book

Quantum-Safe Cryptography Algorithms and Approaches

Name: Quantum-Safe Cryptography Algorithms and Approaches
ISBN: 9783110798364

by Satya Prakash Yadav, Raghuraj Singh, Vibhash Yadav, Fadi Al-Turjman, Swarn Avinash Kumar

August 2023

Intermediate to advanced

344 pages

12h 6m

English

De Gruyter

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Biographies
List of contributors
J. Albert Mayan, G. Swamy Reddy, G. Madhava Rao 1 Optimizing the traffic flow in VANETs using deep quantum annealing
1.1 Introduction1.2 Related works1.3 Methods1.3.1 Deep quantum computing1.4 Evaluation1.5 Conclusion
G. R. Thippeswamy, M. P. Preethi, Buli Yohannis Tasisa 2 Quantum annealing-based routing in UAV network
2.1 Introduction2.2 Related works2.3 Methods2.3.1 Solution2.4 Evaluation2.5 Conclusion
R. Menaka, S. Thenmalar, V. Saravanan 3 Cyberbullying detection of social network tweets using quantum machine learning
3.1 Introduction3.2 Related works3.3 Methods3.3.1 Data collection3.3.2 Cyberbullying annotation3.3.3 Data pre-processing3.3.4 Classification3.4 Evaluation3.5 Conclusion
M. Sunil Kumar, Harsha B K, Leta Tesfaye Jule 4 AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network
4.1 Introduction4.2 Related works4.3 Methods4.3.1 Monitoring4.3.2 Detection4.3.3 Quantum convolutional neural network4.4 Evaluation4.5 Conclusion
N. Karthikeyan, P. Sivaprakash, K. Periyakaruppan, M. Shobana 5 Machine learning-based quantum modeling to classify the traffic flow in smart cities
5.1 Introduction5.2 Related works5.3 Proposed methods5.3.1 Quantum modeling5.4 Results and discussion5.5 Conclusion
Deepali Virmani, T. Nadana Ravishankar, Mihretab Tesfayohanis 6 IoT attack detection using quantum deep learning in large-scale networks
6.1 Introduction6.2 Related works6.3 Methods6.3.1 Pre-processing technique6.3.2 Classification6.3.3 Long short-term memory (LSTM)6.4 Evaluation6.5 Conclusion
S. Karthik, Thenmozhi, R. M. Bhavadharini, T. Sumathi 7 Quantum transfer learning to detect passive attacks in SDN-IOT
7.1 Introduction7.2 Related works7.3 Methods7.3.1 Quantum transfer learning7.3.2 Quantum state encoding (QSE)7.3.3 Variational quantum circuit7.4 Results and discussion7.5 Conclusion
Kumari Manisha, V. Dhanunjana Chari, Leta Tesfaye Jule 8 Intrusion detection framework using quantum computing for mobile cloud computing
8.1 Introduction8.2 Related works8.3 Methods8.3.1 Quantum Intelligence8.4 Evaluation8.5 Conclusion

