Development of an Adapter for Analyzing and Protecting Machine Learning Models from Competitive Activity in the Networks Services

Due to the increasing number of tasks that are solved on remote servers, identifying and classifying traffic is an important task to reduce the load on the server. There are various methods for classifying traffic. This paper discusses machine learning models for solving this problem. However, su...

CVE-2025-30390 Azure ML Compute Elevation of Privilege Vulnerability

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LASHED: LLMs and Static Hardware Analysis for Early Detection of RTL Bugs

While static analysis is useful in detecting early-stage hardware security bugs, its efficacy is limited because it requires information to form checks and is often unable to explain the security impact of a detected vulnerability. Large Language Models can be useful in filling these gaps by...

Moodle Unauthorized Access

The vulnerability of the Moodle virtual learning environment’s web service is related to deficiencies in the authentication mechanism. Exploiting this vulnerability could allow a malicious actor, operating remotely, to gain unauthorized access to user data...

Bilateral Differentially Private Vertical Federated Boosted Decision Trees

Federated learning is a distributed machine learning paradigm that enables collaborative training across multiple parties while ensuring data privacy. Gradient Boosting Decision Trees GBDT, such as XGBoost, have gained popularity due to their high performance and strong interpretability. Therefor...

Whispers of Data: Unveiling Label Distributions in Federated Learning through Virtual Client Simulation

Federated Learning enables collaborative training of a global model across multiple geographically dispersed clients without the need for data sharing. However, it is susceptible to inference attacks, particularly label inference attacks. Existing studies on label distribution inference exhibits...

Graph Privacy: a Heterogeneous Federated GNN for Trans-Border Financial Data Circulation

The sharing of external data has become a strong demand of financial institutions, but the privacy issue has led to the difficulty of interconnecting different platforms and the low degree of data openness. To effectively solve the privacy problem of financial data in trans-border flow and sharin...

Security Bulletin: Several vulnerabilities affect Watson Machine Learning Accelerator on Cloud Pak for Data 5.0.0

Summary Several vulnerabilities in Watson Machine Learning Accelerator on Cloud Pak for Data 5.0.0 have been fixed in Watson Machine Learning Accelerator on Cloud Pak for Data 5.0 latest refresh. Vulnerability Details CVEID:CVE-2024-3568 DESCRIPTION: Hugging Face Transformers could allow a remote...

Security Bulletin: Apache Log4j vulnerability (CVE-2021-4422) addressed in IBM Watson Machine Learning Accelerator

Summary Apache Log4j, which is used by and included with IBM Watson Machine Learning Accelerator , contains security vulnerability issue CVE-2021-44228. This bulletin provides mitigations for the Log4Shell vulnaribility CVE-2021-44228 by applying workaround steps to IBM Watson Machine Learning...

AI-Based Crypto Tokens: the Illusion of Decentralized AI?

The convergence of blockchain and artificial intelligence AI has led to the emergence of AI-based tokens, which are cryptographic assets designed to power decentralized AI platforms and services. This paper provides a comprehensive review of leading AI-token projects, examining their technical...

Federated One-Shot Learning with Data Privacy and Objective-Hiding

Privacy in federated learning is crucial, encompassing two key aspects: safeguarding the privacy of clients' data and maintaining the privacy of the federator's objective from the clients. While the first aspect has been extensively studied, the second has received much less attention. We present...

Bipartite Randomized Response Mechanism for Local Differential Privacy

With the increasing importance of data privacy, Local Differential Privacy LDP has recently become a strong measure of privacy for protecting each user's privacy from data analysts without relying on a trusted third party. In many cases, both data providers and data analysts hope to maximize the...

Data Encryption Battlefield: a Deep Dive into the Dynamic Confrontations in Ransomware Attacks

In the rapidly evolving landscape of cybersecurity threats, ransomware represents a significant challenge. Attackers increasingly employ sophisticated encryption methods, such as entropy reduction through Base64 encoding, and partial or intermittent encryption to evade traditional detection...

ReCIT: Reconstructing Full Private Data from Gradient in Parameter-Efficient Fine-Tuning of Large Language Models

Parameter-efficient fine-tuning PEFT has emerged as a practical solution for adapting large language models LLMs to custom datasets with significantly reduced computational cost. When carrying out PEFT under collaborative learning scenarios e.g., federated learning, it is often required to exchan...

Network Attack Traffic Detection with Hybrid Quantum-Enhanced Convolution Neural Network

The emerging paradigm of Quantum Machine Learning QML combines features of quantum computing and machine learning ML. QML enables the generation and recognition of statistical data patterns that classical computers and classical ML methods struggle to effectively execute. QML utilizes quantum...

A Virtual Cybersecurity Department for Securing Digital Twins in Water Distribution Systems

Digital twins DTs help improve real-time monitoring and decision-making in water distribution systems. However, their connectivity makes them easy targets for cyberattacks such as scanning, denial-of-service DoS, and unauthorized access. Small and medium-sized enterprises SMEs that manage these...

Leveraging LLM to Strengthen ML-Based Cross-Site Scripting Detection

According to the Open Web Application Security Project OWASP, Cross-Site Scripting XSS is a critical security vulnerability. Despite decades of research, XSS remains among the top 10 security vulnerabilities. Researchers have proposed various techniques to protect systems from XSS attacks, with...

DeeCLIP: a Robust and Generalizable Transformer-Based Framework for Detecting AI-Generated Images

This paper introduces DeeCLIP, a novel framework for detecting AI-generated images using CLIP-ViT and fusion learning. Despite significant advancements in generative models capable of creating highly photorealistic images, existing detection methods often struggle to generalize across different...

JailbreaksOverTime: Detecting Jailbreak Attacks under Distribution Shift

Safety and security remain critical concerns in AI deployment. Despite safety training through reinforcement learning with human feedback RLHF 32, language models remain vulnerable to jailbreak attacks that bypass safety guardrails. Universal jailbreaks - prefixes that can circumvent alignment fo...

A Study on Mixup-Inspired Augmentation Methods for Software Vulnerability Detection

Various deep learning DL methods have recently been utilized to detect software vulnerabilities. Real-world software vulnerability datasets are rare and hard to acquire, as there is no simple metric for classifying vulnerability. Such datasets are heavily imbalanced, and none of the current...