13 An improved genetic quantum cryptography model for network communication
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Puneet Garg
Abstract
Any wireless or open space network’s biggest problem is security. When the network has limited coverage and high-speed mobility, communication becomes even more crucial. One similar network with a lower sensing limit and designed for indoor use is WPAN. In order to increase the communication’s dependability and security, this paper suggests a quantum-inspired encoded communication. The work model is established for a highly mobile, randomly scattered WPAN network. The initial step of this model performs node-level characterization under the parameters of coverage, stability, and load. Based on this analysis, the initial population sequence is generated and based on this generation, communication can be performed. The genetic model applied on this population pool is processed to generate the effective communication pair. After generating the population pair, quantum technique is applied to capture the characteristics of the node pair to generate the key. Finally, the data is encoded using the quantum key-based SHA. Over the network, this encoded communication is carried out.
Abstract
Any wireless or open space network’s biggest problem is security. When the network has limited coverage and high-speed mobility, communication becomes even more crucial. One similar network with a lower sensing limit and designed for indoor use is WPAN. In order to increase the communication’s dependability and security, this paper suggests a quantum-inspired encoded communication. The work model is established for a highly mobile, randomly scattered WPAN network. The initial step of this model performs node-level characterization under the parameters of coverage, stability, and load. Based on this analysis, the initial population sequence is generated and based on this generation, communication can be performed. The genetic model applied on this population pool is processed to generate the effective communication pair. After generating the population pair, quantum technique is applied to capture the characteristics of the node pair to generate the key. Finally, the data is encoded using the quantum key-based SHA. Over the network, this encoded communication is carried out.
Chapters in this book
- 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
Chapters in this book
- 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
