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.
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.
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.
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.
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.
Reliable detection of malware is a fundamental component of cybersecurity in the modern world. Models have to be able to identify new and sophisticated malware strains, even among large and complex datasets, while having accuracy, efficiency, and understandability. Review of previous research, based on direct comparison, of Quantum>Support Vector Machines, Quantum Neural>Networks,and hybrid modeles such-as Quantum Multilayer Perceptron is the basis of the proposed clear and concise QML framework for malware detection. Basic research by Cai et al. on QSVM demonstrates high classification accuracy. In exploration of bases, QNN has passed the trials and indicated the need for improvement due to data re-uploading. Finally, further experiments on QMLP and QCNN researched the relationship between classification accuracy and model training cost. XAI added the level of interpretability and the analysis demonstrated an O(log n) computational leverage, which is the key ingredient that maintains this field. These combinations of research construct the unified framework of the strong classification strength of QSVM, the architecture flexibility of QNN, and the insights from XAI. This leads to a more robust accuracy, explainability, and reliability of existing and future malware detection systems.
Deep learning algorithms and models have made an impact in the area of AI and machine learning, one among them is CNN. CNN is extensively used in the area of image recognition and object detection for classification purposes. CNN is composed of several layers of filters to get feature maps of input data, yet foremost and crucial one is convolutional layer, hence the name Convolutional neural networks. However, the growth of quantum computing and quantum neural network in deep learning is limited. Three main obstacles that limit the growth of these are, first is due to the lack of real-time quantum computers to experiment with. Second is the improper training algorithms and at last, non-linearity nature of the neural networks. This paper introduces a novel approach to begin one's journey in quantum computing, along with solutions and developments. This work provides a detailed description of architectures, frameworks and algorithms used for implementing a QCNN model. The research was made regarding image recognition and object detection using QCNN and found that QCNN can increase the computational speeds with better performance metrics compared to classical computational methods. This paper also debates about applications of QCNN in computer vision, signal and image processing, Pharmaceuticals, Cryptography and various other fields. This study also explains Key players and future work in developing quantum computers, quantum computing algorithms, software and hardware support to implement QCNN in various applications.
Abstract Quantum neural network fully utilize the respective advantages of quantum computing and classical neural network, providing a new path for the development of artificial intelligence. In this paper, we propose a modified lightweight quantum convolutional neural network (QCNN), which contains a high-scalability and parameterized quantum convolutional layer and a quantum pooling circuit with quantum bit multiplexing, effectively utilizing the computational advantages of quantum systems to accelerate classical machine learning tasks. The experimental results show that the classification accuracy (precision, F1-score) of this QCNN on DataCon2020, Ember and BODMAS have been improved to 96.65% (94.3%, 96.74%), 92.4% (91.01%, 92.53%) and 95.6% (91.99%, 95.78%), indicating that this QCNN has strong robustness as well as good generalization performance for malicious code detection, which is of great significance to cyberspace security.