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Biblioteca

MARC Lista (tabellare)
The 2018 Yearbook of the Digital Ethics Lab / / edited by Carl Öhman, David Watson
The 2018 Yearbook of the Digital Ethics Lab / / edited by Carl Öhman, David Watson
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (223 pages) : illustrations
Disciplina 174.96
175
Collana Digital Ethics Lab Yearbook
Soggetto topico Philosophy
Technology—Sociological aspects
Computer security
Ethics
Computer crimes
Philosophy of Technology
Science and Technology Studies
Privacy
Cybercrime
ISBN 3-030-17152-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction: Digital Ethics: Goals and Approach (Carl Öhman, David Watson, Mariarosaria Taddeo, Luciano Floridi) -- Chapter 1. Digital Ethics: Its Nature and Scope (Luciano Floridi, Corinne Cath, Mariarosaria Taddeo) -- Chapter 2. Do We Need a Critical Evaluation of the Role of Mathematics in Data Science? (Patrick Allo) -- Chapter 3. Using Data From Git and GitHub in Ethnographies of Software Development (Andrew Turner) -- Chapter 4. The Price of Discovery: A Model of Scientific Research Markets (David Watson) -- Chapter 5. Projecting AI-Crime: A Review of Plausible Threats (Thomas King) -- Chapter 6. The Challenges of Cyber Deterrence (Mariarosaria Taddeo) -- Chapter 7. Internet Governance and Human Rights: A Literature Review (Corinne Cath) -- Chapter 8. Privacy Risks and Responses in the Digital Age (Josh Cowls) -- Chapter 9. Digitalised Legal Information: Towards a New Publication Model (Václav Janeček) -- Chapter 10. From Bones to Bytes: A New Chapter in the History of Death (Carl Öhman) -- Chapter 11. The Green and the Blue — Naïve Ideas to Improve Politics in a Mature Information Society (Luciano FLoridi).
Record Nr. UNINA-9910349541003321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advance Metadata Fair : The Retention and Disclosure of 4G, 5G and Social Media Location Information, for Law Enforcement and National Security, and the Impact on Privacy in Australia / / by Stanley Shanapinda
Advance Metadata Fair : The Retention and Disclosure of 4G, 5G and Social Media Location Information, for Law Enforcement and National Security, and the Impact on Privacy in Australia / / by Stanley Shanapinda
Autore Shanapinda Stanley
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (231 pages)
Disciplina 910.285
Collana Law, Governance and Technology Series
Soggetto topico Information technology—Law and legislation
Mass media—Law and legislation
Computers—Law and legislation
Data protection—Law and legislation
IT Law, Media Law, Intellectual Property
Legal Aspects of Computing
Privacy
ISBN 3-030-50255-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto The Dual Nature of Privacy – As a Target and as a Treasure to Protect: An Introduction -- The Legal Framework to Retain Location Information -- The Legal Framework to Collect and Disclose Location Information -- The Legal Framework to Retain and Disclose the Contents of a Communication -- The Powers of the Agencies to Collect and Use Location Information -- Location Information as Personal Information -- Limits to the Powers of the AFP and ASIO to Collect and Use Location Information -- External Oversight Exercised Over the Powers of the Agencies -- The IP-Mediated LTE Network versus the Law -- Proposing the Judicial Location Information Warrant.
Record Nr. UNINA-9910416138603321
Shanapinda Stanley  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Advanced Applications of Blockchain Technology / / edited by Shiho Kim, Ganesh Chandra Deka
Advanced Applications of Blockchain Technology / / edited by Shiho Kim, Ganesh Chandra Deka
Edizione [1st ed. 2020.]
Pubbl/distr/stampa Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
Descrizione fisica 1 online resource (X, 278 p. 93 illus., 58 illus. in color.)
