The rapid evolution of malware techniques makes conventional defenses increasingly ineffective. Although classical machine learning systems improve detection, they struggle when attempting to classify malware. Approaches that integrate fuzzy logic into machine learning models, specifically neural networks, alleviate this problem. However, this solution still exhibits frequent instability in the classification process. In this thesis, we introduce a novel Quantum Deep Fuzzy Neural Network (QDFNN) that combines quantum machine learning and fuzzy logic layer, enabling probabilistic reasoning through superposition and conditional activation gates to capture complex malware patterns. We derive multiple variants of the model and evaluate them on a popular Malware Detection dataset. The fully quantum variant (QDFNN) achieves an accuracy of 93.6%, outperforming several baseline models, including a hybrid variant, H-QDFNN, a shallow variant of QDFNN (QFNN), a shallow hybrid variant, H-QFNN, a quantum non-fuzzy variant, QNN, and several classical models, such as ANN, SVM, and logistic regression. We also extend our evaluation to include other important evaluation metrics, such as precision, recall, F1 score, and ROC-AUC, for which our proposed QDFNN outperformed all other baseline models. Finally, we evaluated the resilience of the proposed model against quantum noise by adopting six NISQ noise models and comparing our model with its variants, where we observed reasonable accuracy levels of the proposed model. This thesis demonstrates that integrating quantum fuzzy logic yields a potential competitive advantage, especially against conventional classical models, paving the way toward practical quantum-based malware detection systems.
The rapid evolution of malware techniques makes conventional defenses increasingly ineffective. Although classical machine learning systems improve detection, they struggle when attempting to classify malware. Approaches that integrate fuzzy logic into machine learning models, specifically neural networks, alleviate this problem. However, this solution still exhibits frequent instability in the classification process. In this thesis, we introduce a novel Quantum Deep Fuzzy Neural Network (QDFNN) that combines quantum machine learning and fuzzy logic layer, enabling probabilistic reasoning through superposition and conditional activation gates to capture complex malware patterns. We derive multiple variants of the model and evaluate them on a popular Malware Detection dataset. The fully quantum variant (QDFNN) achieves an accuracy of 93.6%, outperforming several baseline models, including a hybrid variant, H-QDFNN, a shallow variant of QDFNN (QFNN), a shallow hybrid variant, H-QFNN, a quantum non-fuzzy variant, QNN, and several classical models, such as ANN, SVM, and logistic regression. We also extend our evaluation to include other important evaluation metrics, such as precision, recall, F1 score, and ROC-AUC, for which our proposed QDFNN outperformed all other baseline models. Finally, we evaluated the resilience of the proposed model against quantum noise by adopting six NISQ noise models and comparing our model with its variants, where we observed reasonable accuracy levels of the proposed model. This thesis demonstrates that integrating quantum fuzzy logic yields a potential competitive advantage, especially against conventional classical models, paving the way toward practical quantum-based malware detection systems.
This study explores the application of quantum machine learning (QML) algorithms to enhance cybersecurity threat detection, particularly in the classification of malware and intrusion detection within high-dimensional datasets. Classical machine learning approaches encounter limitations when dealing with intricate, obfuscated malware patterns and extensive network intrusion data. To address these challenges, we implement and evaluate various QML algorithms, including Quantum Neural Networks (QNN), Quantum Support Vector Machines (QSVM), and hybrid Quantum Convolutional Neural Networks (QCNN) for malware detection tasks. Our experimental analysis utilized two datasets: the Intrusion dataset, comprising 150 samples with 56 memory-based features derived from Volatility framework analysis, and the ObfuscatedMalMem2022 dataset, containing 58,596 samples with 57 features representing benign and malicious software. Remarkably, our QML methods demonstrated superior performance compared to classical approaches, achieving accuracies of 95% for QNN and 94% for QSVM. These quantum-enhanced methods leveraged quantum superposition and entanglement principles to accurately identify complex patterns within highly obfuscated malware samples that were imperceptible to classical methods. To further advance malware analysis, we propose a novel real-time malware analysis framework that incorporates Quantum Feature Extraction using Quantum Fourier Transform, Quantum Feature Maps, and Classification using Variational Quantum Circuits. This system integrates explainable AI methods, including GradCAM++ and ScoreCAM algorithms, to provide interpretable insights into the quantum decision-making processes.
