| Titre : |
Innovative machine learning applications for cryptography |
| Type de document : |
document électronique |
| Auteurs : |
J.- Anitha Ruth, Auteur ; G.V.-Mahesh Vijayalakshmi, Auteur ; P. Visalakshi, Auteur ; R. Uma, Auteur |
| Editeur : |
Hershey [United States] : Engineering science reference |
| Année de publication : |
2024 |
| Importance : |
1 fichier PDF |
| Présentation : |
ill. |
| ISBN/ISSN/EAN : |
979-83693-16429-- |
| Note générale : |
Mode d'accès : accès au texte intégral par :
- authentification après inscription à la plateforme EBSCOhost
ou
- adresse IP de l'École.
Index |
| Langues : |
Anglais (eng) |
| Mots-clés : |
Cryptography--Data processing
Data encryption (Computer science)
Machine learning
Cryptography--Technological innovations |
| Index. décimale : |
004 Informatique. Science et technologie de l'informatique |
| Résumé : |
Data security is paramount in our modern world, and the symbiotic relationship between machine learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how machine learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, machine learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches. Innovative Machine Learning Applications for Cryptography bridges the gap between two powerful domains and assists in diminishing the influence of human error on encryption and decryption processes. For academic scholars, engineers, scientists, and students, this book offers a valuable treasure trove of knowledge and actionable strategies in an age where the security of every byte is of utmost importance. |
| Note de contenu : |
Summary of the book :
1. Introduction to modern cryptography and machine learning
2. Future outlook
3. Artificial intelligence-supported bio-cryptography protection
4. An adaptive cryptography using openai api
5. Optimized deep learning-based intrusion detection using woa with lightgbm
... |
| En ligne : |
https://research.ebsco.com/linkprocessor/plink?id=87b26b50-7e11-37a2-8b94-d82413 [...] |
Innovative machine learning applications for cryptography [document électronique] / J.- Anitha Ruth, Auteur ; G.V.-Mahesh Vijayalakshmi, Auteur ; P. Visalakshi, Auteur ; R. Uma, Auteur . - Hershey [United States] : Engineering science reference, 2024 . - 1 fichier PDF : ill. ISBN : 979-83693-16429-- Mode d'accès : accès au texte intégral par :
- authentification après inscription à la plateforme EBSCOhost
ou
- adresse IP de l'École.
Index Langues : Anglais ( eng)
| Mots-clés : |
Cryptography--Data processing
Data encryption (Computer science)
Machine learning
Cryptography--Technological innovations |
| Index. décimale : |
004 Informatique. Science et technologie de l'informatique |
| Résumé : |
Data security is paramount in our modern world, and the symbiotic relationship between machine learning and cryptography has recently taken center stage. The vulnerability of traditional cryptosystems to human error and evolving cyber threats is a pressing concern. The stakes are higher than ever, and the need for innovative solutions to safeguard sensitive information is undeniable. Innovative Machine Learning Applications for Cryptography emerges as a steadfast resource in this landscape of uncertainty. Machine learning's prowess in scrutinizing data trends, identifying vulnerabilities, and constructing adaptive analytical models offers a compelling solution. The book explores how machine learning can automate the process of constructing analytical models, providing a continuous learning mechanism to protect against an ever-increasing influx of data. This book goes beyond theoretical exploration, and provides a comprehensive resource designed to empower academic scholars, specialists, and students in the fields of cryptography, machine learning, and network security. Its broad scope encompasses encryption, algorithms, security, and more unconventional topics like Quantum Cryptography, Biological Cryptography, and Neural Cryptography. By examining data patterns and identifying vulnerabilities, it equips its readers with actionable insights and strategies that can protect organizations from the dire consequences of security breaches. Innovative Machine Learning Applications for Cryptography bridges the gap between two powerful domains and assists in diminishing the influence of human error on encryption and decryption processes. For academic scholars, engineers, scientists, and students, this book offers a valuable treasure trove of knowledge and actionable strategies in an age where the security of every byte is of utmost importance. |
| Note de contenu : |
Summary of the book :
1. Introduction to modern cryptography and machine learning
2. Future outlook
3. Artificial intelligence-supported bio-cryptography protection
4. An adaptive cryptography using openai api
5. Optimized deep learning-based intrusion detection using woa with lightgbm
... |
| En ligne : |
https://research.ebsco.com/linkprocessor/plink?id=87b26b50-7e11-37a2-8b94-d82413 [...] |
|  |
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