Swati Tiwari, Kamlesh Kumar Yadav, D. Palanikkumar 9 Fault-tolerant mechanism using intelligent quantum computing-based error reduction codes
9.1 Introduction9.2 Related works9.3 Methods9.4 Evaluation9.5 Conclusion
Kaushal Kishor 10 Study of quantum computing for data analytics of predictive and prescriptive analytics models
10.1 Introduction 10.1.1 Predictive vs. prescriptive analytics10.1.2 Problems that predictive analytics vs. prescriptive analytics will address10.1.3 Descriptive and exploratory analysis10.2 The distinction of technology needs10.3 A long-term decision-making guide using prescriptive analytics10.3.1 Sentiment analysis10.3.2 Machine learning provides prediction10.4 Materials and methods10.4.1 Algorithms for deep learning10.4.2 Regression10.4.3 K-Nearest neighbors10.4.4 Naïve Bayes10.4.5 Support vector machine10.4.6 Artificial neural networks10.4.7 Hybrid models10.5 Optimization technique10.5.1 Demystification of optimization techniques10.5.2 Strategies based on one-of-a-kind solutions10.5.3 Approaches focused on the population10.5.4 Complexity in mathematical optimization10.6 Methods for statistical optimization10.6.1 Stochastic gradient descent (SGD)10.6.2 Naturally inspired optimization techniques10.6.3 Evolutionary methods10.6.4 Strategies of Swarm Intelligence10.7 Case study about predictive analytics in finance10.8 Conclusions
B. Aruna Devi, N. Alangudi Balaji, Mulugeta Tesema 11 A review of different techniques and challenges of quantum computing in various applications
11.1 Introduction11.2 Taxonomy11.3 Challenges11.4 Models in QC11.4.1 Circuit models11.4.2 Quantum perceptron11.4.3 Quantum automata11.4.4 Quantum neural computation11.4.5 Abelian quantum double models11.5 Conclusion and future work
Kaushal Kishor 12 Review and significance of cryptography and machine learning in quantum computing
12.1 Introduction12.2 Quantum cryptography12.2.1 Cryptography’s advantages12.2.2 Cryptography’s various forms12.2.3 Cryptography using elliptical curves12.2.4 Visual cryptography12.2.5 Financial cryptography12.2.6 Cryptography in games12.3 Classical machine learning12.3.1 Data-driven learning and interaction-driven learning12.3.2 Supervised learning12.3.3 Unsupervised learning12.3.4 Reinforcement learning12.3.5 Machine learning models12.3.6 Support vector machines for supervised learning12.4 Application quantum computing in ad hoc networks12.5 Computational learning theories12.5.1 Quantum machine learning12.5.2 Quantum machine learning algorithms implementation12.5.3 Quantum clustering [31]12.5.4 The quantum neural network (QNN) [32]12.5.5 Quantum decision tree [33]12.6 Machine learning is used in learning and renormalization procedures12.6.1 Quantum support vector machine12.6.2 Quantum classifier12.6.3 Quantum computing, quantum learning, and quantum artificial intelligence12.6.4 Classical neural networks12.7 Conclusions
Shally Nagpal, Puneet Garg, Shivani Gaba, Alankrita Aggarwal 13 An improved genetic quantum cryptography model for network communication
13.1 Introduction13.1.1 Cryptography13.1.2 Quantum cryptography13.2 Related study13.3 Genetic and quantum-improved group adaptive communication13.3.1 Significance of work13.3.2 Model to optimize network communication13.3.3 Group generation13.3.4 Group leader selection13.3.5 Genetics-based key pair selection13.3.6 Initial population13.3.7 Fitness function13.3.8 Crossover function13.3.9 Mutation function13.3.10 Application area13.4 Algorithm13.5 Conclusion
Swati Gupta, Puneet Garg 14 Code-based post-quantum cryptographic technique: digital signature
14.1 IntroductionHow does quantum-safe cryptography work?14.2 Types of certificates14.2.1 Traditional certificates14.2.2 Quantum-secure certificates14.2.3 Types of quantum-secure certificates14.2.4 Hybrid certificates14.2.5 Composite certificate14.3 Quantum cryptography vs. post-quantum cryptography14.3.1 Quantum cryptography14.3.2 Quantum key dispersion14.3.3 Post-quantum cryptography14.4 Applications of quantum cryptography14.5 Conclusion
Annapantula Sudhakar, G. R. S. Murthy, Thirukumaran S 15 Post-quantum cryptography for the detection of injection attacks in small-scale networks
15.1 Introduction15.2 Related works15.3 Methods15.3.1 GA-CNN model15.4 Evaluation15.5 Conclusion
