A Bayesian Incentive Mechanism for Poison-Resilient Federated Learning

Federated learning FL enables collaborative model training across decentralized clients while preserving data privacy. However, its open-participation nature exposes it to data-poisoning attacks, in which malicious actors submit corrupted model updates to degrade the global model. Existing defens...

CVE-2025-6377

A remote code execution security issue exists in the Rockwell Automation Arena®. A crafted DOE file can force Arena Simulation to write beyond the boundaries of an allocated object. Exploitation requires user interaction, such as opening a malicious file within the software. If exploited, a threa...

Rockwell Automation Arena 安全漏洞

Rockwell Automation Arena is a discrete-event simulation software developed by Rockwell Automation for a wide range of manufacturing, logistics, and service industries. A code execution vulnerability exists in Rockwell Automation Arena, which is caused by out-of-bounds writes to specially crafted...

A Locally Differential Private Coding-Assisted Succinct Histogram Protocol

A succinct histogram captures frequent items and their frequencies across clients and has become increasingly important for large-scale, privacy-sensitive machine learning applications. To develop a rigorous framework to guarantee privacy for the succinct histogram problem, local differential...

Watermarking LLM-Generated Datasets in Downstream Tasks

Large Language Models LLMs have experienced rapid advancements, with applications spanning a wide range of fields, including sentiment classification, review generation, and question answering. Due to their efficiency and versatility, researchers and companies increasingly employ LLM-generated da...

Efficient Blockchain-Based Steganography Via Backcalculating Generative Adversarial Network

Blockchain-based steganography enables data hiding via encoding the covert data into a specific blockchain transaction field. However, previous works focus on the specific field-embedding methods while lacking a consideration on required field-generation embedding. In this paper, we propose a...

Evaluation Empirique De La Sécurisation Et De L'Alignement De ChatGPT Et Gemini: Analyse Comparative Des Vulnérabilités Par Expérimentations De Jailbreaks

Large Language models LLMs are transforming digital usage, particularly in text generation, image creation, information retrieval and code development. ChatGPT, launched by OpenAI in November 2022, quickly became a reference, prompting the emergence of competitors such as Google's Gemini. However...

D2R: Dual Regularization Loss with Collaborative Adversarial Generation for Model Robustness

The robustness of Deep Neural Network models is crucial for defending models against adversarial attacks. Recent defense methods have employed collaborative learning frameworks to enhance model robustness. Two key limitations of existing methods are i insufficient guidance of the target model via...

Adapting under Fire: Multi-Agent Reinforcement Learning for Adversarial Drift in Network Security

Evolving attacks are a critical challenge for the long-term success of Network Intrusion Detection Systems NIDS. The rise of these changing patterns has exposed the limitations of traditional network security methods. While signature-based methods are used to detect different types of attacks, th...

Breaking the Gaussian Barrier: Residual-PAC Privacy for Automatic Privatization

The Probably Approximately Correct PAC Privacy framework 1 provides a powerful instance-based methodology for certifying privacy in complex data-driven systems. However, existing PAC Privacy algorithms rely on a Gaussian mutual information upper bound. We show that this is in general too...

A Certified Unlearning Approach without Access to Source Data

With the growing adoption of data privacy regulations, the ability to erase private or copyrighted information from trained models has become a crucial requirement. Traditional unlearning methods often assume access to the complete training dataset, which is unrealistic in scenarios where the...

Keeping an Eye on LLM Unlearning: the Hidden Risk and Remedy

Although Large Language Models LLMs have demonstrated impressive capabilities across a wide range of tasks, growing concerns have emerged over the misuse of sensitive, copyrighted, or harmful data during training. To address these concerns, unlearning techniques have been developed to remove the...

Weak-Jamming Detection in IEEE 802.11 Networks: Techniques, Scenarios and Mobility

State-of-the-art solutions detect jamming attacks ex-post, i.e., only when jamming has already disrupted the wireless communication link. In many scenarios, e.g., mobile networks or static deployments distributed over a large geographical area, it is often desired to detect jamming at the early...

CVE-2025-22561

Missing Authorization vulnerability in kbowson Title Experiments Free wp-experiments-free allows Exploiting Incorrectly Configured Access Control Security Levels.This issue affects Title Experiments Free: from n/a through = 9.0.4...

CVE-2025-22562

Cross-Site Request Forgery CSRF vulnerability in kbowson Title Experiments Free wp-experiments-free allows Cross Site Request Forgery.This issue affects Title Experiments Free: from n/a through = 9.0.4...

Privacy-Preserving Bathroom Monitoring for Elderly Emergencies Using PIR and LiDAR Sensors

In-home elderly monitoring requires systems that can detect emergency events - such as falls or prolonged inactivity - while preserving privacy and requiring no user input. These systems must be embedded into the surrounding environment, capable of capturing activity, and responding promptly. Thi...

Cryptanalysis of a Lattice-Based PIR Scheme for Arbitrary Database Sizes

Private Information Retrieval PIR schemes enable users to securely retrieve files from a server without disclosing the content of their queries, thereby preserving their privacy. In 2008, Melchor and Gaborit proposed a PIR scheme that achieves a balance between communication overhead and...

The vulnerability of software for discrete event simulation and automation in Rockwell Automation Arena arises from reading data beyond the acceptable range in memory. This allows attackers to exploit the protected information and execute arbitrary code.

The vulnerability of software for discrete event simulation and automation in Rockwell Automation Arena relates to reading data beyond the allowable range in memory. Exploiting this vulnerability can allow an attacker to disclose sensitive information and execute arbitrary code, provided that the...

The vulnerability of software for discrete event simulation and automation in Rockwell Automation Arena arises from reading data beyond the buffer boundaries in memory. This allows a hacker to execute arbitrary code.

The vulnerability of software for discrete event simulation and automation in Rockwell Automation Arena relates to reading data beyond the buffer boundaries in memory. Exploiting this vulnerability can allow an attacker to execute arbitrary code using a specially created DOE file...

The vulnerability of software for discrete event simulation and automation in Rockwell Automation Arena, related to errors during initialization of variables, allows a perpetrator to execute arbitrary code.

The vulnerability of the software for discrete event simulation and automation in Rockwell Automation Arena is related to errors during initialization of variables. Exploiting this vulnerability can allow an attacker to execute arbitrary code using a specially created DOE file...