Disciplina 006.3
Collana Studies in Big Data
Soggetto topico Computational intelligence
Computer security
Big data
Computational Intelligence
Systems and Data Security
Big Data
Privacy
ISBN 981-13-8775-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Introduction to Blockchain Technology and IoT -- IoT, AI, and Blockchain: Implementation perspectives -- Blockchain Technologies for IoT -- Blockchain Technology Use Cases -- Blockchain meets CyberSecurity: Security, Privacy, Challenges and Opportunity -- On the Role of Blockchain Technology in Internet of Things -- Blockchain of Things (BCoT): The Fusion of Blockchain and IoT Technologies -- Blockchain Architecture -- Authenticating IoT Devices with Blockchain -- Security & Privacy Issues of Block chain Technology -- Supply Chain Management in Agriculture Using Blockchain and IoT -- Blockchain Technologies and Artificial Intelligence -- Blockchain Hands on for Developing Genesis Block.
Record Nr. UNINA-9910484022203321
Singapore : , : Springer Singapore : , : Imprint : Springer, , 2020
Materiale a stampa
Lo trovi qui: Univ. Federico II
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AI for Social Sciences : With an Introduction to Security, Privacy, Ethics and Society Impacts / / by Vicenç Torra
AI for Social Sciences : With an Introduction to Security, Privacy, Ethics and Society Impacts / / by Vicenç Torra
Autore Torra Vicenç
Edizione [1st ed. 2026.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026
Descrizione fisica 1 online resource (XV, 176 p. 63 illus., 15 illus. in color.)
Disciplina 006.3
Soggetto topico Artificial intelligence
Expert systems (Computer science)
Natural language processing (Computer science)
Social sciences - Data processing
Data protection - Law and legislation
Multiagent systems
Artificial Intelligence
Knowledge Based Systems
Natural Language Processing (NLP)
Computer Application in Social and Behavioral Sciences
Privacy
Multiagent Systems
ISBN 3-032-07216-6
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto 1. Introduction -- 2. Artificial intelligence and intelligent systems -- 3. Decision-support systems -- 4. Machine learning at work. Data-science -- 5. Security and privacy -- 6. Applications in social sciences -- 7. Ethics and AI.
Record Nr. UNINA-9911058016903321
Torra Vicenç  
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2026
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Aktuelle Entwicklungen des Rechtsrahmens der Cybersicherheit und Privatheit : Sammelband zur ATHENE-Konferenz 2025 in Darmstadt / / herausgegeben von Annika Selzer
Aktuelle Entwicklungen des Rechtsrahmens der Cybersicherheit und Privatheit : Sammelband zur ATHENE-Konferenz 2025 in Darmstadt / / herausgegeben von Annika Selzer
Edizione [1st ed. 2026.]
Pubbl/distr/stampa Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Vieweg, , 2026
Descrizione fisica 1 online resource (XI, 155 S. 6 Abb., 1 Abb. in Farbe.)
Disciplina 005.8
Collana Rechtsrahmen der Cybersicherheit und Privatheit
Soggetto topico Data protection
Data protection - Law and legislation
Data and Information Security
Privacy
ISBN 3-658-49640-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione ger
Nota di contenuto Vorwort -- Datenschutz in Metaversen -- Von der Theorie zur Umsetzung: Die Datenschutzvorsorge im Planspiel und der Weg zur standardisierten Dokumentation -- Same same but different? Der risikobasierte Ansatz nach DSGVO und CRA im Vergleich -- KI in der Personalauswahl: Rechtliche Anforderungen vereinen -- KI-generierte, Verarbeitungskontext-spezifische – Mitarbeiterschulungen zum Datenschutz als Ausprägung der angemessenen Umsetzung datenschutzrechtlicher Pflichten -- Cybersicherheitsforschung im Spannungsfeld des Strafrechts – Überblick über bisherige Rechtsprechung zu einschlägigen strafrechtlichen Normen -- Globale Tendenzen in nationalen Cybersicherheitsstrategien: Ein Leitfaden für die Strategieentwicklung -- Einordnung wesentlicher und wichtiger Einrichtungen nach der NIS-2-Richtlinie - eine Betrachtung mit Schwerpunkt auf dem Energiesektor -- Ansätze zur Unterstützung eines hohen Cybersicherheitsniveaus im Energiesektor: Teilautomatisierte Verifizierung von Vorgaben des (geplanten) NIS-2-Umsetzungs- und Cybersicherheitsstärkungsgesetzes.
