Detecting Sybil Addresses in Blockchain Airdrops: a Subgraph-Based Feature Propagation and Fusion Approach

Sybil attacks pose a significant security threat to blockchain ecosystems, particularly in token airdrop events. This paper proposes a novel sybil address identification method based on subgraph feature extraction lightGBM. The method first constructs a two-layer deep transaction subgraph for eac...

Securing WiFi Fingerprint-Based Indoor Localization Systems from Malicious Access Points

WiFi fingerprint-based indoor localization schemes deliver highly accurate location data by matching the received signal strength indicator RSSI with an offline database using machine learning ML or deep learning DL models. However, over time, RSSI values degrade due to the malicious behavior of...

FedRE: Robust and Effective Federated Learning with Privacy Preference

Despite Federated Learning FL employing gradient aggregation at the server for distributed training to prevent the privacy leakage of raw data, private information can still be divulged through the analysis of uploaded gradients from clients. Substantial efforts have been made to integrate local...

Input-Specific and Universal Adversarial Attack Generation for Spiking Neural Networks in the Spiking Domain

As Spiking Neural Networks SNNs gain traction across various applications, understanding their security vulnerabilities becomes increasingly important. In this work, we focus on the adversarial attacks, which is perhaps the most concerning threat. An adversarial attack aims at finding a subtle...

A Numerical Gradient Inversion Attack in Variational Quantum Neural-Networks

The loss landscape of Variational Quantum Neural Networks VQNNs is characterized by local minima that grow exponentially with increasing qubits. Because of this, it is more challenging to recover information from model gradients during training compared to classical Neural Networks NNs. In this...

Secure Cluster-Based Hierarchical Federated Learning in Vehicular Networks

Hierarchical Federated Learning HFL has recently emerged as a promising solution for intelligent decision-making in vehicular networks, helping to address challenges such as limited communication resources, high vehicle mobility, and data heterogeneity. However, HFL remains vulnerable to...

Analysis of the Vulnerability of Machine Learning Regression Models to Adversarial Attacks Using Data from 5G Wireless Networks

This article describes the process of creating a script and conducting an analytical study of a dataset using the DeepMIMO emulator. An advertorial attack was carried out using the FGSM method to maximize the gradient. A comparison is made of the effectiveness of binary classifiers in the task of...

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...

Performance of Machine Learning Classifiers for Anomaly Detection in Cyber Security Applications

This work empirically evaluates machine learning models on two imbalanced public datasets KDDCUP99 and Credit Card Fraud 2013. The method includes data preparation, model training, and evaluation, using an 80/20 train/test split. Models tested include eXtreme Gradient Boosting XGB, Multi Layer...

A Gradient-Optimized TSK Fuzzy Framework for Explainable Phishing Detection

Phishing attacks represent an increasingly sophisticated and pervasive threat to individuals and organizations, causing significant financial losses, identity theft, and severe damage to institutional reputations. Existing phishing detection methods often struggle to simultaneously achieve high...

Malicious code in arcus-gradient-colors (npm)

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MAL-2025-1736 Malicious code in arcus-gradient-colors (npm)

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MAL-2025-1735 Malicious code in arcus-gradient (npm)

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Malicious code in arcus-gradient (npm)

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GHSA-486G-47CC-8WXF aiocpa contains credential harvesting code

aiocpa is a user-facing library for generating color gradients of text. Version 0.1.13 introduced obfuscated, malicious code targeting Crypto Pay users, forwarding client credentials to a remote Telegram bot. All versions have been removed from PyPI...

Astra Linux – Vulnerability in LibreOffice

LibreOffice can import EMF+ graphics, which may be embedded in documents. A heap buffer overflow occurred when importing an EMF+ gradient brush. The number of gradient blend points was read from the file and used to compute an allocation size; however, this multiplication could lead to a buffer...

Microsoft LightGBM 安全漏洞

Microsoft LightGBM is a gradient boosting framework using a tree-based learning algorithm from Microsoft USA. A remote code execution vulnerability exists in Microsoft LightGBM. An attacker could exploit this vulnerability to execute arbitrary code on a system...

MAI-2024-0037

The Cross-Prompt Injection Attack XPIA can be significantly intensified by incorporating a Greedy Coordinate Gradient GCG suffix into the malicious payload. This technique enhances the probability that a Large Language Model LLM will execute the embedded instruction, even when a user's primary...

Malicious code in better-gradient (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 61ee3d2a1011f83d233eed4719b397bc9a4e69c449841335b187b323d9f980fc --- Category: MALICIOUS - The campaign has clearly malicious intent, like infostealers. Campaign: duvet-love-odyssey Reasons based on the campaign: - infosteal...

MAL-2024-9949 Malicious code in better-gradient (PyPI)

--- -= Per source details. Do not edit below this line.=- Source: kam193 61ee3d2a1011f83d233eed4719b397bc9a4e69c449841335b187b323d9f980fc --- Category: MALICIOUS - The campaign has clearly malicious intent, like infostealers. Campaign: duvet-love-odyssey Reasons based on the campaign: - infosteal...