I am a young researcher in the field of cybersecurity, holding a Bachelor of Engineering in Information Security. Throughout my academic journey, I have built a solid foundation in malware analysis, machine learning-based detection systems, and intrusion detection. My previous work includes designing advanced frameworks that fuse diverse feature types—such as image, tabular, and graph data—for effective Android malware classification. Additionally, I proposed a semi-synthetic dataset generation approach to enhance the training of network intrusion detection systems (NIDS), addressing the limitations of outdated and imbalanced public datasets.

Since 2025, I have been expanding my research interests into the field of quantum computing, with a particular focus on two main directions: (1) Quantum attack modeling, aiming to explore vulnerabilities in quantum and hybrid classical-quantum models through the simulation of practical attack scenarios; (2) Development and analysis of quantum models for machine learning and security, with an emphasis on understanding the capabilities and limitations of current quantum algorithms in addressing security challenges.

I am especially interested in the integration of classical and quantum machine learning techniques to build intelligent defense systems, while also investigating potential security risks associated with quantum algorithms such as Quantum Neural Networks (QNN), Variational Quantum Eigensolvers (VQE), and Quantum Approximate Optimization Algorithms (QAOA) when deployed in real-world environments.