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[null,"1.","Record Nr.",null,"UNISALENTO991000186169707536"]
[null,null,"Autore",null,"Heine, Heinrich"]
[null,null,"Titolo",null,"La scienza della libertà : scritti politici / Heinrich Heine ; a cura di Fritz Mende"]
[null,null,"Pubbl/distr/stampa",null,"Roma : Editori riuniti, 1972"]
[null,null,"Descrizione fisica",null,"305 p. ; 23 cm"]
[null,null,"Collana",null,"Biblioteca del pensiero moderno ; 33"]
[null,null,"Disciplina",null,"320.8"]
[null,null,"Soggetti",null,"Politica - Teorie"]
[null,null,"Lingua di pubblicazione",null,"Italiano"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNISA996464434703316"]
[null,null,"Titolo",null,"Information and communications security . Part 1 : 23rd international conference, ICICS 2021, Chongqing, China, November 19-21, 2021 : proceedings. / / Debin Gao [and three others], editors"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2021]"]
[{"$nulls":4},"©2021"]
[null,null,"ISBN",null,"3-030-86890-7"]
[null,null,"Descrizione fisica",null,"1 online resource (496 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.12918"]
[null,null,"Disciplina",null,"001.5436"]
[null,null,"Soggetti",null,"Cryptography"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Includes index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Keynotes -- Engineering Trustworthy Data-Centric Software: Intelligent Software Engineering and Beyond -- Securing Smart Cars - Opportunities and Challenges -- Contents - Part I -- Contents - Part II -- Blockchain and Federated Learning -- The Golden Snitch: A Byzantine Fault Tolerant Protocol with Activity -- 1 Introduction -- 2 Preliminaries -- 3 Protocol Overview -- 4 The Golden Snitch Protocol -- 4.1 Setup -- 4.2 Replicas Vote in an Honest Round -- 4.3 Replicas Recover in a Timeout Round -- 4.4 A Leader Proposes Proposal -- 5 Performance -- 5.1 Fault-Free Cases -- 5.2 Normal Cases -- 6 Discussion and Conclusion -- A  Analysis of Correctness -- A.1  Safety -- A.2  Liveness -- References -- Rectifying Administrated ERC20 Tokens -- 1 Introduction -- 2 Background -- 3 Administrated ERC20 Patterns -- 3.1 Self-destruction -- 3.2 Deprecation -- 3.3 Change of Address -- 3.4 Change of Parameters -- 3.5 Minting and Burning -- 4 Administrated Tokens in the Wild -- 4.1 Data Set -- 4.2 ERC20 Administration Features -- 4.3 Classifier Evaluation and Model Selection -- 4.4 Implementation and Evaluation of the Analysis Workflow -- 4.5 Results -- 5 SafelyAdministrated Library -- 5.1 Deferred Maintenance -- 5.2 Contract Board of Trustees -- 5.3 Safe Pause -- 5.4 Implementation -- 5.5 Limitation -- 6 Related Work -- 7 Conclusion -- References -- Moat: Model Agnostic Defense against Targeted Poisoning Attacks in Federated Learning -- 1 Introduction -- 2 Related Work -- 3 Federated Learning and Threat Model -- 3.1 Federated Learning -- 3.2 Threat Model -- 4 Moat: The Proposed Defense Technique -- 4.1 Overview -- 4.2 Algorithm -- 5 "]
[{"$nulls":4},"Experiment and Result Analysis -- 5.1 Results -- 6 Discussion -- 7 Conclusion -- A  SHAP Analysis -- B  Results on Distributed Attack -- References -- Malware Analysis and Detection."]
