4 AI-driven cybersecurity modeling using quantum computing for mitigation of attacks in IOT-SDN network
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M. Sunil Kumar
Abstract
The implementation of quantum learning strategies is turning out to be increasingly crucial. When it comes to cybersecurity, one of the key benefits of using machine learning is that it makes the detection of malware more effective, scalable, and actionable than traditional human approaches. This is one of the primary advantages of employing machine learning. The cybersecurity risks posed by quantum learning must be effectively managed on a logical and theoretical level in order to be mitigated. It is necessary to prevail over these obstacles. Deep learning, support vector quantums, and Bayesian classification are just a few examples of the quantum learning and statistical technologies that have showed promise in the area of reducing the consequences of cyberattacks. When designing intelligent security systems, it is essential to unearth previously unknown patterns and insights hidden within network data, as well as to develop a data-driven quantum learning model to counteract the threats posed by these attacks. Additionally, it is essential to uncover previously unknown patterns and insights hidden within network data. The chapter develops AIbased modeling to improve the detection of cybersecurity attacks in Internet of Things--Software-Defined Network (IoT-SDN). The collection of network logs, preprocessing, and classification of instances enables the model to classify the attacks from the network. The simulation is conducted in Python to test the effectiveness of the AI-driven model. The results show that the proposed method achieves higher rate of accuracy in detecting the instances than other methods.
Abstract
The implementation of quantum learning strategies is turning out to be increasingly crucial. When it comes to cybersecurity, one of the key benefits of using machine learning is that it makes the detection of malware more effective, scalable, and actionable than traditional human approaches. This is one of the primary advantages of employing machine learning. The cybersecurity risks posed by quantum learning must be effectively managed on a logical and theoretical level in order to be mitigated. It is necessary to prevail over these obstacles. Deep learning, support vector quantums, and Bayesian classification are just a few examples of the quantum learning and statistical technologies that have showed promise in the area of reducing the consequences of cyberattacks. When designing intelligent security systems, it is essential to unearth previously unknown patterns and insights hidden within network data, as well as to develop a data-driven quantum learning model to counteract the threats posed by these attacks. Additionally, it is essential to uncover previously unknown patterns and insights hidden within network data. The chapter develops AIbased modeling to improve the detection of cybersecurity attacks in Internet of Things--Software-Defined Network (IoT-SDN). The collection of network logs, preprocessing, and classification of instances enables the model to classify the attacks from the network. The simulation is conducted in Python to test the effectiveness of the AI-driven model. The results show that the proposed method achieves higher rate of accuracy in detecting the instances 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