Record Nr. UNINA-9911046008203321
Wiesbaden : , : Springer Fachmedien Wiesbaden : , : Imprint : Springer Vieweg, , 2026
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Algorithms for Data and Computation Privacy / / by Alex X. Liu, Rui Li
Algorithms for Data and Computation Privacy / / by Alex X. Liu, Rui Li
Autore Liu Alex X.
Edizione [1st ed. 2021.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Descrizione fisica 1 online resource (XVIII, 404 p. 163 illus., 130 illus. in color.)
Disciplina 005.8
Soggetto topico Data protection - Law and legislation
Computer networks
Information storage and retrieval systems
Computer networks - Security measures
Data protection
Privacy
Computer Communication Networks
Information Storage and Retrieval
Mobile and Network Security
Data and Information Security
ISBN 3-030-58896-3
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I Privacy Preserving Queries -- Range Queries over Encrypted Data -- Fast and Scalable Range and Keyword Query Processing over Encrypted Data with Provable Adaptive Security -- Nearest Neighbor Queries over Encrypted Data -- K-nearest Neighbor Queries over Encrypted Data -- Top-k Queries for Two-tiered Sensor Networks -- Part II Privacy Preserving Computation -- Collaborative Enforcement of Firewall Policies in Virtual Private Networks -- Privacy Preserving Quantification of Cross-Domain Network Reachability -- Cross-Domain Privacy-Preserving Cooperative Firewall Optimization -- Privacy Preserving String Matching for Cloud Computing -- Privacy Preserving Information Hub Identification in Social Networks -- Part III Differential Privacy -- Publishing Social Network Data with Privacy Guarantees -- Predictable Privacy-Preserving Mobile Crowd Sensing -- Differentially Private and Budget Limited Bandit Learning over Matroids -- Part IV Breaking Privacy -- Breaching Privacy in Encrypted Instant Messaging Networks.
Record Nr. UNINA-9910483392303321
Liu Alex X.  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2021
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Android Malware Detection and Adversarial Methods / / by Weina Niu, Xiaosong Zhang, Ran Yan, Jiacheng Gong
Android Malware Detection and Adversarial Methods / / by Weina Niu, Xiaosong Zhang, Ran Yan, Jiacheng Gong
Autore Niu Weina
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (xiv, 190 pages) : illustrations
Disciplina 005.8
Altri autori (Persone) ZhangXiaosong <1968->
YanRan
GongJiacheng
Soggetto topico Computer networks - Security measures
Data protection
Data protection - Law and legislation
Machine learning
Blockchains (Databases)
Mobile and Network Security
Data and Information Security
Security Services
Privacy
Machine Learning
Blockchain
Cadena de blocs (Bases de dades)
Protecció de dades
Aprenentatge automàtic
Soggetto genere / forma Llibres electrònics
