State-Blocking Side-Channel Attacks and Autonomous Fault Detection in Quantum Key Distribution

• URL: http://arxiv.org/abs/2305.18006v3
• Date: Mon, 2 Sep 2024 21:44:57 GMT
• Title: State-Blocking Side-Channel Attacks and Autonomous Fault Detection in Quantum Key Distribution
• Authors: Matt Young, Marco Lucamarini, Stefano Pirandola,
• Abstract summary: Side-channel attacks allow an Eavesdropper to use insecurities in the practical implementation of QKD systems. We discuss a scheme to autonomously detect such an attack during an ongoing QKD session. We present how Alice and Bob can put in place a countermeasure to continue use of the QKD system, once a detection is made.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Side-channel attacks allow an Eavesdropper to use insecurities in the practical implementation of QKD systems to gain an advantage that is not considered by security proofs that assume perfect implementations. In this work we specify a side-channel capability for Eve that has yet to be considered, before then going on to discuss a scheme to autonomously detect such an attack during an ongoing QKD session, and the limits as to how fast a detection can be made. The side-channel capability is very general and covers a wide variety of possible implementations for the attack itself. We present how Alice and Bob can put in place a countermeasure to continue use of the QKD system, once a detection is made, regardless of the ongoing side-channel attack. This prevents downtime of QKD systems, which in critical infrastructure could pose severe risks. We then extend Eves side-channel capability and present a modified attack strategy. This strengthened attack can be detected under certain conditions by our scheme, however intelligent choices of parameters from Eve allow her strengthened attack to go undetected. From this, we discuss the implications this has on Privacy Amplification, and therefore on the security of QKD as a whole. Finally, consideration is given as to how these types of attacks are analogous to certain types of faults in the QKD system, how our detection scheme can also detect these faults, and therefore how this adds autonomous fault detection and redundancy to implementations of QKD.

