:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Huang, Cheng, Wang, Nannan, Wang, Ziyan, Sun, Siqi, Li, Lingzi, Chen, Junren, Zhao, Qianchong, Han, Jiaxuan, Yang, Zhen, Shi, Lei
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Cryptography and Security
Accesso online:	https://arxiv.org/abs/2403.08334
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence
di: Zhang, Junan, et al.
Pubblicazione: (2023)

Taint-Based Code Slicing for LLMs-based Malicious NPM Package Detection
di: Nguyen, Dang-Khoa, et al.
Pubblicazione: (2025)

JavaSith: A Client-Side Framework for Analyzing Potentially Malicious Extensions in Browsers, VS Code, and NPM Packages
di: Cohen, Avihay
Pubblicazione: (2025)

Attack Effect Model based Malicious Behavior Detection
di: Wang, Limin, et al.
Pubblicazione: (2025)

Malicious Package Detection using Metadata Information
di: Halder, S., et al.
Pubblicazione: (2024)

An Analysis of Malicious Packages in Open-Source Software in the Wild
di: Zhou, Xiaoyan, et al.
Pubblicazione: (2024)

One Detector Fits All: Robust and Adaptive Detection of Malicious Packages from PyPI to Enterprises
di: Montaruli, Biagio, et al.
Pubblicazione: (2025)

Towards Classifying Benign And Malicious Packages Using Machine Learning
di: Nguyen, Thanh-Cong, et al.
Pubblicazione: (2025)

Bridging Expert Reasoning and LLM Detection: A Knowledge-Driven Framework for Malicious Packages
di: Guo, Wenbo, et al.
Pubblicazione: (2026)

MalGuard: Towards Real-Time, Accurate, and Actionable Detection of Malicious Packages in PyPI Ecosystem
di: Gao, Xingan, et al.
Pubblicazione: (2025)

Cutting the Gordian Knot: Detecting Malicious PyPI Packages via a Knowledge-Mining Framework
di: Guo, Wenbo, et al.
Pubblicazione: (2026)

CHASE: LLM Agents for Dissecting Malicious PyPI Packages
di: Toda, Takaaki, et al.
Pubblicazione: (2026)

Leveraging Large Language Models to Detect npm Malicious Packages
di: Zahan, Nusrat, et al.
Pubblicazione: (2024)

PARIS: A Practical, Adaptive Trace-Fetching and Real-Time Malicious Behavior Detection System
di: Wang, Jian, et al.
Pubblicazione: (2024)

Malicious Agent Detection for Robust Multi-Agent Collaborative Perception
di: Zhao, Yangheng, et al.
Pubblicazione: (2023)

Unveiling Malicious Logic: Towards a Statement-Level Taxonomy and Dataset for Securing Python Packages
di: Ryan, Ahmed, et al.
Pubblicazione: (2025)

EagleEye: Attention to Unveil Malicious Event Sequences from Provenance Graphs
di: Gysel, Philipp, et al.
Pubblicazione: (2024)

CleanBase: Detecting Malicious Documents in RAG Knowledge Databases
di: Jin, Weifei, et al.
Pubblicazione: (2026)

FedMUA: Exploring the Vulnerabilities of Federated Learning to Malicious Unlearning Attacks
di: Chen, Jian, et al.
Pubblicazione: (2025)

FLAME: Flexible and Lightweight Biometric Authentication Scheme in Malicious Environments
di: Wang, Fuyi, et al.
Pubblicazione: (2025)

Malicious ML Model Detection by Learning Dynamic Behaviors
di: Nambiar, Sarang, et al.
Pubblicazione: (2026)

URL2Graph++: Unified Semantic-Structural-Character Learning for Malicious URL Detection
di: Tian, Ye, et al.
Pubblicazione: (2025)

USBIPS Framework: Protecting Hosts from Malicious USB Peripherals
di: Wang, Chun-Yi, et al.
Pubblicazione: (2024)

Malicious GenAI Chrome Extensions: Unpacking Data Exfiltration and Malicious Behaviours
di: Seetharam, Shresta B., et al.
Pubblicazione: (2025)

Benchmarking Safety Risks of Knowledge-Intensive Reasoning under Malicious Knowledge Editing
di: Mao, Qinghua, et al.
Pubblicazione: (2026)

Automatically Generating Rules of Malicious Software Packages via Large Language Model
di: Zhang, XiangRui, et al.
Pubblicazione: (2025)

Mind the Gap: Evaluating LLMs for High-Level Malicious Package Detection vs. Fine-Grained Indicator Identification
di: Ryan, Ahmed, et al.
Pubblicazione: (2026)

DySec: A Machine Learning-based Dynamic Analysis for Detecting Malicious Packages in PyPI Ecosystem
di: Mehedi, Sk Tanzir, et al.
Pubblicazione: (2025)

You Can Run But You Can't Hide: Runtime Protection Against Malicious Package Updates For Node.js
di: Ohm, Marc, et al.
Pubblicazione: (2023)

Tarallo: Evading Behavioral Malware Detectors in the Problem Space
di: Digregorio, Gabriele, et al.
Pubblicazione: (2025)

PhishSigma++: Malicious Email Detection with Typed Entity Relations
di: Shang, Shang, et al.
Pubblicazione: (2026)

Explainable and Transferable Adversarial Attack for ML-Based Network Intrusion Detectors
di: Zhang, Hangsheng, et al.
Pubblicazione: (2024)

IP-Augmented Multi-Modal Malicious URL Detection Via Token-Contrastive Representation Enhancement and Multi-Granularity Fusion
di: Tian, Ye, et al.
Pubblicazione: (2025)

Efficient and High-Accuracy Private CNN Inference with Helper-Assisted Malicious Security
di: Wang, Kaiwen, et al.
Pubblicazione: (2025)

MalModel: Hiding Malicious Payload in Mobile Deep Learning Models with Black-box Backdoor Attack
di: Hua, Jiayi, et al.
Pubblicazione: (2024)

Quantifying Psychological Sophistication of Malicious Emails
di: Longtchi, Theodore, et al.
Pubblicazione: (2024)

Invisible Hands: Gray-Box Bit Flip Attack for Steering LLMs Without Knowledge of Gradients, Data, and Weights
di: Almalky, Abeer Matar A., et al.
Pubblicazione: (2025)

SINCon: Mitigate LLM-Generated Malicious Message Injection Attack for Rumor Detection
di: Zhang, Mingqing, et al.
Pubblicazione: (2025)

From Past to Present: A Survey of Malicious URL Detection Techniques, Datasets and Code Repositories
di: Tian, Ye, et al.
Pubblicazione: (2025)

A Study on Malicious Browser Extensions in 2025
di: Singh, Shreya, et al.
Pubblicazione: (2025)