Imagen de Portada

Hybrid Deep Learning Model for Multiple Cache Side Channel Attacks Detection: A Comparative Analysis

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Joshi, Tejal, Kawalay, Aarya, Jamkhande, Anvi, Joshi, Amit
Formato:	Preprint
Publicado:	2025
Materias:	Cryptography and Security Neural and Evolutionary Computing
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

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

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

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

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

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

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

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

MALT Powers Up Adversarial Attacks
por: Melamed, Odelia, et al.
Publicado: (2024)

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

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

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

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

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

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

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

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

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

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

Time-Distributed Backdoor Attacks on Federated Spiking Learning
por: Abad, Gorka, et al.
Publicado: (2024)

From One Attack Domain to Another: Contrastive Transfer Learning with Siamese Networks for APT Detection
por: Benabderrahmane, Sidahmed, et al.
Publicado: (2025)

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

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

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

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

LLM Security: Vulnerabilities, Attacks, Defenses, and Countermeasures
por: Aguilera-Martínez, Francisco, et al.
Publicado: (2025)

LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm
por: Liu, Dazhuang, et al.
Publicado: (2024)

Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent
por: Kummer, Lorenz, et al.
Publicado: (2023)

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

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

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

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

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

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

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

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

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

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

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

Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs
por: Aguilera-Martínez, Francisco, et al.
Publicado: (2025)

RPG-AE: Neuro-Symbolic Graph Autoencoders with Rare Pattern Mining for Provenance-Based Anomaly Detection
por: Tauhid, Asif, et al.
Publicado: (2026)