Machine Learning for Cybersecurity
"Machine Learning for Cybersecurity: Threat Detection and Mitigation" delves into the transformative role of machine learning in addressing contemporary cybersecurity challenges. This reprint provides an in-depth exploration of how advanced techniques such as deep learning, natural language processi...
Gorde:
| Formatua: | Online |
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| Hizkuntza: | ingelesa |
| Argitaratua: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Gaiak: | |
| Sarrera elektronikoa: | ONIX_20250220_9783725827947_435 |
| Etiketak: |
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
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| _version_ | 1869527774209769472 |
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| collection | Directory of Open Access Books |
| description | "Machine Learning for Cybersecurity: Threat Detection and Mitigation" delves into the transformative role of machine learning in addressing contemporary cybersecurity challenges. This reprint provides an in-depth exploration of how advanced techniques such as deep learning, natural language processing, and explainable AI are revolutionizing intrusion detection, anomaly detection, and threat intelligence. With a focus on practical applications, it covers critical topics such as malware analysis, IoT and cloud security, blockchain security, adversarial attacks, and secure data sharing. Through this reprint, readers will gain insights into cutting-edge approaches for vulnerability assessments, authentication, and privacy preservation while exploring frameworks for implementing security-aware AI systems. This comprehensive resource is essential for researchers, practitioners, and policymakers striving to strengthen digital ecosystems. It offers both theoretical insights and actionable solutions, paving the way for innovative cybersecurity strategies to combat an ever-evolving threat landscape. |
| format | Online |
| id | doab-20.500.12854ir-153071 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1530712025-02-20T13:30:55Z Machine Learning for Cybersecurity Elhanashi, Abdussalam Dini, Pierpaolo generative adversarial networks data anonymization privacy preservation loss feedback feature coding power data protection Internet of Things denial-of-service attack Information-Centric Network machine learning network intrusion detection datasets engineering deep learning feature fusion cybersecurity oversampling technique undersampling technique multi-class classification vulnerability mining hotspot code fuzzing AFL device fingerprinting neuromorphic spiking neural network eventization encoding SNN operational technology OT random forest WirelessHART artificial intelligence cyber threat intelligence cyber resilience ethical considerations CTI and AI biases anomaly detection DCNN Internet of Things (IoT) machine learning (ML) SVM XGBoost security blockchain ELF static analysis binary lifting opcode sequence analysis malware detection malware classification fake account detection Twitter (X) image classification syntax aware protocol implementations large language models Linux system calls quantum computing IoMT post-quantum lateral movement threat mitigation unsupervised learning attack graphs active directory hardening placement robotics security industrial control systems network-based intrusion detection systems thema EDItEUR::A The Arts::AT Performing arts::ATF Films, cinema thema EDItEUR::A The Arts::AT Performing arts::ATJ Television "Machine Learning for Cybersecurity: Threat Detection and Mitigation" delves into the transformative role of machine learning in addressing contemporary cybersecurity challenges. This reprint provides an in-depth exploration of how advanced techniques such as deep learning, natural language processing, and explainable AI are revolutionizing intrusion detection, anomaly detection, and threat intelligence. With a focus on practical applications, it covers critical topics such as malware analysis, IoT and cloud security, blockchain security, adversarial attacks, and secure data sharing. Through this reprint, readers will gain insights into cutting-edge approaches for vulnerability assessments, authentication, and privacy preservation while exploring frameworks for implementing security-aware AI systems. This comprehensive resource is essential for researchers, practitioners, and policymakers striving to strengthen digital ecosystems. It offers both theoretical insights and actionable solutions, paving the way for innovative cybersecurity strategies to combat an ever-evolving threat landscape. 2025-02-20T13:30:52Z 2025-02-20T13:30:52Z 2024 book ONIX_20250220_9783725827947_435 9783725827947 9783725827930 https://directory.doabooks.org/handle/20.500.12854/153071 eng application/octet-stream Attribution 4.0 International https://mdpi.com/books/pdfview/book/10270 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-2793-0 10.3390/books978-3-7258-2793-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725827947 9783725827930 318 Basel open access |
