:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Su, Xiaoyan, Zhu, Yinghao, Li, Run
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Artificial Intelligence Cryptography and Security Neural and Evolutionary Computing
Online Access:	https://arxiv.org/abs/2405.03712
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Graph Agent Network: Empowering Nodes with Inference Capabilities for Adversarial Resilience
by: Liu, Ao, et al.
Published: (2023)

Use of Graph Neural Networks in Aiding Defensive Cyber Operations
by: Mitra, Shaswata, et al.
Published: (2024)

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

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

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

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

Differential Privacy Regularization: Protecting Training Data Through Loss Function Regularization
by: Aguilera-Martínez, Francisco, et al.
Published: (2024)

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

Attacking the Spike: On the Transferability and Security of Spiking Neural Networks to Adversarial Examples
by: Xu, Nuo, et al.
Published: (2022)

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

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

Protecting Feed-Forward Networks from Adversarial Attacks Using Predictive Coding
by: Ganjidoost, Ehsan, et al.
Published: (2024)

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

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

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

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

Enhance the machine learning algorithm performance in phishing detection with keyword features
by: Yang, Zijiang
Published: (2025)

Bandwidth Reservation for Time-Critical Vehicular Applications: A Multi-Operator Environment
by: Al-Khatib, Abdullah, et al.
Published: (2025)

Probe-Geometry Alignment: Erasing the Cross-Sequence Memorization Signature Below Chance
by: Rupa, Anamika Paul, et al.
Published: (2026)

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

Evolutionary Multi-Objective Fusion of Deepfake Speech Detectors
by: Staněk, Vojtěch, et al.
Published: (2026)

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

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

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

Watermarking Neuromorphic Brains: Intellectual Property Protection in Spiking Neural Networks
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)

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)

BlackDAN: A Black-Box Multi-Objective Approach for Effective and Contextual Jailbreaking of Large Language Models
by: Wang, Xinyuan, et al.
Published: (2024)

Survival of the Safest: Towards Secure Prompt Optimization through Interleaved Multi-Objective Evolution
by: Sinha, Ankita, et al.
Published: (2024)

Do Spikes Protect Privacy? Investigating Black-Box Model Inversion Attacks in Spiking Neural Networks
by: Poursiami, Hamed, 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)

HoSNN: Adversarially-Robust Homeostatic Spiking Neural Networks with Adaptive Firing Thresholds
by: Geng, Hejia, et al.
Published: (2023)

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

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)

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

Temporal-Guided Spiking Neural Networks for Event-Based Human Action Recognition
by: Yang, Siyuan, et al.
Published: (2025)

Adversarial Robustness on Image Classification with $k$-means
by: Omari, Rollin, et al.
Published: (2023)

Enhancing Adversarial Robustness in SNNs with Sparse Gradients
by: Liu, Yujia, et al.
Published: (2024)