There is a pressing need for anomaly detection algorithms that can handle large amounts of diverse traffic data due to the quick growth of networked systems and the sophistication of contemporary cyberattacks. Despite the widespread use of conventional machine learning techniques for network intrusion detection, feature representation constraints, dataset imbalance, and the computational expense of scaling to vast problem spaces sometimes restrict their efficacy. In the meantime, new developments in quantum-assisted computation have opened up exciting possibilities for using quantum state changes to improve the feature learning process. Using the NF-UNSWNB15-v2 benchmark, this research presents HybridQCNN, a unique quantum-classical computing framework for binary classification of network traffic.The proposed architecture combines standard preprocessing techniques with an 8-qubit Parameterized Quantum Circuit (PQC) featuring multiple variational layers, where IsingXX and IsingYY entangling operations are applied to encode interfeature correlations, and a subsequent fully connected classical layer handles the final binary decision. A rigorous leakage-free data partitioning protocol was enforced, ensuring that all preprocessing transformations—including feature encoding, normalization, and threshold computation—were derived exclusively from the training subset, thereby preventing any information leakage from held-out partitions during evaluation. Experimental validation on a class-balanced subset of 60,000 traffic records yielded a classification accuracy of 97.33%, an F1-score of 0.9730, and a malicious traffic detection precision of 98.38%. Furthermore, the model achieved an AUC-ROC of 0.9861, reflecting strong class-level discriminability. Despite this performance, the entire model contains only 489 trainable parameters, underscoring the parameter efficiency of hybrid architectures relative to conventional deep learning baselines. These results suggest that integrating variational quantum circuits with classical inference modules provides an effective and scalable solution for network anomaly detection.
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.
For intelligent surveillance videos, anomaly detection is extremely important. Deep learning algorithms have been popular for evaluating real-time surveillance recordings, like traffic accidents, and criminal or unlawful incidents such as suicide attempts. Nevertheless, Deep learning methods for classification, like convolutional neural networks, necessitate a lot of computing power. Quantum computing is a branch of technology that solves abnormal and complex problems using quantum mechanics. As a result, the focus of this research is on developing a hybrid quantum computing model which is based on deep learning. This research develops a Quantum Computing-based Convolutional Neural Network (QC-CNN) to extract features and classify anomalies from surveillance footage. A Quantum-based Circuit, such as the real amplitude circuit, is utilized to improve the performance of the model. As far as my research, this is the first work to employ quantum deep learning techniques to classify anomalous events in video surveillance applications. There are 13 anomalies classified from the UCF-crime dataset. Based on experimental results, the proposed model is capable of efficiently classifying data concerning confusion matrix, Receiver Operating Characteristic (ROC), accuracy, Area Under Curve (AUC), precision, recall as well as F1-score. The proposed QC-CNN has attained the best accuracy of 95.65 percent which is 5.37% greater when compared to other existing models. To measure the efficiency of the proposed work, QC-CNN is also evaluated with classical and quantum models.
Classical machine learning (CML) has been extensively studied for malware classification. With the emergence of quantum computing, quantum machine learning (QML) presents a paradigm-shifting opportunity to improve malware detection, though its application in this domain remains largely unexplored. In this study, we investigate two hybrid quantumclassical models - a Quantum Multilayer Perceptron (QMLP) and a Quantum Convolutional Neural Network (QCNN), for malware classification. Both models utilize angle embedding to encode malware features into quantum states. QMLP captures complex patterns through full qubit measurement and data reuploading, while QCNN achieves faster training via quantum convolution and pooling layers that reduce active qubits. We evaluate both models on five widely used malware datasets -API-Graph, EMBER-Domain, EMBER-Class, AZ-Domain, and AZ-Class, across binary and multiclass classification tasks. Our results show high accuracy for binary classification95−96%on API-Graph, 91-92% on AZ-Domain, and77%on EMBER-Domain. In multiclass settings, accuracy ranges from 91.6-95.7% on API-Graph, 41.7-93.6% on AZ-Class, and 60.7-88.1% on EMBER-Class. Overall, QMLP outperforms QCNN in complex multiclass tasks, while QCNN offers improved training efficiency at the cost of reduced accuracy.
Malware detection is an important topic of current cybersecurity, and Machine Learning appears to be one of the main considered solutions even if certain problems to generalize to new malware remain. In the aim of exploring the potential of quantum machine learning on this domain, our previous work showed that quantum neural networks do not perform well on image-based malware detection when using a few qubits. In order to enhance the performances of our quantum algorithms for malware detection using images, without increasing the resources needed in terms of qubits, we implement a new preprocessing of our dataset using Grayscale method, and we couple it with a model composed of five distributed quantum convolutional networks and a scoring function. We get an increase of around 20 \% of our results, both on the accuracy of the test and its F1-score.