2026· Computers, Materials & Continua· Vol 89, pp. 1-10· 0 citations· 28 references
TL;DR
The proposed framework can improve threat detection and system resilience in critical infrastructure contexts and support the use of kernel-based quantum-inspired representations as a tunable early-warning layer for PSAP traffic monitoring, while also showing that threshold calibration and operational context remain necessary before deployment.
Abstract
: Increasing digitalization exposes critical infrastructure to sophisticated cyber threats, requiring new approaches to improving security and resilience. While classical machine learning techniques have shown promise in anomaly detection and threat mitigation, emerging quantum-inspired methods offer new opportunities to enhance detection capabilities by leveraging principles derived from quantum computing. The objective of this work is to propose a model for the early detection of Telephony Denial of Service attacks using a combination of classical algorithms and quantum computing-based techniques. Call records are embedded into a low-dimensional quantum feature space using spatial and temporal attributes, mapped through a quantum kernel in two modalities: Quantum-Inspired Kernel or Qiskit-Simulated Quantum Kernel. This approach enables the identification of subtle and complex patterns that can be difficult to capture using classical methods alone, without the need for fully fault-tolerant quantum hardware. The anomaly decision threshold is empirically calibrated, allowing the detector to balance early sensitivity against false-alarm control under the evaluated Public Safety Answering Points (PSAP) traffic conditions. The evaluation shows that the proposed framework can improve threat detection and system resilience in critical infrastructure contexts. The results support the use of kernel-based quantum-inspired representations as a tunable early-warning layer for PSAP traffic monitoring, while also showing that threshold calibration and operational context remain necessary before deployment.
The framework provides a pragmatic, classifier-agnostic defense layer deployable on freely accessible cloud platforms (Google Colab) without specialized quantum hardware, and offers viable post-quantum hardening for security-critical applications.
Soha Rawas, Mohammed Al Saleh, Agariadne Dwinggo Samala et al.· Applied Computing and Inform...· 0 citations
The number of digital communications and cloud infrastructure is rising rapidly, cyber attacks have become much more advanced and frequent. Conventional approaches for intrusion detection cannot detect the evolving threats due to highly dimensional traffic patterns and complex attacker behavior. To tackle these issues, we introduce a hybrid quantum-classical intrusion detection approach by integrating classical and quantum machine learning algorithms to build an efficient intrusion detection system. Our model takes advantage of UNSW-NB15 Cybersecurity benchmark dataset that contains modern network attack vectors including exploit, denial of service attack, reconnaissance, shellcode, and backdoor attacks. For data preparation, we implement various data processing methods including categorical encoding, feature scaling, and dimensionality reduction. For comparison purposes, Random Forest algorithm is used as a classical detection method and variational quantum classifier based on Qiskit software library is utilized as our quantum learning model. Our experimental results show that our hybrid model is highly accurate in detecting the attacks. This work provides a comparative analysis between classical and quantum learning models demonstrating the promising future of quantum computers in cyber attacks detection tasks.
Pravin P, Kuldeep Chowdary Raavi, Meher Deepika Uppaluri et al.· 2026 7th International Confe...· 0 citations
Their combination of adversarial AI attacks with cryptographically relevant quantum computers endangers the traditional public-key infrastructure. This paper proposes a hybrid system that combines real-time deep learning-based threat detection and Quantum Key Distribution (QKD) to secure communication systems. A convolutional neural network with attention mechanism detects network anomalies with 98.4% accuracy on the CIC-IDS-2017 dataset. At the same time, a decoy-state BB84 QKD protocol is used to create symmetric keys on a modeled 40 km fiber channel with a quantum bit error rate of less than 2.5%. A new query fragment caching algorithm cuts the key delivery latency by a factor of 37. Empirical evidence demonstrates the system throughput of 850 Mbps and key generation rate of 12.4 kbps providing a viable roadmap to information-theoretically secure communication when using active cyber threats.
