Malware Classification and Detection using Quantum Neural Network (QNN)Online
Quantum Machine Learning (QML) is an emerging field that involves training a parameterized quantum circuit in order to analyze quantum or classical datasets. QML has generated great excitement in recent years with the aim to develop Machine Learning (ML) models specifically designed for quantum computers that would allow exponential advantage in making accurate predictions on quantum data. Cybersecurity threats are evolving with the advancement of computing technology concurrently. Utilizing the concept of proactive prevention and early detection of security vulnerabilities may provide advantages to mitigate cybersecurity risk. In this paper, we adopt the Quantum Neural Network (QNN), a subset of QML for malware classification and detection. We use Google Collab as IDE and utilize an open-source ClaMP Dataset from Kaggle. We demonstrate our repository to classify and detect malware using a quantum neural network (QNN) and the result indicates that we achieved an accuracy of 94% which shows initial efficiency in quantum machine learning (QML) that would improve over time.
Jobair Hossain currently pursuing an MS degree in Software Engineering and working as a Graduate Research Assistant (GRA) under the supervision of professors Hossain Shahriar and Maria Valero at Kennesaw State University. He also worked as an R&D Software Engineering Intern at Material Handelings System Inc. Prior to joining KSU, he also has working experience with the United Nations Secretariat, New York. He received his Bachelor’s degree in Software Engineering from Sultan Idris Education University, Malaysia.
Jobair’s general research interests focus on the field of software engineering, and emphasis has spanned the areas of blockchain, machine learning, IoT, health informatics, software, and cyber security. In the past three years, he has published his research works at various IEEE/ACM conferences including SERA, COMPSAC, BigData, ICDH, and ICHI. He won the prestigious NSF Travel Grant Award at IEEE Big Data 2021, NSF Student Travel Award for IEEE CHASE 2022, Special Paper Award at IEEE ICDH 2021, and Best Paper Award at IEEE ICHI 2022 conferences.
