7 Quantum transfer learning to detect passive attacks in SDN-IOT
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S. Karthik
Abstract
An attack on the network presents a potential risk to the integrity of the network security, which may or may not occur. As the field of quantum machine learning has made rapid strides in recent years, quantum learning (QL) has demonstrated quantum benefits in a variety of classification problems. This is due to the fact that variational quantum neural networks are also known as QL. When compared to an IDS that is based on quantum machine learning, an IDS that is based on classical machine learning is both less efficient and less accurate. This chapter discusses quantum transfer learning to detect the passive attacks in SDN-IoT. The use of quantum transfer learning makes the system detect the possible intrusions in the network before the data is traversed into the network environment. The results show an improved accuracy rate than those of state-of-art methods.
Abstract
An attack on the network presents a potential risk to the integrity of the network security, which may or may not occur. As the field of quantum machine learning has made rapid strides in recent years, quantum learning (QL) has demonstrated quantum benefits in a variety of classification problems. This is due to the fact that variational quantum neural networks are also known as QL. When compared to an IDS that is based on quantum machine learning, an IDS that is based on classical machine learning is both less efficient and less accurate. This chapter discusses quantum transfer learning to detect the passive attacks in SDN-IoT. The use of quantum transfer learning makes the system detect the possible intrusions in the network before the data is traversed into the network environment. The results show an improved accuracy rate than those of state-of-art methods.
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- Biographies XI
- List of contributors XIII
- 1 Optimizing the traffic flow in VANETs using deep quantum annealing 1
- 2 Quantum annealing-based routing in UAV network 13
- 3 Cyberbullying detection of social network tweets using quantum machine learning 25
- 4 AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network 37
- 5 Machine learning-based quantum modeling to classify the traffic flow in smart cities 49
- 6 IoT attack detection using quantum deep learning in large-scale networks 67
- 7 Quantum transfer learning to detect passive attacks in SDN-IOT 79
- 8 Intrusion detection framework using quantum computing for mobile cloud computing 97
- 9 Fault-tolerant mechanism using intelligent quantum computing-based error reduction codes 109
- 10 Study of quantum computing for data analytics of predictive and prescriptive analytics models 121
- 11 A review of different techniques and challenges of quantum computing in various applications 147
- 12 Review and significance of cryptography and machine learning in quantum computing 159
- 13 An improved genetic quantum cryptography model for network communication 177
- 14 Code-based post-quantum cryptographic technique: digital signature 193
- 15 Post-quantum cryptography for the detection of injection attacks in small-scale networks 207
- 16 RSA security implementation in quantum computing for a higher resilience 219
- 17 Application of quantum computing for digital forensic investigation 231
- 18 Modern healthcare system: unveiling the possibility of quantum computing in medical and biomedical zones 249
- 19 Quantum computing-assisted machine learning to improve the prediction of cardiovascular disease in healthcare system 265
- 20 Mitigating the risk of quantum computing in cyber security era 283
- 21 IoMT-based data aggregation using quantum learning 301
- Index 319
Kapitel in diesem Buch
- Frontmatter I
- Preface V
- Contents VII
- Biographies XI
- List of contributors XIII
- 1 Optimizing the traffic flow in VANETs using deep quantum annealing 1
- 2 Quantum annealing-based routing in UAV network 13
- 3 Cyberbullying detection of social network tweets using quantum machine learning 25
- 4 AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network 37
- 5 Machine learning-based quantum modeling to classify the traffic flow in smart cities 49
- 6 IoT attack detection using quantum deep learning in large-scale networks 67
- 7 Quantum transfer learning to detect passive attacks in SDN-IOT 79
- 8 Intrusion detection framework using quantum computing for mobile cloud computing 97
- 9 Fault-tolerant mechanism using intelligent quantum computing-based error reduction codes 109
- 10 Study of quantum computing for data analytics of predictive and prescriptive analytics models 121
- 11 A review of different techniques and challenges of quantum computing in various applications 147
- 12 Review and significance of cryptography and machine learning in quantum computing 159
- 13 An improved genetic quantum cryptography model for network communication 177
- 14 Code-based post-quantum cryptographic technique: digital signature 193
- 15 Post-quantum cryptography for the detection of injection attacks in small-scale networks 207
- 16 RSA security implementation in quantum computing for a higher resilience 219
- 17 Application of quantum computing for digital forensic investigation 231
- 18 Modern healthcare system: unveiling the possibility of quantum computing in medical and biomedical zones 249
- 19 Quantum computing-assisted machine learning to improve the prediction of cardiovascular disease in healthcare system 265
- 20 Mitigating the risk of quantum computing in cyber security era 283
- 21 IoMT-based data aggregation using quantum learning 301
- Index 319