ISBN 981-9714-59-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Foreword -- Preface -- Acknowledgments -- Contents -- Part I The Overview of Android Malware Detection -- 1 Introduction of Android Malware Detection -- 1.1 Android Malware Family -- 1.1.1 Trojan Horse -- 1.1.2 Viruses -- 1.1.3 The Back Door -- 1.1.4 Zombies -- 1.1.5 Espionage -- 1.1.6 Intimidation -- 1.1.7 Extortion -- 1.1.8 Advertising -- 1.1.9 Tracking -- 1.2 History of Android Malware Detection -- 1.3 Android Malware Detection Overview -- 1.4 Challenges and Apps of Android Malware Detection -- 1.5 Domestic and International Android Malware Detection -- 1.5.1 Android Malware Detection Method Based on Static Analysis -- 1.5.2 Android Malware Detection Method Based on Dynamic Analysis -- 1.5.3 Android Malware Detection Method Based on Hybrid Analysis -- 1.6 Chapter Summary -- References -- Part II The General Android Malware Detection Method -- 2 Feature Code Based Android Malware Detection Method -- 2.1 Detection Based on Traditional Feature Codes -- 2.1.1 Introduction -- 2.1.2 DroidAnalyzer: A Case Study in Android Malware Analysis -- 2.1.2.1 Suspicious Android APIs and Keywords 3 -- 2.1.2.2 Main Algorithm of DroidAnalyzer -- 2.2 Detection Based on Semantic Feature Codes -- 2.2.1 Introduction -- 2.2.2 DroidNative: A Case Study in Android Malware Analysis -- 2.2.2.1 Static Analysis in DroidNative -- 2.2.2.2 System Design and Implementation -- 2.3 Chapter Summary -- References -- 3 Behavior-Based Detection Method for Android Malware -- 3.1 Privacy Disclosure -- 3.2 Permission Escalation -- 3.2.1 Permission Escalation Method -- 3.2.2 Authorization Based on Configuration Files -- 3.2.3 Code Analysis -- 3.2.4 Taint Analysis -- 3.3 Machine Learning Technology and Malicious Behavior of Android Software -- 3.4 Chapter Summary -- References -- 4 AI-Based Android Malware Detection Methods.
4.1 Detection Based on Permissions, APIs, and Components -- 4.1.1 Permissions in Android System -- 4.1.1.1 Permissions in Android System -- 4.1.1.2 Overview of Permission-Based Detection Methods -- 4.1.2 Detection Based on API -- 4.1.3 Component-Based Detection -- 4.1.3.1 Components of an Application -- 4.1.3.2 Overview of Component-Based Detection Methods -- 4.1.4 Specific Case: Drebin -- 4.1.4.1 Static Analysis of Applications -- 4.1.4.2 Embedding in Vector Space -- 4.1.4.3 Learning-Based Detection -- 4.1.4.4 Explanation -- 4.2 Detection Anchored in Dynamic Runtime Features -- 4.2.1 Dynamic Analysis and Runtime Features -- 4.2.2 Overview of Detection Methods Based on Dynamic Runtime Features -- 4.2.3 Specific Case: EnDroid -- 4.2.3.1 Training Phase -- 4.2.3.2 Detection Phase -- 4.3 Detection Through Semantic Code Analysis -- 4.3.1 Dalvik Bytecode -- 4.3.2 Overview of Code Semantic-Based Detection Methods -- 4.3.3 Specific Case: MviiDroid -- 4.3.3.1 Static Analysis Phase -- 4.3.3.2 Feature Generation Phase -- 4.3.3.3 Model Training Phase -- 4.4 Detection via Image Analysis -- 4.4.1 Overview of Image-Based Detection Methods -- 4.4.2 Specific Case: R2-D2 -- 4.5 Detection Through Graph Analysis -- 4.5.1 Overview of Homogeneous Graph-Based Detection Methods -- 4.5.2 Overview of Heterogeneous Graph-Based Detection Methods -- 4.5.3 Case Study: HAWK -- 4.5.3.1 Feature Engineering -- 4.5.3.2 Constructing Heterogeneous Information Network (HIN) -- 4.5.3.3 Constructing Application Graph from HIN -- 4.6 Chapter Summary -- References -- Part III The Adversarial Method for Android Malware Detection -- 5 Static Adversarial Method -- 5.1 Static Obfuscation -- 5.1.1 Code Obfuscation -- 5.1.2 Resource Obfuscation -- 5.1.3 Manifest File Obfuscation -- 5.1.4 Control Flow Obfuscation -- 5.2 Common APK Static Obfuscation Tools -- 5.2.1 Obfuscapk -- 5.2.2 ProGuard.
