With advent of Quantum Computing, traditional cyber security mechanisms are failing. This paper discusses the effectiveness of certain machine learning models in detecting such quantum attacks on quantum systems. The dataset used is custom generated using qiskit and AerSimulator and it focuses on three states of system, normal state, side-channel attack state and intercept-resend attack state. Machine learning models like logistic regression, neural networks, SVM and XGBoost were trained on the dataset. The highest accuracy observed was 0.81 obtained using XGBoost. These findings highlight the challenges of quantum attack detection and suggest future research directions for improving classification accuracy.
With advent of Quantum Computing, traditional cyber security mechanisms are failing. This paper discusses the effectiveness of certain machine learning models in detecting such quantum attacks on quantum systems. The dataset used is custom generated using qiskit and AerSimulator and it focuses on three states of system, normal state, side-channel attack state and intercept-resend attack state. Machine learning models like logistic regression, neural networks, SVM and XGBoost were trained on the dataset. The highest accuracy observed was 0.81 obtained using XGBoost. These findings highlight the challenges of quantum attack detection and suggest future research directions for improving classification accuracy.
Phishing is a kind of cybercrime where attackers trick unsuspecting network users into revealing sensitive information, resulting in identity theft and financial damage. As this threat continues to grow, artificial intelligence strategies have emerged as a promising solution for its detection in recent years. However, conventional machine learning approaches are starting to show limitations because training on large datasets in standard computing environments may either take too long for accurate results or produce poor accuracy with quick training. The recent advances in the study of the application of quantum computing to machine learning tasks have demonstrated that existing solutions can be strengthened by utilizing the synergy of the two fields, known as Quantum Machine Learning (QML). To explore the potential of this emerging technique, this study implements various QML models integrated with the latest version of Qiskit and presents a detailed execution process along with a comprehensive comparative analysis. The quantitative results obtained from the experiments demonstrate the significant potential of QML models in phishing detection. Furthermore, these state-of-the-art assessments also contribute to future research in improving the composition of QML models applied in the cyber defense field.
The application and assessment of quantum-based anomaly detection models in cybersecurity are examined in this work. The efficacy of quantum machine learning approaches for detecting several cyber threats, including Denial of Service (DoS), Remote-to-Local (R2L), and botnet penetration, using two well-known intrusion detection datasets: NSL-KDD and CICIDS2017. Angle and amplitude encoding techniques are worn to preprocess and instruct traditional data into quantum-readable formats, making it compatible with quantum circuits. Due to the present constraints of Noisy Intermediate-Scale Quantum (NISQ) devices, the models are developed using Qiskit, using parameterized quantum layers via the TwoLocal ansatz, and simulated on classical hardware using Qiskit Aer. Metrics are used to compare performance to traditional machine learning models, such as Support Vector Machines (SVM), Random Forests (RF), and Multilayer Perceptions (MLP). The quantum SVM (QSVM) performs better in classification on both datasets, according to the results, indicating that quantum computing may be used to improve anomaly detection in cybersecurity applications.
In this paper, we apply Quantum Machine Learning to analyze security datasets. We compare cross-models, Quantum Machine Learning (QML) against Classical Machine Learning (CML), performance with increasing data size, and performance with increasing iteration numbers using commonly used machine learning techniques such as Neural Networks (NN), Support Vector Machines (SVM), and Logistic Regression (LR). Our study focuses on assessing the accuracy of QML and CML approaches on real-world security datasets. The results provide light on the advantages and disadvantages of both QML and CML methodologies, with implications for their use in security data analysis. The experimental findings provide useful information on the applicability of QML and CML for security-related applications. The study contributes to the growing field of quantum machine learning research, particularly in the context of security data analysis, and offers helpful advice for academics and practitioners working in this area.
With the potential for quantum computing to completely transform cybersecurity, quantum machine learning is becoming a ground-breaking technology. Cyber-attacks have been successfully countered by traditional network defense systems, which mostly use conventional machine learning (ML) techniques. However, the growing complexity of assaults and the exponential expansion of network data reveal the shortcomings of traditional methods, especially with regard to speed and scalability. By utilizing quantum algorithms like Quantum Support Vector Machines (QSVM), Quantum Neural Networks (QNN), and Variational Quantum Circuits (VQC), Quantum Machine Learning (QML) presents a viable substitute. These methods are especially well-suited for real-time network defense scenarios since they show the capacity to handle high-dimensional data, identify complex patterns, and increase computational efficiency. In this research, the effectiveness of QML techniques in network defense is thoroughly examined and contrasted with that of traditional machine learning techniques. The study emphasizes how QML may surpass conventional models in terms of accuracy, scalability, and resilience against sophisticated threats by concentrating on use cases like intrusion detection, malware analysis, and anomaly detection. The necessity for hybrid quantum-classical models and the present constraints of quantum hardware are among the difficulties that are discussed in the study. This study highlights the revolutionary potential of QML in strengthening network defense mechanisms and identifies crucial areas for further research, paving the road for a secure digital future by combining theoretical ideas with experimental discoveries.
Abstract Recently, machine learning has been widely applied in the field of quantum information, notably in tasks such as entanglement detection, steering characterization, and nonlocality verification. However, few studies have focused on utilizing machine learning to detect quantum information masking (QIM). In this work, we investigate supervised machine learning for detecting QIM in both pure and mixed qubit states. For pure qubit states, we randomly generate the corresponding density matrices and train an XGBoost model to detect QIM. For mixed qubit states, we improve the XGBoost method by optimizing the selection of training samples. The experimental results demonstrate that our approach achieves higher classification accuracy. Furthermore, we analyze the area under the curve of the receiver operating characteristic curve for this method, which further confirms its classification performance.
This study investigates the synergy between Classical Machine Learning (CML) and Quantum Machine Learning (QML) in analyzing security datasets, conducting a comparative analysis using models based on QML and CML to evaluate their performance as data sizes and iteration counts increase.The author, specifically, employs popular machine learning methods, including Support Vector Machines (SVM), Neural Networks (NN), and Logistic Regression (LR), to assess these techniques on real-world security datasets, such as network intrusion detection data and malware classification logs.The primary focus is determining the effectiveness and efficiency of QML and CML approaches in handling large-scale security data.Through rigorous experimentation, the study highlights the benefits and drawbacks of both QML and CML, indicating that while QML offers significant speedups in processing times for large datasets due to quantum parallelism, it faces challenges in terms of hardware accessibility and noise sensitivity, while CML methods, though slower with massive data, benefit from mature algorithms and more robust infrastructure.The outcomes provide critical insights into the practicality of applying QML and CML to security-related applications, demonstrating that QML techniques can outperform CML in specific scenarios, such as real-time threat detection, due to their superior computational efficiency.However, the current limitations of quantum hardware suggest that CML remains more practical for many applications in the short term.This work significantly advances the state of the art in Quantum Machine Learning.It offers vital guidance for practitioners and researchers in security data analysis, underscoring the potential of QML to revolutionize security data processing while acknowledging the ongoing need for advancements in quantum computing technology.