| spellingShingle | generative adversarial networks data anonymization privacy preservation loss feedback feature coding power data protection Internet of Things denial-of-service attack Information-Centric Network machine learning network intrusion detection datasets engineering deep learning feature fusion cybersecurity oversampling technique undersampling technique multi-class classification vulnerability mining hotspot code fuzzing AFL device fingerprinting neuromorphic spiking neural network eventization encoding SNN operational technology OT random forest WirelessHART artificial intelligence cyber threat intelligence cyber resilience ethical considerations CTI and AI biases anomaly detection DCNN Internet of Things (IoT) machine learning (ML) SVM XGBoost security blockchain ELF static analysis binary lifting opcode sequence analysis malware detection malware classification fake account detection Twitter (X) image classification syntax aware protocol implementations large language models Linux system calls quantum computing IoMT post-quantum lateral movement threat mitigation unsupervised learning attack graphs active directory hardening placement robotics security industrial control systems network-based intrusion detection systems thema EDItEUR::A The Arts::AT Performing arts::ATF Films, cinema thema EDItEUR::A The Arts::AT Performing arts::ATJ Television Machine Learning for Cybersecurity |
| title | Machine Learning for Cybersecurity |
| title_full | Machine Learning for Cybersecurity |
| title_fullStr | Machine Learning for Cybersecurity |
| title_full_unstemmed | Machine Learning for Cybersecurity |
| title_short | Machine Learning for Cybersecurity |
| title_sort | machine learning for cybersecurity |
| topic | generative adversarial networks data anonymization privacy preservation loss feedback feature coding power data protection Internet of Things denial-of-service attack Information-Centric Network machine learning network intrusion detection datasets engineering deep learning feature fusion cybersecurity oversampling technique undersampling technique multi-class classification vulnerability mining hotspot code fuzzing AFL device fingerprinting neuromorphic spiking neural network eventization encoding SNN operational technology OT random forest WirelessHART artificial intelligence cyber threat intelligence cyber resilience ethical considerations CTI and AI biases anomaly detection DCNN Internet of Things (IoT) machine learning (ML) SVM XGBoost security blockchain ELF static analysis binary lifting opcode sequence analysis malware detection malware classification fake account detection Twitter (X) image classification syntax aware protocol implementations large language models Linux system calls quantum computing IoMT post-quantum lateral movement threat mitigation unsupervised learning attack graphs active directory hardening placement robotics security industrial control systems network-based intrusion detection systems thema EDItEUR::A The Arts::AT Performing arts::ATF Films, cinema thema EDItEUR::A The Arts::AT Performing arts::ATJ Television |
| topic_facet | generative adversarial networks data anonymization privacy preservation loss feedback feature coding power data protection Internet of Things denial-of-service attack Information-Centric Network machine learning network intrusion detection datasets engineering deep learning feature fusion cybersecurity oversampling technique undersampling technique multi-class classification vulnerability mining hotspot code fuzzing AFL device fingerprinting neuromorphic spiking neural network eventization encoding SNN operational technology OT random forest WirelessHART artificial intelligence cyber threat intelligence cyber resilience ethical considerations CTI and AI biases anomaly detection DCNN Internet of Things (IoT) machine learning (ML) SVM XGBoost security blockchain ELF static analysis binary lifting opcode sequence analysis malware detection malware classification fake account detection Twitter (X) image classification syntax aware protocol implementations large language models Linux system calls quantum computing IoMT post-quantum lateral movement threat mitigation unsupervised learning attack graphs active directory hardening placement robotics security industrial control systems network-based intrusion detection systems thema EDItEUR::A The Arts::AT Performing arts::ATF Films, cinema thema EDItEUR::A The Arts::AT Performing arts::ATJ Television |
| url | ONIX_20250220_9783725827947_435 |