5.2.3 DexGuard -- 5.2.4 Allatori -- 5.2.5 DashO -- 5.2.6 Bangcle -- 5.2.7 Arxan -- 5.2.8 Comparative Analysis -- 5.3 Research on Static Obfuscation -- 5.3.1 Detection Methods Based on New Features -- 5.3.1.1 Static Detection Based on Perceptual Hashing -- 5.3.1.2 Static Detection Based on Semantic Feature Set -- 5.3.1.3 Static Detection Based on Static Data Streams -- 5.3.1.4 Static Detection Based on Grayscale Images -- 5.3.1.5 Static Detection Based on Permission Pairs -- 5.3.1.6 Static Detection Based on Static Sensitive Subgraphs -- 5.3.1.7 Static Detection Based on Malicious URLs -- 5.3.2 Detection Method Based on Binding Method -- 5.3.2.1 Static Detection Combined with Dynamic -- 5.3.2.2 Static Detection Combined with Machine Learning -- 5.3.2.3 Static Detection Combined with Deep Learning -- 5.4 Chapter Summary -- References -- 6 Dynamic Adversarial Method in Android Malware -- 6.1 Automatic Dynamic Analysis Evasion -- 6.1.1 Detection Dependent -- 6.1.1.1 Fingerprint -- 6.1.1.2 Reverse Turing Test -- 6.1.1.3 Target -- 6.1.2 Detection Independent -- 6.1.2.1 Stalling -- 6.1.2.2 Trigger-Based -- 6.1.2.3 Fileless Attack -- 6.2 Manual Dynamic Analysis Evasion -- 6.2.1 Direct Detection -- 6.2.1.1 Read PEB -- 6.2.1.2 Breakpoint Query -- 6.2.1.3 System Artifacts -- 6.2.1.4 Parent Process Detection -- 6.2.2 Deductive Detection -- 6.2.2.1 Trap -- 6.2.2.2 Time-Based Detection -- 6.2.3 Debugger Evasion -- 6.2.3.1 Control Flow Manipulation -- 6.2.3.2 Lockout Evasion -- 6.2.3.3 Debugger Identification -- 6.2.3.4 Fileless Malware -- 6.3 Related Research About Dynamic Analysis Evasion -- 6.3.1 Research About Improving Sandbox -- 6.3.1.1 The Droid is in the Details: Environment-Aware Evasion of Android Sandboxes -- 6.3.1.2 Morpheus: Automatically Generating Heuristics to Detect Android Emulators -- 6.3.2 Research About Detecting Dynamic Evasion.
6.3.2.1 CamoDroid: An Android App Analysis Environment Resilient Against Sandbox Evasion -- 6.3.2.2 Lumus: Dynamically Uncovering Evasive Android apps -- 6.4 Chapter Summary -- References -- 7 AI-Based Adversarial Method in Android -- 7.1 Introduction to Adversarial Examples -- 7.2 Classification of Adversarial Example Generation Methods -- 7.2.1 Gradient-Based Attacks -- 7.2.2 Optimization-Based Attacks -- 7.2.3 GAN-Based Attacks -- 7.2.4 Domain-Specific Attacks (Audio, Images, Text, etc.) -- 7.3 Black-Box Attacks -- 7.3.1 Introduction to Black-Box Attacks -- 7.3.2 Common Black-Box Attack Methods -- 7.3.3 Transfer Learning-Based Black-Box Attacks -- 7.3.4 Meta-Model Based Black-Box Attacks -- 7.3.5 Query-Based Attacks -- 7.3.6 Optimization-Based Attacks -- 7.4 White-Box Attacks -- 7.4.1 Optimization-Based Attacks -- 7.4.1.1 C& -- W Attack -- 7.4.1.2 PGD Attack -- 7.4.2 Gradient-Based Attacks -- 7.4.2.1 FGSM Attack -- 7.4.2.2 BIM Attack -- 7.4.3 App of Adversarial Attacks in Malware Detection -- 7.5 Chapter Summary -- References -- Part IV The Future Trends of Android Malware Detection -- 8 Future Trends in Android Malware Detection -- 8.1 Machine Learning And Deep Learning Techniques -- 8.1.1 Overview of Machine Learning and Deep Learning for Android Malware Detection -- 8.1.2 Challenges Faced -- 8.2 Integrated Solutions -- 8.2.1 Challenges Faced -- 8.3 Blockchain Technology -- 8.3.1 Introduction to Blockchain Technology -- 8.3.2 Examples of Blockchain Technology in the Field of Android Malware Detection -- 8.4 Hardware Technology -- 8.4.1 Advantages of Hardware Technology -- 8.4.2 Challenges to Hardware Technology -- 8.4.3 Examples of Hardware Technologies Applied in the Field of Android Malware Detection -- 8.5 BPF Technology -- 8.5.1 Development of BPF Technology -- 8.5.2 eBPF Technology Overview.
