Developing intelligent cyber threat detection systems through quantum computing

Abstract

In the face of increasingly sophisticated cyber threats, traditional detection systems often fall short in protecting critical supply chains. This research presents the development and evaluation of an intelligent cyber threat detection system integrating Quantum Computing (QC) and Artificial Intelligence (AI). The proposed system significantly enhances detection accuracy, reduces latency, and improves resource efficiency compared to traditional methods. Quantum algorithms, such as Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN), demonstrated superior performance with accuracies of 95.2% and 96.7%, respectively. The system achieved high detection rates for various cyber threats, including malware, phishing, ransomware, and advanced persistent threats (APTs), with reduced false positive rates. The integration of QC also resulted in faster threat detection and response times, with system latency halved across key components. These advancements provide substantial benefits for cyber threat response in supply chains, ensuring robust protection of financial transactions and critical infrastructure. The enhanced scalability and efficiency make the system a valuable asset for safeguarding the United States' financial sector against sophisticated cyber-attacks.