11011nam 2200505 450 99646450010331620220327041531.03-030-78086-4(CKB)5470000000736331(MiAaPQ)EBC6676198(Au-PeEL)EBL6676198(OCoLC)1259363420(PPN)257358412(EXLCZ)99547000000073633120220327d2021 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCyber security cryptography and machine learning 5th International Symposium, CSCML 2021, Be'er Sheva, Israel, July 8-9, 2021, proceedings /editors, Shlomi Dolev [and three others]Cham, Switzerland :Springer,[2021]©20211 online resource (520 pages)Lecture notes in computer science : security and cryptology ;Volume 127163-030-78085-6 Includes bibliographical references and index.Intro -- Preface -- Organization -- Contents -- Programmable Bootstrapping Enables Efficient Homomorphic Inference of Deep Neural Networks -- 1 Introduction -- 2 Preliminaries -- 2.1 Torus and Torus Polynomials -- 2.2 Probability Distributions -- 3 Discretized TFHE -- 3.1 Encoding/Decoding Messages -- 3.2 Description -- 3.3 Leveled Operations -- 4 Programmable Bootstrapping -- 4.1 Blind Rotation -- 4.2 Look-Up Table Evaluation -- 5 Application to Neural Networks -- 5.1 Layers Without PBS -- 5.2 Layers with PBS -- 6 Experimental Results and Benchmarks -- 7 Conclusion -- A Complexity Assumptions Over the Real Torus -- B Algorithms -- B.1 Blind Rotation -- B.2 Sample Extraction -- B.3 Key Switching -- References -- Adversaries Strike Hard: Adversarial Attacks Against Malware Classifiers Using Dynamic API Calls as Features -- 1 Introduction -- 2 Problem Statement -- 3 Adversarial Learning Background -- 4 Related Work -- 5 Design and Implementation -- 5.1 Data Set Collection and Features Extraction -- 5.2 Target BlackBox Models -- 5.3 Malware Evasion Using GAN (MEGAN) and MEGAN with Reduced Perturbation (MEGAN-RP) -- 5.4 Malware Evasion Using Reinforcement Agents -- 6 Evaluation Results -- 7 Conclusion and Future Work -- References -- Privacy-Preserving Coupling of Vertically-Partitioned Databases and Subsequent Training with Gradient Descent -- 1 Introduction -- 1.1 Related Work -- 1.2 Outline -- 2 Hidden Set Intersection -- 3 Secure Gradient Descent -- 3.1 Regression -- 3.2 Classification -- 3.3 Gradient Descent Approach -- 3.4 MPyC -- 4 Performance -- 4.1 Run-Time -- 4.2 Accuracy -- 5 Conclusions and Future Work -- References -- Principal Component Analysis Using CKKS Homomorphic Scheme -- 1 Introduction -- 2 Preliminaries -- 2.1 CKKS Homomorphic Encryption Scheme -- 2.2 Principal Component Analysis (PCA) -- 2.3 Goldschmidt's Algorithm.2.4 R2 Score -- 3 Vector Operations -- 3.1 Norm and Inversion by Norm -- 3.2 Ciphertext Packing -- 3.3 Vector Operations on Ciphertext and Sub-ciphertexts -- 4 Homomorphic Evaluations -- 4.1 Homomorhpic Goldschmidt's Algorithm -- 4.2 Homomorphic Power Method -- 4.3 Homomorphic PCA -- 5 Implementation Details and Results -- 5.1 Parameter Selection -- 5.2 Results -- 6 Conclusion and Future Work -- References -- DepthStAr: Deep Strange Arguments Detection -- 1 Introduction -- 2 Goals -- 3 Pattern Description -- 4 Methodology -- 4.1 A Formal Outline of the Algorithm -- 4.2 Suggested Workflow to Find Exploitable Security Weaknesses -- 5 Implementation -- 5.1 The angr Framework -- 5.2 Implementation Details -- 6 Evaluation -- 6.1 Rediscovery of Known Weaknesses in libcurl -- 6.2 Newly Detected Weaknesses -- 6.3 Synthetic Evaluation -- 7 A More General Take Away -- 8 Conclusion -- References -- Robust Multivariate Anomaly-Based Intrusion Detection System for Cyber-Physical Systems -- 1 Introduction -- 2 Threat Model -- 3 Proposed Methodology -- 3.1 Anomaly Detection Algorithm-Denoising Autoencoder (DAE) -- 3.2 Localization of the Attack Points -- 4 Experiments and Results -- 4.1 Dataset -- 4.2 Training Phase -- 4.3 Performance Evaluation Phase -- 4.4 Robustness in the Presence of Adversary During Training -- 5 Deployment of DAE in Real Time -- 6 Conclusion -- References -- Privacy-Preserving Password Strength Meters with FHE -- 1 Introduction -- 2 Fully Homomorphic Encryption -- 2.1 Privacy Preserving Search -- 2.2 Privacy Preserving Index Search -- 3 Privacy Preserving Password Strength