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[null,"1.","Record Nr.",null,"UNISA996587860703316"]
[null,null,"Autore",null,"Pöpper Christina"]
[null,null,"Titolo",null,"Applied Cryptography and Network Security : 22nd International Conference, ACNS 2024, Abu Dhabi, United Arab Emirates, March 5-8, 2024, Proceedings, Part II"]
[null,null,"Pubbl/distr/stampa",null,"Cham : , : Springer, , 2024"]
[{"$nulls":4},"©2024"]
[null,null,"ISBN",null,"3-031-54773-X"]
[null,null,"Edizione",null,"[1st ed.]"]
[null,null,"Descrizione fisica",null,"1 online resource (523 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science Series ; ; v.14584"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Abstracts of Keynote Talks -- Applying Machine Learning to Securing Cellular Networks -- Real-World Cryptanalysis -- CAPTCHAs: What Are They Good For? -- Contents - Part II -- Post-quantum -- Automated Issuance of Post-Quantum Certificates: A New Challenge -- 1 Introduction -- 2 Background -- 2.1 TLS Version 1.3 -- 2.2 ACMEv2 Characteristics -- 2.3 Post-Quantum Cryptography -- 3 Quantum Threat and PQC Adoption -- 3.1 Quantum Threats in ACME -- 3.2 Integrating PQC Algorithms -- 3.3 Impacts of PQC in ACME -- 4 Proposed ACME Challenge -- 4.1 Design Details -- 4.2 Issuance and Renewal Timings -- 4.3 Discussion -- 5 Final Remarks and Future Work -- A  ACME's HTTP-01 Challenge -- B  POST Request Example -- References -- Algorithmic Views of Vectorized Polynomial Multipliers - NTRU Prime -- 1 Introduction -- 1.1 Contributions -- 1.2 Code -- 1.3 Structure of This Paper -- 2 Preliminaries -- 2.1 Polynomials in NTRU Prime -- 2.2 Cortex-A72 -- 2.3 Modular Arithmetic -- 3 Fast Fourier Transforms -- 3.1 The Chinese Remainder Theorem (CRT) for Polynomial Rings -- 3.2 Cooley-Tukey FFT -- 3.3 Bruun-Like FFTs -- 3.4 Good-Thomas FFTs -- 3.5 Rader's FFT for Odd Prime p -- 3.6 Schönhage's and Nussbaumer's FFTs -- 4 Implementations -- 4.1 The Needs of Vectorization -- 4.2 Good-Thomas FFT in ``BigSmall'' Polynomial Multiplications -- 4.3 Good-Thomas, Schönhage's, and Bruun's FFT -- 4.4 Good-Thomas, Rader's, and Bruun's FFT -- 5 Results -- 5.1 Benchmark Environment -- 5.2 Performance of Vectorized Polynomial Multiplications -- 5.3 Performance of Schemes -- A  Detailed "]
[{"$nulls":4},"Performance Numbers -- References -- Efficient Quantum-Safe Distributed PRF and Applications: Playing DiSE in a Quantum World -- 1 Introduction -- 1.1 Related Works and Our Contributions -- 2 Preliminaries and Background -- 2.1 Notation."]