[{"$nulls":4},"Certified Malware in South Korea: A Localized Study of Breaches of Trust in Code-Signing PKI Ecosystem -- 1 Introduction -- 2 Background and Motivation -- 2.1 Overview of the Code-Signing PKI -- 2.2 Code-Signing Process -- 2.3 Revocation -- 2.4 Motivation -- 3 Data Collection -- 3.1 Data Source -- 3.2 System Overview -- 3.3 Binary Labeling -- 4 Code-Signing PKI Abuse in Korea -- 4.1 Abusers -- 4.2 Issuer -- 4.3 Certificate Life-Cycle -- 5 Related Work -- 6 Conclusion -- A  Appendix -- References -- GAN-Based Adversarial Patch for Malware C2 Traffic to Bypass DL Detector -- 1 Introduction -- 2 Background and Related Work -- 2.1 Background-Malware Traffic Detection -- 2.2 Related Work-Malware Traffic Evasion -- 3 Method -- 3.1 Thread Model -- 3.2 Framework -- 3.3 Generation Module - WGAN -- 3.4 Transfer Module-Transfer Learning -- 4 Experiment -- 4.1 Dataset -- 4.2 Hyperparameters -- 4.3 Detector -- 5 Results -- 5.1 Evasion Performance -- 5.2 Time Performance -- 6 Real-Life Experiment -- 6.1 Custom Malware -- 6.2 Impact on Malware -- 7 Conclusion -- References -- Analyzing the Security of OTP 2FA in the Face of Malicious Terminals -- 1 Introduction -- 2 Background -- 2.1 One Time Pin Based 2FA -- 2.2 Malware on Terminal -- 3 Our Attack: Overview and Design -- 3.1 Attack Overview -- 3.2 Attack Assumptions -- 3.3 Attack Implementations Vs. Other Known Attack -- 3.4 Attack Components -- 3.5 Internal Attack -- 3.6 Remote Attack -- 4 Implementation -- 4.1 Attack Components of Internal Attack -- 4.2 Attack Components of Remote Attack -- 5 Evaluation -- 5.1 Evaluation of Commercially Deployed OTP-2FA Schemes in the Face of the Attack -- 5.2 Detectability from Terminal and 2FA Device -- 5.3 Detectability from Service -- 5.4 Detectability in the Presence of Anti-Malware Program -- 5.5 Detectability During Attack Module Deployment -- 6 Discussion and Future Work."]
[{"$nulls":4},"6.1 Attack Summary -- 6.2 General Discussion -- 6.3 Mitigation Strategy -- 6.4 Limitations and Future Work -- 7 Related Work -- 8 Conclusion -- A  Appendix -- A.1  Tables -- A.2  Other snapshots -- References -- IoT Security -- Disappeared Face: A Physical Adversarial Attack Method on Black-Box Face Detection Models -- 1 Introduction -- 2 Related Works -- 2.1 Adversarial Attacks on Face Recognition -- 2.2 Adversarial Attacks on Face Detection -- 3 Our Proposed Method -- 3.1 Configure Input Images -- 3.2 Search for Face Detection Models' Public Weakness -- 3.3 Update the Adversarial Patches -- 4 Experiments and Result Analysis -- 4.1 Experiment Settings -- 4.2 Escape Experiments in the Real World -- 4.3 Contrast Experiments -- 4.4 Ablation Experiments -- 5 Conclusion -- References -- HIAWare: Speculate Handwriting on Mobile Devices with Built-In Sensors -- 1 Introduction -- 2 Preliminaries -- 2.1 Targeted Vulnerable Apps -- 2.2 Motion Sensor Selection -- 2.3 Threat Model -- 3 HIAWare Design -- 3.1 Handwriting Detection -- 3.2 Sensor Data Capture -- 3.3 Preprocessing -- 3.4 Posture-Aware Analysis -- 3.5 Character Restoration -- 4 Algorithm Details -- 4.1 MCFAR Algorithm -- 4.2 User-Independent Posture-Aware Algorithm -- 5 Performance Evaluation -- 5.1 Experiment Setup -- 5.2 Performance of Segment Detection -- 5.3 Performance of Different Holding Postures -- 5.4 Performance of Different Devices -- 5.5 Performance of Different Inputs -- 5.6 Discussions -- 6 Related Work -- 7 Conclusions -- References -- Studies of Keyboard Patterns in Passwords: Recognition, Characteristics and Strength Evolution -- 1 Introduction -- 2 General Method of Keyboard Pattern Recognition -- 2.1 Recognition Method "]
[{"$nulls":4},"Design -- 2.2 Recognition Results -- 3 Characteristic Analyses of Keyboard Patterns -- 3.1 Length Distribution of Keyboard Patterns."]
