Cover Image

Nebula: Self-Attention for Dynamic Malware Analysis

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Trizna, Dmitrijs, Demetrio, Luca, Biggio, Battista, Roli, Fabio
Format:	Preprint
Published:	2023
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Robust Synthetic Data-Driven Detection of Living-Off-the-Land Reverse Shells
by: Trizna, Dmitrijs, et al.
Published: (2024)

SLIFER: Investigating Performance and Robustness of Malware Detection Pipelines
by: Ponte, Andrea, et al.
Published: (2024)

Updating Windows Malware Detectors: Balancing Robustness and Regression against Adversarial EXEmples
by: Kozak, Matous, et al.
Published: (2024)

Quo Vadis: Hybrid Machine Learning Meta-Model based on Contextual and Behavioral Malware Representations
by: Trizna, Dmitrijs
Published: (2022)

secml-malware: Pentesting Windows Malware Classifiers with Adversarial EXEmples in Python
by: Demetrio, Luca, et al.
Published: (2021)

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
by: Angioni, Daniele, et al.
Published: (2024)

Regression-aware Continual Learning for Android Malware Detection
by: Ghiani, Daniele, et al.
Published: (2025)

Demystifying the Role of Rule-based Detection in AI Systems for Windows Malware Detection
by: Ponte, Andrea, et al.
Published: (2025)

ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
by: Pintor, Maura, et al.
Published: (2022)

Evaluating the Evaluators: Trust in Adversarial Robustness Tests
by: Cinà, Antonio Emanuele, et al.
Published: (2025)

Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
by: Cinà, Antonio Emanuele, et al.
Published: (2021)

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
by: Cinà, Antonio Emanuele, et al.
Published: (2024)

Label-efficient Training Updates for Malware Detection over Time
by: Minnei, Luca, et al.
Published: (2026)

Energy-Latency Attacks via Sponge Poisoning
by: Cinà, Antonio Emanuele, et al.
Published: (2022)

Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness
by: Piras, Giorgio, et al.
Published: (2024)

ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
by: Floris, Giuseppe, et al.
Published: (2023)

Sonic: Fast and Transferable Data Poisoning on Clustering Algorithms
by: Villani, Francesco, et al.
Published: (2024)

Empirical Quantification of Spurious Correlations in Malware Detection
by: Perasso, Bianca, et al.
Published: (2025)

Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De)Randomized Smoothing
by: Gibert, Daniel, et al.
Published: (2024)

Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
by: Lan, Tianwei, et al.
Published: (2025)

Evaluating Line-level Localization Ability of Learning-based Code Vulnerability Detection Models
by: Pintore, Marco, et al.
Published: (2025)

A Lean Transformer Model for Dynamic Malware Analysis and Detection
by: Quertier, Tony, et al.
Published: (2024)

On the Effectiveness of Adversarial Training on Malware Classifiers
by: Bostani, Hamid, et al.
Published: (2024)

Cluster Analysis and Concept Drift Detection in Malware
by: Mishra, Aniket, et al.
Published: (2025)

On Benchmarking Code LLMs for Android Malware Analysis
by: He, Yiling, et al.
Published: (2025)

Decoding Android Malware with a Fraction of Features: An Attention-Enhanced MLP-SVM Approach
by: Hakim, Safayat Bin, et al.
Published: (2024)

Unraveling the Key of Machine Learning-based Android Malware Detection
by: Liu, Jiahao, et al.
Published: (2024)

Applying Graph Analysis for Unsupervised Fast Malware Fingerprinting
by: Karbab, ElMouatez Billah, et al.
Published: (2025)

Understanding Malware Propagation Dynamics through Scientific Machine Learning
by: Pappu, Karthik, et al.
Published: (2025)

Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
by: Fu, Annan, et al.
Published: (2026)

DRMD: Deep Reinforcement Learning for Malware Detection under Concept Drift
by: McFadden, Shae, et al.
Published: (2025)

LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
by: Haque, Md Ahsanul, et al.
Published: (2025)

Comparative Analysis of Hash-based Malware Clustering via K-Means
by: Thein, Aink Acrie Soe, et al.
Published: (2025)

Multimodal Techniques for Malware Classification
by: Jiang, Jonathan, et al.
Published: (2025)

XAI and Android Malware Models
by: Kulkarni, Maithili, et al.
Published: (2024)

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks
by: Nasr, Milad, et al.
Published: (2025)

Machine Learning Security against Data Poisoning: Are We There Yet?
by: Cinà, Antonio Emanuele, et al.
Published: (2022)

Malware Detection based on API calls
by: Fellicious, Christofer, et al.
Published: (2025)

On the Robustness of Malware Detectors to Adversarial Samples
by: Salman, Muhammad, et al.
Published: (2024)

Quality-Assured Fuzz Harness Generation via the Four Principles Framework
by: Sheng, Ze, et al.
Published: (2026)