Sreelatha P, Nitin Purohit, S. Chandra Sekaran 16 RSA security implementation in quantum computing for a higher resilience
16.1 Introduction16.2 Related works16.3 Methods16.3.1 Quantum key distribution16.4 Evaluation16.5 Conclusion
Kaushal Kishor 17 Application of quantum computing for digital forensic investigation
17.1 Introduction17.2 Literature review17.3 Cryptography using public keys17.3.1 Secret-key cryptography17.4 Quantum approach17.4.1 Quantum bits (qubits)17.4.2 Quantum gates17.4.3 Quantum cryptography17.4.4 Physical and technical principles17.5 Quantum convolutional neural network17.6 Quantum key distributions17.7 Quantum computing in ad hoc networks17.7.1 Comparison17.8 Quantum cryptographic protocols17.8.1 BB84 protocol17.8.2 B92 protocol17.8.3 KCQ protocol17.9 Conclusions and future work
Ayushi Prakash, Sandhya Avasthi, Pushpa Kumari, Mukesh Rawat, Puneet Garg 18 Modern healthcare system: unveiling the possibility of quantum computing in medical and biomedical zones
18.1 Introduction18.1.1 Quantum computing18.1.2 Comparison of classical computing vs. quantum computing18.1.3 Quantum computing for healthcare18.1.4 Supersonic drug design18.1.5 Silicon clinical trials18.1.6 Sequencing and analyzing DNA at full speed18.1.7 Making patients truly the point of care18.1.8 Challenges in efficient healthcare services18.1.9 Trying to take advantage18.1.10 Information and integrated health services18.1.11 Cyber security18.1.12 Increasing medicine costs18.1.13 Payment processing and invoicing18.1.14 Pressure on pharmaceutical prices18.1.15 Disruption in the outside supply chain18.1.16 Motivation18.1.17 Contributions of this chapter18.2 Quantum computing healthcare applications18.3 Security of quantum healthcare computing18.4 Open aspects and research challenges/future research opportunities18.4.1 Safety18.4.2 Data exchange18.4.3 Extensive data18.4.4 Excessive power consumption18.4.5 Inadequate standardization18.5 Conclusion
S. Karthik, H. Summia Parveen, R. Sabitha, B. Anuradha 19 Quantum computing-assisted machine learning to improve the prediction of cardiovascular disease in healthcare system
19.1 Introduction19.2 Related works19.3 Methods19.3.1 Preprocessing19.3.2 Classification in the presence of noise19.3.3 QML classification19.4 Results and discussion19.5 Conclusion
M. S. Kavitha, G. Ramachandra Rao, K. Sangeetha, A. Kowshika 20 Mitigating the risk of quantum computing in cyber security era
20.1 Introduction20.2 Related works20.3 Methods20.3.1 Proposed quantum CNN20.3.2 Quantum-inspired convolutional operation20.3.3 QCNN architecture20.4 Results and discussion20.5 Conclusion
J. Shanthini, R. Sathya, K. Sri Hari, A. Christopher Paul 21 IoMT-based data aggregation using quantum learning
21.1 Introduction21.2 Related works21.3 Methods21.3.1 Data aggregation21.3.2 Architecture21.3.3 Devices integration layer21.3.4 Data integration layer21.3.5 Data visualization layer21.3.6 Implementation21.3.7 Quantum learning21.4 Results and discussion21.5 Conclusion
Index

Content preview from Quantum-Safe Cryptography Algorithms and Approaches

12 Review and significance of cryptography and machine learning in quantum computing

Kaushal Kishor

IT Department, ABES Institute of Technology, Ghaziabad, India

Abstract

Quantum machine learning, which combines quantum computers with regular machine learning, is a much contested field of research. The goal of quantum ML is to provide light on the possible applications of quantum mechanical findings to ML challenges. The exponential growth of data that must be taught to a typical computer paradigm implies that the present cutting-edge computing equipment cannot keep up with the pace. Quantum computing might be useful in this context for continuous training with enormous datasets. Scientists have resorted to quantum machine learning to ...

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Transitioning to Quantum-Safe Cryptography on IBM Z

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ISBN: 9783110798364

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Quantum-Safe Cryptography Algorithms and Approaches

by Satya Prakash Yadav, Raghuraj Singh, Vibhash Yadav, Fadi Al-Turjman, Swarn Avinash Kumar