[{"$nulls":4},"3.2 Top Popular Keyboard Patterns -- 3.3 Common Structures of Keyboard Patterns -- 3.4 Characters' Frequency in Keyboard Patterns -- 3.5 Frequency Distribution of Keyboard Patterns -- 4 Security Impacts of Keyboard-Pattern-Based Passwords -- 4.1 Method Design -- 4.2 Evaluation Results -- 5 Conclusions and Suggestions -- References -- CNN-Based Continuous Authentication on Smartphones with Auto Augmentation Search -- 1 Introduction -- 2 Related Work -- 2.1 Continuous Authentication System -- 2.2 Time-Series Data Augmentation Method -- 2.3 Auto Augmentation Method -- 3 CAuSe Architecture -- 3.1 Data Collection and Preprocessing -- 3.2 Auto Augmentation Search -- 3.3 Feature Extraction -- 3.4 Authentication with LOF Classifier -- 4 Performance Evaluation -- 4.1 Experimental Settings -- 4.2 Feature Number and Classifier Parameter -- 4.3 Auto Augmentation Search -- 4.4 Optimal Strategy -- 4.5 Comparison with Representative Schemes -- 5 Conclusion -- References -- Generating Adversarial Point Clouds on Multi-modal Fusion Based 3D Object Detection Model -- 1 Introduction -- 2 Related Work -- 2.1 Multi-modal Fusion -- 2.2 Adversarial Point Clouds -- 2.3 Attacks on 3D Object Detection -- 3 Robustness Analysis -- 4 Generating Adversarial Point Clouds -- 4.1 Problem Definition -- 4.2 Input Perturbation -- 4.3 Objective Function -- 4.4 Attack Method -- 5 Experiments -- 5.1 Experiment Setup -- 5.2 Results and Discussion -- 6 Conclusion -- References -- Source Identification from In-Vehicle CAN-FD Signaling: What Can We Expect? -- 1 Introduction -- 2 Background and Related Work -- 2.1 Controller Area Network -- 2.2 Comparing CAN-FD with CAN -- 2.3 Related Work -- 3 Signaling and Ringing -- 3.1 ECUs' Voltage Output Behavior -- 3.2 Ringing and Its Intensity -- 4 System Model -- 4.1 Threat Models -- 4.2 Signal Acquisition and Preprocessing -- 4.3 Feature Extraction."]
[{"$nulls":4},"4.4 Identifying ECUs -- 5 Source Identification and Intrusion Detection -- 5.1 Experiment Setup -- 5.2 Sender Identification -- 5.3 Detecting Known/Unknown ECUs -- 6 Discussions -- A  Source Identification on Type B and Recessive States-Falling Edges -- B  Detecting Known ECUs -- C  Detecting Unknown ECUs -- References -- EmuIoTNet: An Emulated IoT Network for Dynamic Analysis -- 1 Introduction -- 2 Background and Related Work -- 2.1 Security Issues in IoT Network -- 2.2 IoT Emulation Methods -- 3 Basic Design of EmuIotNet -- 3.1 Design Goals -- 3.2 Overview Architecture -- 3.3 Challenges -- 4 Implementation Details -- 4.1 IoT Device Emulation -- 4.2 Companion Application Emulation -- 4.3 Network Models -- 4.4 IP Configuration -- 5 Evaluation -- 5.1 Scalability in Device Emulation -- 5.2 Compatibility in Network Setup -- 5.3 Dynamic Analysis on Networks -- 6 Discussion and Conclusion -- References -- Software Security -- ACGVD: Vulnerability Detection Based on Comprehensive Graph via Graph Neural Network with Attention -- 1 Introduction -- 2 Related Work -- 3 ACGVD Pipeline -- 3.1 Overview of ACGVD -- 3.2 Comprehensive Graph Representation -- 3.3 Node Feature Initialization -- 3.4 Double-Level Attention Mechanism -- 3.5 Classifier Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Evaluation Metrics -- 5 Experiments Study -- 5.1 How Effective Is ACGVD When Compared with the Traditional Static Analysis Tools? -- 5.2 How Effective Is ACGVD When Compared with Deep Learning Method Based on Single Semantic Graph? -- 5.3 How Effective Is ACGVD When Compared with Graph Neural Network Method Without Attention Mechanism? -- 5.4 What Is the Impact of Modifying the Classifier on the Experiment? -- 6 Threats Factors -- 7 Conclusion -- "]
[{"$nulls":4},"References -- TranFuzz: An Ensemble Black-Box Attack Framework Based on Domain Adaptation and Fuzzing."]
[{"$nulls":4},"1 Introduction."]