Computer Security - ESORICS 2025
Description
This four-volume set LNCS 16053-16056 constitutes the refereed proceedings of the 30th European Symposium on Research in Computer Security, ESORICS 2025, held in Toulouse, France, during September 22-24, 2025.
The 100 full papers presented in these proceedings were carefully reviewed and selected from 600 submissions. They were organized in topical sections as follows:
AI and Data-Centric Security, Systems and Hardware Security, Privacy, Cryptography and Secure Protocol Design, Blockchain and Financial Security, Privacy Policy and Identity Management, Adversarial and Backdoor Defenses.
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Content
.- Time-Distributed Backdoor Attacks on Federated Spiking Learning.
.- TATA: Benchmark NIDS Test Sets Assessment and Targeted Augmentation.
.- Abuse-Resistant Evaluation of AI-as-a-Service via Function-Hiding Homomorphic Signatures.
.- PriSM: A Privacy-friendly Support vector Machine.
.- Towards Context-Aware Log Anomaly Detection Using Fine-Tuned Large Language Models.
.- PROTEAN: Federated Intrusion Detection in Non-IID Environments through Prototype-Based Knowledge Sharing.
.- KeTS: Kernel-based Trust Segmentation against Model Poisoning Attacks.
.- Machine Learning Vulnerabilities in 6G: Adversarial Attacks and Their Impact on Channel Gain Prediction and Resource Allocation in UC-CF-mMIMO.
.- FuncVul: An Effective Function Level Vulnerability Detection Model using LLM and Code Chunk.
.- LUMIA: Linear probing for Unimodal and MultiModal Membership Inference Attacks leveraging internal LLM states.
.- Membership Privacy Evaluation in Deep Spiking Neural Networks.
.- DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?.
.- Countering Jailbreak Attacks with Two-Axis Pre-Detection and Conditional Warning Wrappers.
.- How Dataset Diversity Affects Generalization in ML-based NIDS.
.- Llama-based source code vulnerability detection: Prompt engineering vs Finetuning.
.- DBBA: Diffusion-based Backdoor Attacks on Open-set Face Recognition Models.
.- Evaluation of Autonomous Intrusion Response Agents In Adversarial and Normal Scenarios.
.- Trigger-Based Fragile Model Watermarking for Image Transformation Networks.
.- Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks.
.- On the Adversarial Robustness of Graph Neural Networks with Graph Reduction.
.- SecureT2I: No More Unauthorized Manipulation on AI Generated Images from Prompts.
.- GANSec: Enhancing Supervised Wireless Anomaly Detection Robustness through Tailored Conditional GAN Augmentation.
.- Fine-Grained Data Poisoning Attack to Local Differential Privacy Protocols for Key-Value Data.
.- The DCR Delusion: Measuring the Privacy Risk of Synthetic Data.
.- StructTransform: A Scalable Attack Surface for Safety-Aligned Large Language Models.