Cover Image

Evaluating PQC KEMs, Combiners, and Cascade Encryption via Adaptive IND-CPA Testing Using Deep Learning

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Calderon, Simon, Johansson, Niklas, Günlü, Onur
Format:	Preprint
Published:	2026
Subjects:	Cryptography and Security Information Theory Machine Learning Neural and Evolutionary Computing Signal Processing
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Homomorphic Encryption Framework for Privacy-Preserving Spiking Neural Networks
by: Nikfam, Farzad, et al.
Published: (2023)

Hybrid Deep Learning Model for Multiple Cache Side Channel Attacks Detection: A Comparative Analysis
by: Joshi, Tejal, et al.
Published: (2025)

A Systematic Evaluation of Evolving Highly Nonlinear Boolean Functions in Odd Sizes
by: Carlet, Claude, et al.
Published: (2024)

Privacy-Preserving Distributed Optimization Under Time Constraints Using Secure Multi-Party Computation and Evolutionary Algorithms
by: Gruber, Sebastian, et al.
Published: (2026)

A Systematic Study on the Design of Odd-Sized Highly Nonlinear Boolean Functions via Evolutionary Algorithms
by: Carlet, Claude, et al.
Published: (2025)

Privacy-preserving Continual Federated Clustering via Adaptive Resonance Theory
by: Masuyama, Naoki, et al.
Published: (2023)

Randomized Distributed Function Computation (RDFC): Ultra-Efficient Semantic Communication Applications to Privacy
by: Günlü, Onur
Published: (2026)

Monotone but Exciting: On Evolving Monotone Boolean Functions with High Nonlinearity
by: Carlet, Claude, et al.
Published: (2026)

Degree is Important: On Evolving Homogeneous Boolean Functions
by: Carlet, Claude, et al.
Published: (2025)

IDEM Enough? Evolving Highly Nonlinear Idempotent Boolean Functions
by: Carlet, Claude, et al.
Published: (2026)

Proof-of-Useful-Work Blockchain for Trustworthy Biomedical Hyperdimensional Computing
by: Wen, Jinghao, et al.
Published: (2022)

NegaBent, No Regrets: Evolving Spectrally Flat Boolean Functions
by: Carlet, Claude, et al.
Published: (2026)

On Counts and Densities of Homogeneous Bent Functions: An Evolutionary Approach
by: Carlet, Claude, et al.
Published: (2025)

A Discrete Particle Swarm Optimizer for the Design of Cryptographic Boolean Functions
by: Mariot, Luca, et al.
Published: (2024)

Adapting Novelty towards Generating Antigens for Antivirus systems
by: Murali, Ritwik, et al.
Published: (2025)

Data Poisoning-based Backdoor Attack Framework against Supervised Learning Rules of Spiking Neural Networks
by: Jin, Lingxin, et al.
Published: (2024)

A systematic review of metaheuristics-based and machine learning-driven intrusion detection systems in IoT
by: Ahsan, Mohammad Shamim, et al.
Published: (2025)

Rectifying Adversarial Examples Using Their Vulnerabilities
by: Morimoto, Fumiya, et al.
Published: (2026)

Evasive Active Hypothesis Testing with Deep Neuroevolution: The Single- and Multi-Agent Cases
by: Stamatelis, George, et al.
Published: (2024)

Feedback Lunch: Learned Feedback Codes for Secure Communications
by: Zhou, Yingyao, et al.
Published: (2025)

Sequencing the Neurome: Towards Scalable Exact Parameter Reconstruction of Black-Box Neural Networks
by: Goldfeder, Judah, et al.
Published: (2024)

Adversarially Robust Spiking Neural Networks with Sparse Connectivity
by: Schmolli, Mathias, et al.
Published: (2025)

On the Adversarial Robustness of Spiking Neural Networks Trained by Local Learning
by: Lin, Jiaqi, et al.
Published: (2025)

MALT Powers Up Adversarial Attacks
by: Melamed, Odelia, et al.
Published: (2024)

Secret-Protected Evolution for Differentially Private Synthetic Text Generation
by: Wang, Tianze, et al.
Published: (2025)

Enhancing IoT Security: A Novel Feature Engineering Approach for ML-Based Intrusion Detection Systems
by: Mahanipour, Afsaneh, et al.
Published: (2024)

Impact of White-Box Adversarial Attacks on Convolutional Neural Networks
by: Podder, Rakesh, et al.
Published: (2024)

Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks
by: Poursiami, Hamed, et al.
Published: (2024)

Boosting Adversarial Robustness and Generalization with Structural Prior
by: Hou, Zhichao, et al.
Published: (2025)

Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
by: Poursiami, Hamed, et al.
Published: (2025)

Are Neuromorphic Architectures Inherently Privacy-preserving? An Exploratory Study
by: Moshruba, Ayana, et al.
Published: (2024)

Synchronization of Tree Parity Machines using non-binary input vectors
by: Stypiński, Miłosz, et al.
Published: (2021)

BrainLeaks: On the Privacy-Preserving Properties of Neuromorphic Architectures against Model Inversion Attacks
by: Poursiami, Hamed, et al.
Published: (2024)

Provable Unlearning with Gradient Ascent on Two-Layer ReLU Neural Networks
by: Melamed, Odelia, et al.
Published: (2025)

CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors
by: Yang, Guangmingmei, et al.
Published: (2024)

Secure Integrated Sensing and Communication Under Correlated Rayleigh Fading
by: Mittelbach, Martin, et al.
Published: (2024)

Optimizing Cyber Response Time on Temporal Active Directory Networks Using Decoys
by: Ngo, Huy Q., et al.
Published: (2024)

Refining Decision Boundaries In Anomaly Detection Using Similarity Search Within the Feature Space
by: Benabderrahmane, Sidahmed, et al.
Published: (2026)

Federated Learning with Quantum Computing and Fully Homomorphic Encryption: A Novel Computing Paradigm Shift in Privacy-Preserving ML
by: Dutta, Siddhant, et al.
Published: (2024)

Ranking-Enhanced Anomaly Detection Using Active Learning-Assisted Attention Adversarial Dual AutoEncoders
by: Benabderrahmane, Sidahmed, et al.
Published: (2025)