: http://arxiv.org/abs/2503.04963v1
• Date: Thu, 06 Mar 2025 20:50:58 GMT
• Title: Energy-Latency Attacks: A New Adversarial Threat to Deep Learning
• Authors: Hanene F. Z. Brachemi Meftah, Wassim Hamidouche, Sid Ahmed Fezza, Olivier Deforges,
• Abstract summary: This paper provides a comprehensive overview of current research on energy-latency attacks.<n>It focuses on the vulnerability of deep neural networks to this emerging attack paradigm, which can trigger denial-of-service attacks.<n>We explore different metrics used to measure the success of these attacks and provide an analysis and comparison of existing attack strategies.
• Score: 10.192836826455544
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The growing computational demand for deep neural networks ( DNNs) has raised concerns about their energy consumption and carbon footprint, particularly as the size and complexity of the models continue to increase. To address these challenges, energy-efficient hardware and custom accelerators have become essential. Additionally, adaptable DNN s are being developed to dynamically balance performance and efficiency. The use of these strategies became more common to enable sustainable AI deployment. However, these efficiency-focused designs may also introduce vulnerabilities, as attackers can potentially exploit them to increase latency and energy usage by triggering their worst-case-performance scenarios. This new type of attack, called energy-latency attacks, has recently gained significant research attention, focusing on the vulnerability of DNN s to this emerging attack paradigm, which can trigger denial-of-service ( DoS) attacks. This paper provides a comprehensive overview of current research on energy-latency attacks, categorizing them using the established taxonomy for traditional adversarial attacks. We explore different metrics used to measure the success of these attacks and provide an analysis and comparison of existing attack strategies. We also analyze existing defense mechanisms and highlight current challenges and potential areas for future research in this developing field. The GitHub page for this work can be accessed at https://github.com/hbrachemi/Survey_energy_attacks/

Related papers

• Energy Backdoor Attack to Deep Neural Networks [14.40890430352325]
  Deep learning (DL) has increased computing complexity and energy use, prompting the adoption of application specific integrated circuits (ASICs) for energy-efficient edge and mobile deployment.<n>Recent studies have demonstrated the vulnerability of these accelerators to energy attacks.<n>In this paper, we design an innovative energy backdoor attack against deep neural networks (DNNs) operating on sparsity-based accelerators.
  arXiv  Detail & Related papers  (2025-01-14T14:26:18Z)
• A Novel Bifurcation Method for Observation Perturbation Attacks on Reinforcement Learning Agents: Load Altering Attacks on a Cyber Physical Power System [1.7887848708497243]
  This work proposes a novel attack technique for continuous control using Group Difference Logits loss with a bifurcation layer. We demonstrate the impacts of powerful gradient-based attacks in a realistic smart energy environment.
  arXiv  Detail & Related papers  (2024-07-06T20:55:24Z)
• IDEA: Invariant Defense for Graph Adversarial Robustness [60.0126873387533]
  We propose an Invariant causal DEfense method against adversarial Attacks (IDEA) We derive node-based and structure-based invariance objectives from an information-theoretic perspective. Experiments demonstrate that IDEA attains state-of-the-art defense performance under all five attacks on all five datasets.
  arXiv  Detail & Related papers  (2023-05-25T07:16:00Z)
• Probabilistic Categorical Adversarial Attack & Adversarial Training [45.458028977108256]
  The existence of adversarial examples brings huge concern for people to apply Deep Neural Networks (DNNs) in safety-critical tasks. How to generate adversarial examples with categorical data is an important problem but lack of extensive exploration. We propose Probabilistic Categorical Adversarial Attack (PCAA), which transfers the discrete optimization problem to a continuous problem that can be solved efficiently by Projected Gradient Descent.
  arXiv  Detail & Related papers  (2022-10-17T19:04:16Z)
• Downlink Power Allocation in Massive MIMO via Deep Learning: Adversarial Attacks and Training [62.77129284830945]
  This paper considers a regression problem in a wireless setting and shows that adversarial attacks can break the DL-based approach. We also analyze the effectiveness of adversarial training as a defensive technique in adversarial settings and show that the robustness of DL-based wireless system against attacks improves significantly.
  arXiv  Detail & Related papers  (2022-06-14T04:55:11Z)
• A Practical Adversarial Attack on Contingency Detection of Smart Energy Systems [0.0]
  We propose an innovative adversarial attack model that can practically compromise dynamical controls of energy system. We also optimize the deployment of the proposed adversarial attack model by employing deep reinforcement learning (RL) techniques.
  arXiv  Detail & Related papers  (2021-09-13T23:11:56Z)
• The Feasibility and Inevitability of Stealth Attacks [63.14766152741211]
  We study new adversarial perturbations that enable an attacker to gain control over decisions in generic Artificial Intelligence systems. In contrast to adversarial data modification, the attack mechanism we consider here involves alterations to the AI system itself.
  arXiv  Detail & Related papers  (2021-06-26T10:50:07Z)
• Exploiting Vulnerabilities of Deep Learning-based Energy Theft Detection in AMI through Adversarial Attacks [1.5791732557395552]
  We study the vulnerabilities of deep learning-based energy theft detection through adversarial attacks, including single-step attacks and iterative attacks. The evaluation based on three types of neural networks shows that the adversarial attacker can report extremely low consumption measurements to the utility without being detected by the DL models.
  arXiv  Detail & Related papers  (2020-10-16T02:25:40Z)
• Measurement-driven Security Analysis of Imperceptible Impersonation Attacks [54.727945432381716]
  We study the exploitability of Deep Neural Network-based Face Recognition systems. We show that factors such as skin color, gender, and age, impact the ability to carry out an attack on a specific target victim. We also study the feasibility of constructing universal attacks that are robust to different poses or views of the attacker's face.
  arXiv  Detail & Related papers  (2020-08-26T19:27:27Z)
• Sparsity Turns Adversarial: Energy and Latency Attacks on Deep Neural Networks [3.9193443389004887]
  Adrial attacks have exposed serious vulnerabilities in Deep Neural Networks (DNNs) We propose and demonstrate sparsity attacks, which adversarial modify a DNN's inputs so as to reduce sparsity in its internal activation values. We launch both white-box and black-box versions of adversarial sparsity attacks and demonstrate that they decrease activation sparsity by up to 1.82x.
  arXiv  Detail & Related papers  (2020-06-14T21:02:55Z)
• Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies [73.39668293190019]
  Adversary attacks can be easily fooled by small perturbation on the input. Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability. In this survey, we categorize existing attacks and defenses, and review the corresponding state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-03-02T04:32:38Z)
• Challenges and Countermeasures for Adversarial Attacks on Deep Reinforcement Learning [48.49658986576776]
  Deep Reinforcement Learning (DRL) has numerous applications in the real world thanks to its outstanding ability in adapting to the surrounding environments. Despite its great advantages, DRL is susceptible to adversarial attacks, which precludes its use in real-life critical systems and applications. This paper presents emerging attacks in DRL-based systems and the potential countermeasures to defend against these attacks.
  arXiv  Detail & Related papers  (2020-01-27T10:53:11Z)

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.