Naga Naveena Chennupati· International Conference Com...· 0 citations
: The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these challenges, this paper proposes a Residual Hybrid Quantum-Classical Neural Network (RHQ-CNN) for efficient intrusion detection in Edge-Industrial Internet of Things (Edge-IIoT) environments under idealized quantum simulation conditions. The proposed framework employs a classical neural network encoder to compress high-dimensional network traffic into a compact latent representation suitable for quantum processing. A variational quantum circuit with serial data re-uploading is then utilised to model complex non-linear decision boundaries without increasing qubit requirements. In addition, a residual connection fuses classical and quantum representations to improve training stability and preserve latent feature information. The model is evaluated on the Edge-IIoTset dataset and achieves a test accuracy of 99.94%, with high weighted performance across the 15-class detection task, although the extremely low-sample Fingerprinting class remains comparatively more challenging. Additional controlled ablation, deployment-cost, and bootstrap confidence interval analyses demonstrate the test-set metric stability and computational trade-offs of the proposed architecture. These findings highlight the potential of hybrid quantum-classical models for next-generation cybersecurity in industrial IoT systems.
Alanoud Al Mazroa, A. Alamoudi, N. Karabayev et al.· Computers, Materials & C...· 0 citations
Conventional intrusion detection systems lack the ability to detect advanced cyberattacks because they rely on traditional computational models and linear correlations of features. This work proposes a simulation-based Quantum Intrusion Detection framework using the principles of quantum computing, with the main focus on multi-qubit entanglement and superposition to improve the detection of network anomalies. The multi-layered quantum circuit has three unique quantum state configurations: extended Bell states for 4-qubit pairwise correlations, Greenberger-Horne-Zeilinger (GHZ) states for 6-qubit multipartite entanglement and combined 8-qubit circuits with Quantum Fourier Transform for temporal analysis. The pipeline first converts classical network traffic features to quantum-compatible representations using statistical aggregation and correlation matrix construction. Cybersecurity datasets are handled by translating network features and grouping them into protocol type, packet size, interarrival time, source IP entropy, destination port, TCP flag, payload entropy, and flow duration using statistical aggregation. Quantum entanglement breaking is the main indicator of network anomaly, where malicious traffic patterns disturb the learned quantum correlations in baseline training done using normal traffic. The anomaly detection utilizes multi-level quantum analysis that integrates probability divergence measurement by Hellinger distance, entanglement deviation monitoring via concurrence computation and correlation breaking detection between quantum state pairs. The system uses weighted decision fusion with increased weight on 8-qubit unified circuit results. The system is evaluated through simulation using the NSL-KDD dataset, achieving an overall detection accuracy of 85%. This work serves as a baseline for investigating how quantum computing can be used in the field of cybersecurity in future quantum-enabled environments.
Harshini K, I. Jahan M A, Gaurav Kumar Bharti· Women in Optics and Photonic...· 0 citations
The escalation of security attacks has led to a growing complexity and posed serious challenges for traditional IDS paradigms to securing and sustaining a resilient network infrastructure. Current solutions are generally inefficient at optimizing detection parameters, cannot detect attacks that have previously not been encountered and do not offer clear decision making processes. In this work, a framework for optimization of secure data protection using quantum computing is proposed which combines machine learning, anomaly detection, explainable AI and quantum computing inspired optimization techniques in a single cybersecurity framework to overcome these limitations. Selected network traffic from CICIDS2017 data set is used in the system to detect various types of attacks such as Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), PortScan and WebAttack. The multi-class intrusion detection uses an XGBoost (Extreme Gradient Boosted) Classifier to learn discriminative patterns from network flow features. The Quantum Approximate Optimization Algorithm (QAOA) is integrated to optimize certain model parameters (learning rate, tree depth and decision thresholds) that can enhance detection performance. Moreover, an Isolation Forest model is run concurrently to detect zero day and unknown anomalies not found in the training set. To enhance the interpretability, SHAP-based explainability is incorporated to measure the network's contribution to each prediction by its features. All the framework is deployed via Flask and is displayed on an interactive dashboard that shows attack classifications, threat risk scores, optimized configurations and explanatory insights. The hybrid architecture proposed shows a novel combination of classical artificial intelligence and quantum optimization techniques to create an adaptive, explainable, and intelligent cybersecurity solution.
M. Anusha, M. Neha, PG Student et al.· 2026 International Conferenc...· 0 citations