Meters -- 3.1 Privacy Preserving Markov Model -- 3.2 Privacy Preserving PCFG Model -- 4 Conclusion and Future Work -- References -- Automatic Detection of Water Stress in Corn Using Image Processing and Deep Learning -- 1 Introduction -- 2 Proposed Approach -- 2.1 Dataset.2.2 Proposed Method -- 3 Results -- 4 Conclusions -- References -- Tortoise and Hares Consensus: The Meshcash Framework for Incentive-Compatible, Scalable Cryptocurrencies -- 1 Introduction -- 1.1 Consensus, Money, and Contracts -- 1.2 Permissionless Consensus via PoW -- 1.3 Importance of Incentive-Compatibility -- 1.4 Drawbacks of Leader Election -- 1.5 Our Contributions -- 1.6 Related Works -- 2 Informal Protocol Overview -- 3 Meshcash Security -- 3.1 Security Proof Overview -- References -- Game of Drones - Detecting Spying Drones Using Time Domain Analysis -- 1 Introduction -- 2 Background -- 2.1 Video Coding Algorithms -- 3 Related Work -- 4 Adversary Model and Proposed Detection Scheme -- 4.1 Detection Model -- 4.2 Detecting FPV Channels -- 5 Influence of Physical Stimulus -- 5.1 Lab Experiments -- 6 Evaluation -- 7 Conclusions and Future Work -- References -- Privacy Vulnerability of NeNDS Collaborative Filtering -- 1 Introduction -- 2 The NeNDS Algorithm -- 3 Privacy Attack on NeNDS -- 4 NeNDS Shortcomings -- 5 Conclusions -- References -- Lawful Interception in WebRTC Peer-To-Peer Communication -- 1 Introduction -- 2 Background and Related Work -- 2.1 Browsers' Support and Open Source WebRTC Libraries -- 2.2 ETSI Reference Model for Lawful Interception -- 2.3 Current Solutions for Intercepting VoIP Calls -- 3 WebRTC -- 3.1 Connection Initiation -- 3.2 Encryption -- 3.3 P2P Communication -- 3.4 Multi-party Conversations -- 4 The Interception Model -- 4.1 Signaling Services -- 4.2 Web Applications -- 5 Showcase -- 5.1 Signaling Services -- 5.2 Web Applications -- 5.3 LEA Management Console -- 6 Limitation of the Current Work -- 7 Conclusion -- References -- Hierarchical Ring Signatures Immune to Randomness Injection Attacks -- 1 Introduction -- 2 Hierarchical Signature Scheme -- 2.1 Preliminaries and Notation.2.2 Definition of Hierarchical-Signature Scheme -- 3 New Security Model -- 3.1 Anonymity Model -- 3.2 Strong Unforgeability Model -- 4 Modified Specific HRS Scheme -- 4.1 Unforgeability Analysis -- 4.2 Anonymity Analysis -- 5 Implementation -- 6 Conclusion -- References -- Theoretical Aspects of a Priori On-Line Assessment of Data Predictability in Applied Tasks -- 1 Introduction -- 2 Description and Problem Definitions -- 3 Metrics of Predictability: Related Work -- 3.1 Selection of a Predictor Based on the Model of Losses from Erroneous Predictions -- 4 Model and Procedure for Choosing a Predictor -- 5 "Ontological" Factors in Probabilistic Models of Prediction -- 6 Conclusion -- References -- Randomly Rotate Qubits, Compute and Reverse for Weak Measurements Resilient QKD and Securing Entanglement -- 1 Introduction -- 2 The Random Basis Encryption Scheme -- 3 Securing Entanglement -- 4 WM and the Random Basis CNOT QKD Scheme -- References -- Warped Input Gaussian Processes for Time Series Forecasting -- 1 Introduction -- 2 Preliminaries -- 3 Warped Input Gaussian Process Model -- 3.1 Model -- 3.2 Training -- 3.3 Forecasting -- 3.4 Modelling Seasonality -- 3.5 Time and Space Complexity -- 4 Empirical Evaluation -- 4.1 Synthetic Datasets -- 4.2 Real-World Datasets -- 5 Related Work -- 6 Conclusion -- References -- History Binding Signature -- 1 Introduction -- 2 Preliminaries -- 2.1 Verifiable Secret Sharing -- 2.2 Verifiable Secret Public Sharing -- 2.3 Verifiable Random Functions -- 3 History Binding Signature -- 4 Conditions for a Valid Signature -- 4.1 Unforgeability -- 4.2 Security -- 4.3 Correctness (Signing) -- 4.4 Correctness (Key-Revealing) -- 5 Conclusion and Future Work -- References -- Effective Enumeration of Infinitely Many Programs that Evade Formal Malware Analysis -- 1 Introduction -- 2 Foundations of Computation Theory.3 Recursive Function Theory -- 4 Theoretical Impossibility of a Complete formal Malware/Non-malware