6 IoT attack detection using quantum deep learning in large-scale networks
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Deepali Virmani
Abstract
The probability of a cyberattack rises at an exponential pace in direct proportion to the size of the network-connected device population. Cybercriminals will concentrate their efforts on wireless networks because it is anticipated that more than half of all data on the Internet would originate from wireless networks. In this chapter, we develop an IoT attack detection using an intrusion detection system (IDS) framework that develops a quantum convolutional neural network-based Long Short-Term Memory (QCNN-LSTM) in large-scale networks. The collection of network logs is initially preprocessed and then it is classified using classifiers. The model is simulated to test its robustness against different scale of attacks, and the results show higher level of accuracy in detecting the attacks using QCNN-LSTM than other methods.
Abstract
The probability of a cyberattack rises at an exponential pace in direct proportion to the size of the network-connected device population. Cybercriminals will concentrate their efforts on wireless networks because it is anticipated that more than half of all data on the Internet would originate from wireless networks. In this chapter, we develop an IoT attack detection using an intrusion detection system (IDS) framework that develops a quantum convolutional neural network-based Long Short-Term Memory (QCNN-LSTM) in large-scale networks. The collection of network logs is initially preprocessed and then it is classified using classifiers. The model is simulated to test its robustness against different scale of attacks, and the results show higher level of accuracy in detecting the attacks using QCNN-LSTM than other methods.
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