[{"$nulls":4},"2.2 Some Terminologies and Definitions -- 2.3 Distributed PRF (DPRF) -- 2.4 (t, T)-Threshold Secret Sharing -- 3 Our Contribution: Proposed Distributed PRF -- 3.1 Underlying Quantum-Safe PRF -- 3.2 Proposed (T, T)-Distributed PRF -- 3.3 Generalised (t, T)-Threshold PRF -- 3.4 Choice of Parameters -- 3.5 Proposed PQDPRF vs. the Lattice-Based DPRF in ch3libert2021adaptively -- 4 Application -- 4.1 An Overview of the DiSE Protocol -- 4.2 Our Improved PQ-DiSE Protocol -- 5 Experimental Result -- 6 Conclusion and Future Work -- A Generalised (t, T)-Threshold PRF -- A.1 Proof of Correctness and Consistency -- A.2 Proof of Security -- References -- On the Untapped Potential of the Quantum FLT-Based Inversion -- 1 Introduction -- 1.1 Background -- 1.2 Our Contribution -- 2 Preliminaries -- 2.1 Binary Elliptic Curve Discrete Logarithm Problem -- 2.2 Quantum Computation in F2n -- 2.3 Shor's Algorithm for Solving the Binary ECDLP -- 3 Our Method -- 3.1 Register-Bounded Addition Chain -- 3.2 Modified Quantum Point Addition Algorithm -- 3.3 Depth Reduction of Quantum Multiple Squaring Circuits -- 3.4 Proposed Inversion Algorithm -- 4 Comparison -- 4.1 Our Choice of Register-Bounded Addition Chains -- 4.2 Quantum Resources Trade-Off in Our Proposed Inversion Algorithm -- 4.3 Comparison with Previous Methods in Shor's Algorithm -- References -- Breaking DPA-Protected Kyber via the Pair-Pointwise Multiplication -- 1 Introduction -- 1.1 Our Contribution -- 1.2 State of the Art -- 2 Notation and Preliminaries -- 2.1 Kyber -- 2.2 Number Theoretic Transform (NTT) -- 2.3 Online Template Attacks -- 3 Our Attack -- 3.1 Attack Steps-Extracting the Key via q+q Templates -- 3.2 Attack on DPA-Protected Kyber -- 4 Simulations -- 4.1 Implementation of Pair-Point Multiplication -- 4.2 Hamming Weight Model -- 4.3 Simulations of Gaussian Noise -- 5 Experimental Evidence."]
[{"$nulls":4},"5.1 Attack Analysis -- 6 Possible Countermeasures -- A Kyber Algorithms -- B Montgomery Reduction -- C Details on Noiseless and Noisy Simulations -- D Comparison -- References -- Cryptographic Protocols II -- The Key Lattice Framework for Concurrent Group Messaging -- 1 Introduction -- 1.1 Related Work -- 1.2 Technical Overview -- 2 General Definitions and Notation -- 3 Key Lattice -- 3.1 Key Evolution -- 3.2 The Key Graph -- 3.3 Instantiation -- 3.4 Key Lattice as a Key Management Technique -- 4 Group Key Agreement -- 5 Group Randomness Messaging -- 5.1 Instantiation -- 6 Group Messaging -- 6.1 Security Definition -- 6.2 GM from GRM and GKA -- 6.3 Concrete Costs -- 6.4 Main Theorem -- References -- Identity-Based Matchmaking Encryption from Standard Lattice Assumptions -- 1 Introduction -- 2 Preliminaries -- 2.1 Identity-Based Matchmaking Encryption -- 2.2 Homomorphic Signatures -- 3 IB-ME: Generic Construction -- 3.1 IB-ME Achieving Enhanced Privacy -- 3.2 Achieving Authenticity -- 3.3 Security Analysis -- 4 Instantiations from Lattice Assumptions -- 5 Conclusions -- A Reusable Computational Extractors -- B Identity-Based Encryption -- C Indistinguishable from Random Privacy vs Enhanced Privacy -- References -- Decentralized Private Stream Aggregation from Lattices -- 1 Introduction -- 1.1 Our Contributions -- 1.2 Related Work -- 1.3 Organization -- 2 Preliminaries -- 2.1 Lattices -- 2.2 Learning with Errors -- 2.3 Pseudorandom Functions -- 3 Decentralized Private Stream Aggregation -- 3.1 Our Construction -- 3.2 Aggregator Obliviousness -- 3.3 Parameters -- 3.4 Decentralized Setup -- 3.5 Client Failures -- 3.6 Optimizing Peer-to-Peer Communication -- 3.7 Dynamic Join and "]
[{"$nulls":4},"Leave -- 4 DPSA in the Standard Model -- 5 Conclusion -- A Private Stream Aggregation -- B Games for the Proof of Theorem 1 -- References -- Wireless and Networks."]