Related papers

• Deep-learning-based continuous attacks on quantum key distribution protocols [0.0]
  We design a new attack scheme that exploits continuous measurement together with the powerful pattern recognition capacities of deep recurrent neural networks. We show that, when applied to the BB84 protocol, our attack can be difficult to notice while still allowing the spy to extract significant information about the states of the qubits sent in the quantum communication channel.
  arXiv  Detail & Related papers  (2024-08-22T17:39:26Z)
• Simulations of distributed-phase-reference quantum key distribution protocols [0.1398098625978622]
  Quantum key distribution protocols provide a secret key between two users with security guaranteed by the laws of quantum mechanics. We perform simulations on the Interconnect platform to characterise the practical implementation of these devices. We briefly describe and simulate some possible eavesdropping attempts, backflash attack, trojan-horse attack and detector-blinding attack exploiting the device imperfections.
  arXiv  Detail & Related papers  (2024-06-13T13:19:04Z)
• Gaussian Mixture Models for Affordance Learning using Bayesian Networks [50.18477618198277]
  Affordances are fundamental descriptors of relationships between actions, objects and effects. This paper approaches the problem of an embodied agent exploring the world and learning these affordances autonomously from its sensory experiences.
  arXiv  Detail & Related papers  (2024-02-08T22:05:45Z)
• Mitigation of Channel Tampering Attacks in Continuous-Variable Quantum Key Distribution [8.840486611542584]
  In CV-QKD, vulnerability to communication disruption persists from potential adversaries employing Denial-of-Service (DoS) attacks. Inspired by DoS attacks, this paper introduces a novel threat in CV-QKD called the Channel Amplification (CA) attack. To counter this threat, we propose a detection and mitigation strategy.
  arXiv  Detail & Related papers  (2024-01-29T05:48:51Z)
• Fully passive Measurement Device Independent Quantum Key Distribution [15.545098722427678]
  Measurement-device-independent quantum key distribution (MDI-QKD) can resist all attacks on the detection devices. One possible solution is to use the passive protocol to eliminate the side channels introduced by active modulators at the source. We propose a fully passive MDI-QKD scheme that can protect the system from both side channels of source modulators and attacks on the measurement devices.
  arXiv  Detail & Related papers  (2023-09-14T10:18:52Z)
• Upper bounds on key rates in device-independent quantum key distribution based on convex-combination attacks [1.118478900782898]
  We present the convex-combination attack as an efficient, easy-to-use technique for upper-bounding DIQKD key rates. It allows verifying the accuracy of lower bounds on key rates for state-of-the-art protocols.
  arXiv  Detail & Related papers  (2022-06-13T15:27:48Z)
• Toward Certified Robustness Against Real-World Distribution Shifts [65.66374339500025]
  We train a generative model to learn perturbations from data and define specifications with respect to the output of the learned model. A unique challenge arising from this setting is that existing verifiers cannot tightly approximate sigmoid activations. We propose a general meta-algorithm for handling sigmoid activations which leverages classical notions of counter-example-guided abstraction refinement.
  arXiv  Detail & Related papers  (2022-06-08T04:09:13Z)
• On Trace of PGD-Like Adversarial Attacks [77.75152218980605]
  Adversarial attacks pose safety and security concerns for deep learning applications. We construct Adrial Response Characteristics (ARC) features to reflect the model's gradient consistency. Our method is intuitive, light-weighted, non-intrusive, and data-undemanding.
  arXiv  Detail & Related papers  (2022-05-19T14:26:50Z)
• Experimental measurement-device-independent type quantum key distribution with flawed and correlated sources [14.143874849657317]
  Security of quantum key distribution (QKD) is threatened by discrepancies between realistic devices and theoretical assumptions. Here, we adopt the reference technique to prove security of an efficient four-phase measurement-device-independent QKD using laser pulses against potential source imperfections. In addition, we demonstrate the feasibility of our protocol through a proof-of-principle experimental implementation and achieve a secure key rate of 253 bps with a 20 dB channel loss.
  arXiv  Detail & Related papers  (2022-04-18T13:44:51Z)
• CC-Cert: A Probabilistic Approach to Certify General Robustness of Neural Networks [58.29502185344086]
  In safety-critical machine learning applications, it is crucial to defend models against adversarial attacks. It is important to provide provable guarantees for deep learning models against semantically meaningful input transformations. We propose a new universal probabilistic certification approach based on Chernoff-Cramer bounds.
  arXiv  Detail & Related papers  (2021-09-22T12:46:04Z)
• 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)
• Nonclassical Attack on a Quantum KeyDistribution System [0.0]
  The article is focused on research of an attack on the quantum key distribution system and proposes a countermeasure method. Results of the research show that quantum key distribution systems have vulnerabilities not only in the protocols, but also in other vital system components.
  arXiv  Detail & Related papers  (2021-04-28T11:49:52Z)
• Deconfounding Scores: Feature Representations for Causal Effect Estimation with Weak Overlap [140.98628848491146]
  We introduce deconfounding scores, which induce better overlap without biasing the target of estimation. We show that deconfounding scores satisfy a zero-covariance condition that is identifiable in observed data. In particular, we show that this technique could be an attractive alternative to standard regularizations.
  arXiv  Detail & Related papers  (2021-04-12T18:50:11Z)
• A context-aware gate set tomography characterization of superconducting qubits [1.4979445283937185]
  We introduce a context-aware version of the gate set tomography protocol. We experimentally infer context-dependent errors in a publicly accessible cloud-based superconducting qubits platform. Our results show that when the GST is upgraded to include such features of context-awareness, a large coherence in the errors is observed.
  arXiv  Detail & Related papers  (2021-03-17T21:41:26Z)
• Trust but Verify: Assigning Prediction Credibility by Counterfactual Constrained Learning [123.3472310767721]
  Prediction credibility measures are fundamental in statistics and machine learning. These measures should account for the wide variety of models used in practice. The framework developed in this work expresses the credibility as a risk-fit trade-off.
  arXiv  Detail & Related papers  (2020-11-24T19:52:38Z)
• Countermeasure against quantum hacking using detection statistics [0.0]
  We present a new countermeasure against these kind of attacks based on the use of multi-pixel detectors. We show that with this method, we are able to estimate an upper bound on the information an eavesdropper could have on the key exchanged.
  arXiv  Detail & Related papers  (2020-10-16T16:19:50Z)
• Structural Causal Models Are (Solvable by) Credal Networks [70.45873402967297]
  Causal inferences can be obtained by standard algorithms for the updating of credal nets. This contribution should be regarded as a systematic approach to represent structural causal models by credal networks. Experiments show that approximate algorithms for credal networks can immediately be used to do causal inference in real-size problems.
  arXiv  Detail & Related papers  (2020-08-02T11:19:36Z)
• A Self-supervised Approach for Adversarial Robustness [105.88250594033053]
  Adversarial examples can cause catastrophic mistakes in Deep Neural Network (DNNs) based vision systems. This paper proposes a self-supervised adversarial training mechanism in the input space. It provides significant robustness against the textbfunseen adversarial attacks.
  arXiv  Detail & Related papers  (2020-06-08T20:42:39Z)
• Backflash Light as a Security Vulnerability in Quantum Key Distribution Systems [77.34726150561087]
  We review the security vulnerabilities of quantum key distribution (QKD) systems. We mainly focus on a particular effect known as backflash light, which can be a source of eavesdropping attacks.
  arXiv  Detail & Related papers  (2020-03-23T18:23:12Z)
• Hacking single-photon avalanche detector in quantum key distribution via pulse illumination [6.285329211368446]
  We show an adversary's capability of exploiting the imperfection of the patch itself to bypass the patch. We also analyze the secret key rate under the pulse illumination attack, which theoretically confirmed that Eve can conduct the attack to learn the secret key.
  arXiv  Detail & Related papers  (2020-02-21T06:05:18Z)
• Block Switching: A Stochastic Approach for Deep Learning Security [75.92824098268471]
  Recent study of adversarial attacks has revealed the vulnerability of modern deep learning models. In this paper, we introduce Block Switching (BS), a defense strategy against adversarial attacks based on onity.
  arXiv  Detail & Related papers  (2020-02-18T23:14:25Z)
• Adversarial vs behavioural-based defensive AI with joint, continual and active learning: automated evaluation of robustness to deception, poisoning and concept drift [62.997667081978825]
  Recent advancements in Artificial Intelligence (AI) have brought new capabilities to behavioural analysis (UEBA) for cyber-security. In this paper, we present a solution to effectively mitigate this attack by improving the detection process and efficiently leveraging human expertise.
  arXiv  Detail & Related papers  (2020-01-13T13:54:36Z)

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.