8.5.3 Examples of BPF Techniques in the Field of Android Malware Detection -- 8.6 Chapter Summary -- References.
Record Nr. UNINA-9910864193603321
Niu Weina  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Artificial Intelligence and Cybersecurity : Theory and Applications / / edited by Tuomo Sipola, Tero Kokkonen, Mika Karjalainen
Artificial Intelligence and Cybersecurity : Theory and Applications / / edited by Tuomo Sipola, Tero Kokkonen, Mika Karjalainen
Autore Sipola Tuomo
Edizione [1st ed. 2023.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Descrizione fisica 1 online resource (300 pages)
Disciplina 006.3
Soggetto topico Artificial intelligence
Data protection - Law and legislation
Cryptography
Data encryption (Computer science)
Computer networks - Security measures
Artificial Intelligence
Privacy
Cryptology
Mobile and Network Security
ISBN 3-031-15030-9
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I Cybersecurity Concerns -- Use of Artificial Intelligence in a Cybersecurity Environment -- Petri Vähäkainu and Martti Lehto -- A Review of Cyber Threat (Artificial) Intelligence in Security -- Management -- Martin Lundgren and Ali Padyab -- Model Based Resilience Engineering for Design and Assessment of -- Mission Critical Systems Containing AI Components -- Automation of CybersecurityWork -- Artificial Intelligence for Cybersecurity Education and Training -- Offensive Machine Learning Methods and the Cyber Kill Chain -- Defensive Machine Learning Methods and the Cyber Defence Chain -- Part II Privacy and Ethics -- Differential Privacy: An Umbrella Review -- AI in Cyber Operations: Ethical and Legal Considerations for End-Users -- Part III Applications -- Android Malware Detection Using Deep Learning -- Artificial Intelligence Enabled Radio Signal Intelligence -- Deep Learning Quantile Regression for Robustness, Confidence and -- Planning -- Model Fooling Threats Against Medical Imaging.
Record Nr. UNINA-9910634050303321
Sipola Tuomo  
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Artificial Intelligence for Security : Enhancing Protection in a Changing World / / edited by Tuomo Sipola, Janne Alatalo, Monika Wolfmayr, Tero Kokkonen
Artificial Intelligence for Security : Enhancing Protection in a Changing World / / edited by Tuomo Sipola, Janne Alatalo, Monika Wolfmayr, Tero Kokkonen
Edizione [1st ed. 2024.]
