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Quantum time inversion to prevent DDoS attacks: A potentially realizable TENET technology

Del Rajan

Published Nov 22, 2021• 0 citations
OpenAlex

Abstract

An effect known as time inversion was introduced in Christopher Nolan's film TENET, and here we demonstrate that such an effect exists for quantum information. We do this by investigating the von Neumann entropy of experimentally realized photons that are entangled in time. Using these findings, we design a technology called a Turnstile and apply it to detect Distributed Denial-of-Service (DDoS) attacks in quantum networks. Pragmatically, our work can be viewed as formulating the quantum analogue of the Shannon entropic DDoS detection systems used in classical networks.

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An effect known as time inversion was introduced in Christopher Nolan's film TENET, and here we demonstrate that such an effect exists for quantum information. We do this by investigating the von Neumann entropy of experimentally realized photons that are entangled in time. Using these findings, we design a technology called a Turnstile and apply it to detect Distributed Denial-of-Service (DDoS) attacks in quantum networks. Pragmatically, our work can be viewed as formulating the quantum analogue of the Shannon entropic DDoS detection systems used in classical networks.
Distributed Denial-of-Service (DDoS) attacks can occur in quantum networks, which can pose a significant threat to its key distribution protocols. We introduce a quantum analogue of a classical entropic DDoS detection system, and apply it in the context of detecting an attack on a quantum network. In particular, we examine DDoS attacks on a quantum repeater and harness the associated entanglement entropy for the detection system. Our method contributes to the applicability of quantum information from the domain of data security to the area of network security.
Distributed Denial-of-Service (DDoS) attacks are a significant issue in classical networks. These attacks have been shown to impact the critical infrastructure of a nation, such as its major financial institutions. The possibility of DDoS attacks has also been identified for quantum networks. In this theoretical work, we introduce a quantum analogue of classical entropic DDoS detection systems and apply it in the context of detecting an attack on a quantum network. In particular, we examine DDoS attacks on a quantum repeater and harness the associated entanglement entropy for the detection system. Our results extend the applicability of quantum information from the domain of data security to the area of network security.
Motivated by the growing threat of distributed denial-of-service (DDoS) attacks and the emergence of quantum computing, this study introduces a novel “quanvolutional autoencoder” architecture for learning representations. The architecture leverages the computational advantages of quantum mechanics to improve upon traditional machine learning techniques. Specifically, the quanvolutional autoencoder employs randomized quantum circuits to analyze time-series data from DDoS attacks, offering a robust alternative to classical convolutional neural networks. Experimental results suggest that the quanvolutional autoencoder performs similarly to classical models in visualizing and learning from DDoS hive plots and leads to faster convergence and learning stability. These findings suggest that quantum machine learning holds significant promise for advancing data analysis and visualization in cybersecurity. The study highlights the need for further research in this fast-growing field, particularly for unsupervised anomaly detection.
Within quantum networks (employing quantum key distribution - QKD), the attacker has no chance to get hold of the established keys (most commonly used as one time pads) without being detected by the communicating parties. So the attacker might change his strategy to denial of service in order to inhibit the efficient usage of the quantum-key-distribution-links and one-time-pad-encryption and hence to force the use of classical (a)symmetric cryptography. In this paper we will propose a method for strengthening classical cryptographic algorithms by the use of small amounts of one time pads, so that these algorithms can be used for an emergency operation when suffering a Denial-of-Service attack and during the time needed to fix it. Additional scenarios of using the presented approach include the down time of QKD-links or QKD-devices in case of failure or maintenance.
Communication systems are subject to adversarial attacks since malevolent adversaries might harm and disrupt legitimate transmissions intentionally. Of particular interest in this paper are so-called denial-of-service (DoS) attacks in which the jammer completely prevents any transmission. Arbitrarily varying classical-quantum channels, providing a suitable model to capture the jamming attacks of interest, are studied. Algorithmic detection frameworks are developed based on Turing machines and also Blum-Shub-Smale (BSS) machines, where the latter can process and store arbitrary real numbers. It is shown that Turing machines are not capable of detecting DoS attacks. However, BSS machines are capable thereof implying that real number signal processing enables the algorithmic detection of DoS attacks.
In recent years, we have seen an increase in computer attacks through our communication networks worldwide, whether due to cybersecurity systems’ vulnerability or their absence. This paper presents three quantum models to detect distributed denial of service attacks. We compare Quantum Support Vector Machines, hybrid QuantumClassical Neural Networks, and a two-circuit ensemble model running parallel on two quantum processing units. Our work demonstrates quantum models’ effectiveness in supporting current and future cybersecurity systems by obtaining performances close to 100%, being 96% the worst-case scenario. It compares our models’ performance in terms of accuracy and consumption of computational resources.