AI, Machine Learning and Deep Learning
portes grátis
AI, Machine Learning and Deep Learning
A Security Perspective
Hu, Fei; Hei, Xiali
Taylor & Francis Ltd
12/2024
334
Mole
9781032034058
Pré-lançamento - envio 15 a 20 dias após a sua edição
Descrição não disponível.
Part I. Secure AI/ML Systems: Attack Models
1. Machine Learning Attack Models, 2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications, 3. Threat of Adversarial Attacks to Deep Learning: A Survey, 4. Attack Models for Collaborative Deep Learning, 5. Attacks on Deep Reinforcement Learning Systems: A Tutorial, 6. Trust and Security of Deep Reinforcement Learning, 7. IoT Threat Modeling using Bayesian Networks
Part II. Secure AI/ML Systems: Defenses
8. Survey of Machine Learning Defense Strategies, 9. Defenses Against Deep Learning Attacks, 10. Defensive Schemes for Cyber Security of Deep Reinforcement Learning, 11. Adversarial Attacks on Machine Learning Models in Cyber-Physical Systems, 12. Federated Learning and Blockchain: An Opportunity for Artificial Intelligence with Data Regulation
Part III. Using AI/ML Algorithms for Cyber Security
13. Using Machine Learning for Cyber Security: Overview, 14. Performance of Machine Learning and Big Data Analytics Paradigms in Cyber Security, 15. Using ML and DL Algorithms for Intrusion Detection in Industrial Internet of Things.
Part IV. Applications
16. On Detecting Interest Flooding Attacks in Named Data Networking (NDN)-based IoT Searches, 17. Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks, 18. An Artificial Intelligence-assisted Security Analysis of Smart Healthcare Systems, 19. A User-centric Focus for Detecting Phishing Emails
1. Machine Learning Attack Models, 2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications, 3. Threat of Adversarial Attacks to Deep Learning: A Survey, 4. Attack Models for Collaborative Deep Learning, 5. Attacks on Deep Reinforcement Learning Systems: A Tutorial, 6. Trust and Security of Deep Reinforcement Learning, 7. IoT Threat Modeling using Bayesian Networks
Part II. Secure AI/ML Systems: Defenses
8. Survey of Machine Learning Defense Strategies, 9. Defenses Against Deep Learning Attacks, 10. Defensive Schemes for Cyber Security of Deep Reinforcement Learning, 11. Adversarial Attacks on Machine Learning Models in Cyber-Physical Systems, 12. Federated Learning and Blockchain: An Opportunity for Artificial Intelligence with Data Regulation
Part III. Using AI/ML Algorithms for Cyber Security
13. Using Machine Learning for Cyber Security: Overview, 14. Performance of Machine Learning and Big Data Analytics Paradigms in Cyber Security, 15. Using ML and DL Algorithms for Intrusion Detection in Industrial Internet of Things.
Part IV. Applications
16. On Detecting Interest Flooding Attacks in Named Data Networking (NDN)-based IoT Searches, 17. Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks, 18. An Artificial Intelligence-assisted Security Analysis of Smart Healthcare Systems, 19. A User-centric Focus for Detecting Phishing Emails
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
artificial learning (AI);machine learning;deep learning;cyber security;Adversarial Machine Learning;Attacks on Deep Reinforcement Learning Systems;Bayesian networks;Big Data Analytics paradigms in Cybersecurity;Defenses to Deep Learning Attacks;Adversarial Attack;Adversarial Examples;Ml Model;DRL;DNN;Ml Algorithm;Dl Model;IoT Device;Dl Algorithm;Ml;Cps;Data Sets;Hyperparameter Optimization;Poisoning Attacks;Phishing Attack;Random Forest Classifier;RF;Classical Machine Learning Algorithms;DNN Model;Intrusion Detection Systems;Phishing Detector;SVM;CVSS Score;Supervised Machine Learning
Part I. Secure AI/ML Systems: Attack Models
1. Machine Learning Attack Models, 2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications, 3. Threat of Adversarial Attacks to Deep Learning: A Survey, 4. Attack Models for Collaborative Deep Learning, 5. Attacks on Deep Reinforcement Learning Systems: A Tutorial, 6. Trust and Security of Deep Reinforcement Learning, 7. IoT Threat Modeling using Bayesian Networks
Part II. Secure AI/ML Systems: Defenses
8. Survey of Machine Learning Defense Strategies, 9. Defenses Against Deep Learning Attacks, 10. Defensive Schemes for Cyber Security of Deep Reinforcement Learning, 11. Adversarial Attacks on Machine Learning Models in Cyber-Physical Systems, 12. Federated Learning and Blockchain: An Opportunity for Artificial Intelligence with Data Regulation
Part III. Using AI/ML Algorithms for Cyber Security
13. Using Machine Learning for Cyber Security: Overview, 14. Performance of Machine Learning and Big Data Analytics Paradigms in Cyber Security, 15. Using ML and DL Algorithms for Intrusion Detection in Industrial Internet of Things.
Part IV. Applications
16. On Detecting Interest Flooding Attacks in Named Data Networking (NDN)-based IoT Searches, 17. Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks, 18. An Artificial Intelligence-assisted Security Analysis of Smart Healthcare Systems, 19. A User-centric Focus for Detecting Phishing Emails
1. Machine Learning Attack Models, 2. Adversarial Machine Learning: A New Threat Paradigm for Next-generation Wireless Communications, 3. Threat of Adversarial Attacks to Deep Learning: A Survey, 4. Attack Models for Collaborative Deep Learning, 5. Attacks on Deep Reinforcement Learning Systems: A Tutorial, 6. Trust and Security of Deep Reinforcement Learning, 7. IoT Threat Modeling using Bayesian Networks
Part II. Secure AI/ML Systems: Defenses
8. Survey of Machine Learning Defense Strategies, 9. Defenses Against Deep Learning Attacks, 10. Defensive Schemes for Cyber Security of Deep Reinforcement Learning, 11. Adversarial Attacks on Machine Learning Models in Cyber-Physical Systems, 12. Federated Learning and Blockchain: An Opportunity for Artificial Intelligence with Data Regulation
Part III. Using AI/ML Algorithms for Cyber Security
13. Using Machine Learning for Cyber Security: Overview, 14. Performance of Machine Learning and Big Data Analytics Paradigms in Cyber Security, 15. Using ML and DL Algorithms for Intrusion Detection in Industrial Internet of Things.
Part IV. Applications
16. On Detecting Interest Flooding Attacks in Named Data Networking (NDN)-based IoT Searches, 17. Attack on Fraud Detection Systems in Online Banking Using Generative Adversarial Networks, 18. An Artificial Intelligence-assisted Security Analysis of Smart Healthcare Systems, 19. A User-centric Focus for Detecting Phishing Emails
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
artificial learning (AI);machine learning;deep learning;cyber security;Adversarial Machine Learning;Attacks on Deep Reinforcement Learning Systems;Bayesian networks;Big Data Analytics paradigms in Cybersecurity;Defenses to Deep Learning Attacks;Adversarial Attack;Adversarial Examples;Ml Model;DRL;DNN;Ml Algorithm;Dl Model;IoT Device;Dl Algorithm;Ml;Cps;Data Sets;Hyperparameter Optimization;Poisoning Attacks;Phishing Attack;Random Forest Classifier;RF;Classical Machine Learning Algorithms;DNN Model;Intrusion Detection Systems;Phishing Detector;SVM;CVSS Score;Supervised Machine Learning