[{"$nulls":4},"A Security Analysis of WPA3-PK: Implementation and Precomputation Attacks -- 1 Introduction -- 2 Background -- 2.1 Simultaneous Authentication of Equals (SAE) -- 2.2 WPA3 Public Key (WPA3-PK) -- 2.3 Generation of the WPA3-PK Password -- 2.4 Security Guarantees Provided by WPA3-PK -- 3 Implementation and Network-Based Attacks -- 3.1 Bad Randomness Leaks the Password -- 3.2 Network-Based Attacks -- 4 Precomputation Attacks and Rainbow Tables -- 4.1 Background on Time-Memory Trade-Off Attacks -- 4.2 Motivation: SSID Reuse -- 4.3 Baseline Precomputation Attack Against WPA3-PK -- 4.4 Improved Analysis of the Baseline Precomputation Attack -- 4.5 Rainbow Tables for WPA3-PK -- 4.6 Rainbow Table: Performance Experiments -- 5 Multi-network Password Collisions -- 5.1 Constructing Password Collisions -- 5.2 Public Key Embedding and Trailing Data -- 5.3 Accepting Trailing Data Inside the Public Key -- 5.4 Multi-network Password Collisions -- 6 Defenses and Discussion -- 6.1 Handling Bad Randomness: Encrypting the Public Key -- 6.2 Preventing Network-Layer Attacks -- 6.3 Mitigating Time-Memory Trade-Off Attacks -- 6.4 Preventing Password Collisions: Committing to an SSID Length -- 7 Related Work -- 8 Conclusion -- References -- When and How to Aggregate Message Authentication Codes on Lossy Channels? -- 1 Introduction -- 2 MAC Aggregation on Lossy Channels -- 2.1 Message Authentication Codes -- 2.2 MAC Aggregation to Combat Bandwidth Scarcity -- 2.3 Introducing Existing MAC Aggregation Schemes -- 2.4 Interplay of Lossy Channels and MAC Aggregation -- 3 Synthetic Measurements -- 3.1 Simulation Setup -- 3.2 Influence of Channel Quality on Goodput -- 3.3 Influence of Payload Length on Goodput -- 3.4 Optimal Packet Lengths for Authenticated Data -- 4 MAC Aggregation in Real-World Scenarios -- 4.1 Description of the Scenarios."]
[{"$nulls":4},"4.2 Evaluating MAC Aggregation in Realistic Scenarios -- 5 Beyond Goodput as Evaluation Metric -- 5.1 Average Delay Until Authentication -- 5.2 Performance and Memory Overhead -- 5.3 Resilience to Adversarial Interference -- 6 Guidelines on Employing MAC Aggregation -- 6.1 When to Use MAC Aggregation on Lossy Channels? -- 6.2 How to Employ MAC Aggregation on Lossy Channels? -- 6.3 Selecting an MAC Aggregation Scheme -- 7 Conclusion -- References -- DoSat: A DDoS Attack on the Vulnerable Time-Varying Topology of LEO Satellite Networks -- 1 Introduction -- 2 Background and Related Work -- 2.1 LEO Satellite Network -- 2.2 Denial-of-Service Attack -- 2.3 LSN Simulation -- 3 DoSat Attack -- 3.1 Threat Model -- 3.2 Feasibility Analysis -- 3.3 DoSat Overview -- 3.4 Attack Mechanism -- 4 Simulation and Evaluation -- 4.1 Simulation Setup -- 4.2 Network Setup -- 4.3 Evaluation Metrics -- 4.4 Results -- 5 Mitigations -- 6 Conclusion -- References -- DDoSMiner: An Automated Framework for DDoS Attack Characterization and Vulnerability Mining -- 1 Introduction -- 2 Background and Related Work -- 2.1 TCP-Based DDoS Attacks -- 2.2 DDoS Mining/Exploit Schemes -- 2.3 Exploration TCP Stack with Symbolic Execution -- 3 Threat Model and Problem Definition -- 3.1 Threat Model -- 3.2 Problem Definition -- 4 Workflow of DDoSMiner -- 4.1 Generation of Attack Call Flow Graph -- 4.2 Selective Symbolic Execution -- 5 Evaluation -- 5.1 Experiment Configuration -- 5.2 Attack Call Flow Graph Analysis -- 5.3 Symbol Execution Experiment Setup -- 5.4 Symbolic Execution Results -- 5.5 Evasion Evaluation Against IDS -- 6 Conclusion -- 7 Limitations and Future Work -- A  Visualization and Analysis of System Calls -- B  The "]
[{"$nulls":4},"Kernel Address Corresponding to the Full Drop Nodes for Six Categories of Attacks -- References -- Privacy and Homomorphic Encryption."]
[{"$nulls":4},"Memory Efficient Privacy-Preserving Machine Learning Based on Homomorphic Encryption."]