Quantum Computing Cybersecurity— Exploring the Potential of Quantum Machine Learning Techniques in Intrusion Detection
Aditya Jain, Ligandro Singh Yumnam -, G. Usha
The field of cybersecurity faces increasingly sophisticated threats, necessitating innovative approaches to intrusion detection. Quantum computing, with its unparalleled computational power, offers a promising avenue for addressing these challenges. This paper delves into the intersection of quantum computing and cybersecurity, specifically focusing on the potential of quantum machine learning techniques in intrusion detection. Traditional intrusion detection systems rely on classical computing, which may struggle to keep pace with the evolving tactics of cyber attackers. Quantum machine learning leverages the principles of quantum mechanics to enhance the speed and efficiency of data analysis. By harnessing quantum bits or qubits, these techniques exhibit exponential processing capabilities that could revolutionize cybersecurity. In this chapter, we explore various aspects of quantum machine learning for intrusion detection, including quantum feature selection, quantum clustering, and quantum anomaly detection. We discuss the advantages and limitations of applying quantum computing in this context, highlighting the potential for faster and more accurate threat detection. Furthermore, we examine the challenges associated with implementing quantum machine learning algorithms in real-world cybersecurity scenarios, such as the requirement for quantum hardware and the need for quantum-resistant cryptographic measures. This chapter aims to provide insights into the evolving landscape of quantum computing in cybersecurity and offers a road map for future research directions. By integrating the transformative potential of quantum machine learning, we aspire to bolster the resilience of intrusion detection systems against emerging cyber threats.
CRC Press eBooks•Aug 2, 2024•4 citations