Pubbl/distr/stampa	Washington : , : U.S. Government Publishing Office, , 2020
Descrizione fisica	1 online resource (ii, 8 pages)
Collana	Report / 116th Congress, 2d session, Senate
Soggetto topico	Drone aircraft - Law and legislation - United States Data protection - Law and legislation - United States National security - Law and legislation - United States Data protection - Law and legislation Drone aircraft - Law and legislation National security - Law and legislation
Soggetto genere / forma	Legislative materials.
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Altri titoli varianti	American Security Drone Act of 2020
Record Nr.	UNINA-9910713802603321

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

Pubbl/distr/stampa	Luxembourg : , : Office for Official Publications of the European Communities, , 2005-2009
Descrizione fisica	1 online resource
Soggetto topico	Data protection - Law and legislation - European Union countries Privacy, Right of - European Union countries droit de la protection des données protection de la vie privée protection des données data protection legislation protection of privacy data protection Data protection - Law and legislation Privacy, Right of
Soggetto genere / forma	Periodicals. Internet resources.
ISSN	1830-9585
Formato	Materiale a stampa
Livello bibliografico	Periodico
Lingua di pubblicazione	eng
Record Nr.	UNINA-9910142269203321

Autore	Selig Ralf
Pubbl/distr/stampa	Berlin : , : Logos, , 2011
Descrizione fisica	1 online resource (183 pages)
Disciplina	342.0858
Soggetto topico	Data protection - Law and legislation Employees
Soggetto genere / forma	Electronic books.
ISBN	3-8325-9699-2
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	ger
Record Nr.	UNINA-9910467236803321

Pubbl/distr/stampa	Abingdon, Oxon ; ; New York, NY : , : Routledge, , 2021
Descrizione fisica	1 online resource (ix, 270 pages)
Disciplina	340.028563
Soggetto topico	Artificial intelligence - Law and legislation Artificial intelligence - Law and legislation - Criminal privisions Computer crimes - Law and legislation Criminal liability Privacy, Right of Data protection - Law and legislation Artificial intelligence - Law and legislation - China Data protection - Laws and legislation - China
ISBN	1-000-21052-9 0-429-34401-5 1-000-21064-2
Formato	Materiale a stampa
Livello bibliografico	Monografia
Lingua di pubblicazione	eng
Nota di contenuto	Emerging technologies and the criminal law / Dennis J. Baker and Paul H. Robinson -- Financial technology : opportunities and challenges to law and regulation / the Right Hon. Lord Hodge P.C. -- Between prevention and enforcement : the role of 'disruption' in confronting cybercrime / Jonathan Clough -- Preventive cybercrime and cybercrime by omission in China / He Ronggong and Jinglijia -- Criminal law protection of virtual property / Zhang Mingkai and Wang Wenjing -- Criminalising cybercrime facilitation by omission and its remote harm form in China / Liang Genlin and Dennis J. Baker -- Rethinking personal data protection in the criminal law of China / Lao Dongyan -- Using conspiracy and complicity for criminalising cyber-fraud in China : lessons from the common law / Li lifeng, Tianhong Zhao and Dennis J. Baker -- Sadie Creese -- AI v IP : criminal liability for intellectual property offences of artificial intelligence entities / Gabriel Hallevy -- Do not panic : artificial intelligence and criminal law 101 / Mark Dsouza.
Record Nr.	UNINA-9910794316103321