Pubbl/distr/stampa Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Descrizione fisica 1 online resource (373 pages)
Disciplina 006.3
Soggetto topico Data protection - Law and legislation
Artificial intelligence
Computer networks - Security measures
Privacy
Artificial Intelligence
Mobile and Network Security
ISBN 9783031574528
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Part I Methodological Fundamentals of Artificial Intelligence -- Chapter.1.Safeguarding the Future of Artificial Intelligence: An AI Blueprint -- Chapter.2.Cybersecurity and the AI Silver Bullet.-Chapter.3.Artificial Intelligence and Differential Privacy – Review of Protection Estimate Models -- Chapter.4.To Know What You Do Not Know: Challenges for Explainable AI for Security and Threat Intelligence -- Chapter.5.Securing the Future: The Role of Knowledge Discovery Frameworks -- Chapter.6.Who Guards the Guardians? On Robustness of Deep Neural Networks.-Part II Artificial Intelligence for Critical Infrastructure Protection -- Chapter.7.Opportunities and Challenges of Using Artificial Intelligence in Securing Cyber-Physical System -- Chapter.8.Artificial Intelligence Working to Secure Small Enterprises -- Chapter.9.On the Cyber Security of Logistics in the Age of Artificial Intelligence.-Chapter.10.Fuzzy Machine Learning for Smart Grid Instability Detection -- Chapter.11.On Protection of the Next-Generation Mobile Networks against Adversarial Examples -- Chapter.12.Designing and Implementing an Interactive Cloud Platform for Teaching Machine Learning with Medical Data -- Part III Artificial Intelligence for Anomaly Detection -- Chapter.13.Machine Learning and Anomaly Detection for an Automated Monitoring of Log Data -- Chapter.14.Detecting Web Application DAST Attacks in Large-scale Event.-Chapter15.Enhancing IoT Intrusion Detection Using Hybrid DAIDS-RNN Mode.
Record Nr. UNINA-9910869176503321
Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2024
Materiale a stampa
Lo trovi qui: Univ. Federico II
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Artificial Intelligence Security and Privacy : Second International Conference, AIS&P 2024, Guangzhou, China, December 6-7, 2024, Proceedings / / edited by Fangguo Zhang, Weiwei Lin, Hongyang Yan
Artificial Intelligence Security and Privacy : Second International Conference, AIS&P 2024, Guangzhou, China, December 6-7, 2024, Proceedings / / edited by Fangguo Zhang, Weiwei Lin, Hongyang Yan
Autore Zhang Fangguo
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (254 pages)
Disciplina 006.3
Altri autori (Persone) LinWeiwei
YanHongyang
Collana Lecture Notes in Computer Science
Soggetto topico Artificial intelligence
Security systems
Data protection - Law and legislation
Cryptography
Data encryption (Computer science)
Data protection
Artificial Intelligence
Security Science and Technology
Privacy
Cryptology
Security Services
ISBN 9789819611485
9789819611478
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto -- BadHAR: Backdoor Attacks in Federated Human Activity Recognition Systems. -- Fully Automated Generation Mechanism of Rootfs for Specified Operating Systems under Linux. -- Anti-Side-Channel Attack Mechanisms in Blockchain Payment Channels. -- F2L: A Lightweight Focus Layer against Backdoor Attack in Federated Learning. -- Intelligent backpack based on ireless mobile technology. -- Tourism Industry Upgrading and Public Opinion Prevention Methods Based on BERTopic: A Case Study of Hotel Management. -- Privacy-Preserving Covert Channels in VoLTE via Inter-Frame Delay Modulation. -- Enhancing Adversarial Robustness in Object Detection via Multi-Task Learning and Class-Aware Adversarial Training. -- FedHKD: A Hierarchical Federated Learning Approach Integrating lustering and Knowledge Distillation for Non-IID Data. -- Application of Ensemble Learning Based on High-Dimensional Features in Financial Big Data. -- Collaborative Framework for Dynamic Knowledge Updating and Transparent Reasoning with Large Language Models. -- Zero-Shot Dense Retrieval based on Query Expansion. -- Lightweight Attention-CycleGAN for Nighttime-Daytime Image ransformation. -- Generative Image Steganography Based on Latent Space Vector Coding and Diffusion Model.
Record Nr. UNINA-9910983324703321
Zhang Fangguo  
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
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