Program Classification -- 5 Discussion and Directions for Further Research -- References -- DNS-Morph: UDP-Based Bootstrapping Protocol for Tor -- 1 Introduction -- 1.1 Our Contribution -- 2 Related Work -- 3 Threat Model -- 4 Obfsproxy Design -- 5 DNS-Morph Design -- 6 DNS-Morph Reliability -- 6.1 Received Packets Acknowledgments -- 6.2 Sorting Received Packets -- 6.3 DNS-Morph Identifiers' Encryption and Decryption -- 6.4 DNS-Morph Multiple Sessions Support -- 7 DNS-Morph Encoded Packets -- 8 DNS-Morph: Security Analysis -- 8.1 Censor's DPI Capabilities -- 8.2 DNS-Morph DPI Resistance -- 8.3 Additional Attacks and Resistance -- 8.4 Active Probing and Replay Attack Resistance -- 8.5 Domain Names' Entropy -- 9 DNS-Morph Design Considerations -- 9.1 Choice of DNS -- 9.2 Choice of Base32 -- 9.3 Query Types -- 9.4 Recursive DNS -- 10 Tests and Results -- 10.1 Test Setup -- 10.2 Client's Testing Environment -- 10.3 Deep Packet Inspection Tools -- 11 Summary -- 11.1 Future Works -- References -- Polynomial Time k-Shortest Multi-criteria Prioritized and All-Criteria-Disjoint Paths -- 1 Introduction and Related Work -- 2 Finding Prioritized Multi-criteria k-Shortest Paths in Polynomial Time -- 3 Prioritized Multi-criteria 2-Disjoint (Node/Edge) Shortest Paths -- 4 k-Disjoint All-Criteria-Shortest Paths -- References -- Binding BIKE Errors to a Key Pair -- 1 Introduction -- 2 Specific Proposals for BIKE -- 3 Practical Considerations and the BIKE Additional Implementation Package -- 4 Conclusion -- References -- Fast and Error-Free Negacyclic Integer Convolution Using Extended Fourier Transform -- 1 Introduction -- 2 Preliminaries -- 3 Efficient Negacyclic Convolution -- 3.1 Redundant Approach -- 3.2 Non-redundant Approach -- 4 Analysis of Error Propagation.4.1 Error Propagation Through FFT and FFNT.LNCS sublibrary.SL 4,Security and cryptology ;Volume 12716.Data encryption (Computer science)CongressesData encryption (Computer science)005.82Dolev ShlomiMiAaPQMiAaPQMiAaPQBOOK996464500103316Cyber Security Cryptography and Machine Learning2200042UNISA02826nam 22005535 450 991048498820332120251230065906.03-030-39445-X10.1007/978-3-030-39445-5(CKB)4100000010473815(MiAaPQ)EBC6121811(DE-He213)978-3-030-39445-5(PPN)243767412(MiAaPQ)EBC6122072(EXLCZ)99410000001047381520200224d2020 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierAdvances in Information and Communication Proceedings of the 2020 Future of Information and Communication Conference (FICC), Volume 1 /edited by Kohei Arai, Supriya Kapoor, Rahul Bhatia1st ed. 2020.Cham :Springer International Publishing :Imprint: Springer,2020.1 online resource (927 pages)Advances in Intelligent Systems and Computing,2194-5365 ;1129Includes index.3-030-39444-1 This book presents high-quality research on the concepts and developments in the field of information and communication technologies, and their applications. It features 134 rigorously selected papers (including 10 poster papers) from the Future of Information and Communication Conference 2020 (FICC 2020), held in San Francisco, USA, from March 5 to 6, 2020, addressing state-of-the-art intelligent methods and techniques for solving real-world problems along with a vision of future research Discussing various aspects of communication, data science, ambient intelligence, networking, computing, security and Internet of Things, the book offers researchers, scientists, industrial engineers and students valuable insights into the current research and next generation information science and communication technologies.Advances in Intelligent Systems and Computing,2194-5365 ;1129Computational intelligenceTelecommunicationComputational IntelligenceCommunications Engineering, NetworksComputational intelligence.Telecommunication.Computational Intelligence.Communications Engineering, Networks.006.3Arai Koheiedthttp://id.loc.gov/vocabulary/relators/edtKapoor Supriyaedthttp://id.loc.gov/vocabulary/relators/edtBhatia Rahuledthttp://id.loc.gov/vocabulary/relators/edtMiAaPQMiAaPQMiAaPQBOOK9910484988203321Advances in information and communication1897925UNINA