Ergebnisse für: Machine Learning Attacks
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One of the most influential actors in spreading Islamist violence across the Sahel is Jama’at Nasr Al Islam Wal Muslimin (JNIM).This book provides the first systematic quantitative analysis of JNIM’s behavior by analyzing a 12-year database of JNIM’s attacks and the environment surrounding JNI...
Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial inte...
This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This book delves into the resilience and security challenges inherent to FL, such as model poisoning and malicious attacks, that jeopardize system integrit...
This book offers a comprehensive exploration of the emerging threats and defense strategies in adversarial machine learning and AI security. It covers a broad range of topics, from federated learning attacks, adversarial defenses, biometric vulnerabilities, and security weaknesses in generative...
This book studies in detail the robustness of machine learning (ML) algorithms involved in dealing with vulnerabilities where the errors or malfunctions are both intentional and malicious, therefore being associated with a specific attack model. Reliability is key to the wider adoption of machine le...
One of the most influential actors in spreading Islamist violence across the Sahel is Jama’at Nasr Al Islam Wal Muslimin (JNIM).This book provides the first systematic quantitative analysis of JNIM’s behavior by analyzing a 12-year database of JNIM’s attacks and the environment surrounding JNI...
This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.By implementing innovative deep learning solutions, cybersecurity researchers, students and ...
This SpringerBrief presents the underlying principles of machine learning and how to deploy various deep learning tools and techniques to tackle and solve certain challenges facing the cybersecurity industry.By implementing innovative deep learning solutions, cybersecurity researchers, students and ...
Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial inte...
Machine learning boosts the capabilities of security solutions in the modern cyber environment. However, there are also security concerns associated with machine learning models and approaches: the vulnerability of machine learning models to adversarial attacks is a fatal flaw in the artificial inte...
This volume constitutes the proceedings of 9th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2025, in Be'er Sheva, Israel, during December 4–5, 2025.
The 17 regular papers and 9 short papers presented here were carefully reviewed and selected from 43 submission...
This book constitutes selected papers from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online. The 8 full papers were thoroughly reviewed and selected from 13 qua...
This book constitutes the proceedings of the 8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024, held in Be'er Sheva, Israel, during December 19–20, 2024.
The 16 full papers and 11 short papers presented here were carefully reviewed and sele...
This book constitutes the proceedings of the 8th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2024, held in Be'er Sheva, Israel, during December 19–20, 2024.
The 16 full papers and 11 short papers presented here were carefully reviewed and sele...
This volume constitutes the proceedings of 9th International Symposium on Cyber Security, Cryptology, and Machine Learning, CSCML 2025, in Be'er Sheva, Israel, during December 4–5, 2025.
The 17 regular papers and 9 short papers presented here were carefully reviewed and selected from 43 submission...
This book constitutes selected papers from the First International Workshop on Deployable Machine Learning for Security Defense, MLHat 2020, held in August 2020. Due to the COVID-19 pandemic the conference was held online. The 8 full papers were thoroughly reviewed and selected from 13 qua...
This book offers a comprehensive exploration of federated learning (FL), a novel approach to decentralized, privacy-preserving machine learning. This book delves into the resilience and security challenges inherent to FL, such as model poisoning and malicious attacks, that jeopardize system integrit...
This book offers a comprehensive exploration of the emerging threats and defense strategies in adversarial machine learning and AI security. It covers a broad range of topics, from federated learning attacks, adversarial defenses, biometric vulnerabilities, and security weaknesses in generative...
Die Dissertation behandelt die Erkennung von Angriffen auf industrielle Anlagen und Organisationen. Dabei wird zunächst ein Fokus auf die Auswahl geeigneter Datenquellen gelegt, die genutzt werden können, um aussagekräftige Daten zu erlangen. Aus diesen Daten werden Eigenschaften abgeleitet, die ...
This book studies in detail the robustness of machine learning (ML) algorithms involved in dealing with vulnerabilities where the errors or malfunctions are both intentional and malicious, therefore being associated with a specific attack model. Reliability is key to the wider adoption of machine le...