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)
• 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)
• 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)
• 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)
• 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)
• 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.