Malware Analysis

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifiers
May 18, 2026Gabriel Sauger, Jean-Yves Marion, Sazzadur Rahaman +3Adversarial Attacks Malware Classification
UL, CNRS, LORIA, Inria · ∗Universit´e de Lorraine, CNRS, LORIA, F-54000 Nancy, France · CNRS, UL, LORIA, Inria +2
FreeMOCA: Memory-Free Continual Learning for Malicious Code Analysis
May 10, 2026Zahra Asadi, Haeseung Jeon, Sohyun Han +3Class-Incremental Learning Mode Connectivity
Division of Artificial Intelligence & Software Ewha Womans University · Department of Computer Science University of Texas at El Paso
Diagnosing and Mitigating Domain Shift in Permission-Based Android Malware Detection
May 9, 2026Md Rafid IslamAndroid Malware Detection Cross-Domain Generalization
North South University, Department of Electrical and Computer Engineering, Bashundhara, Dhaka, 1229, Bangladesh
Beyond the Wrapper: Identifying Artifact Reliance in Static Malware Classifiers using TRUSTEE
May 7, 2026Riyazuddin Mohammed, Lan ZhangMalware Classification Malware Analysis
School of Informatics, Computing, and Cyber Systems
McNdroid: A Longitudinal Multimodal Benchmark for Robust Drift Detection in Android Malware
May 7, 2026Md Mahmuduzzaman Kamol, Jesus Lopez, Saeefa Rubaiyet Nowmi +5Concept Drift Android Malware Detection
Department of Computer Science, University of Texas at El Paso · CrowdStrike
LCC-LLM: Leveraging Code-Centric Large Language Models for Malware Attribution
May 7, 2026Christopher G. Pedraza Pohlenz, Hassan Jalil Hadi, Ali Hassan +1LLMs for Cybersecurity Malware Analysis
aCyberSaR, King Abdullah University of Science and Technology, Thuwal, 23955-6900, Makkah Province, Saudi Arabia
TUANDROMD-X: Advanced Entropy and Visual Analytics Dataset for Enhanced Malware Detection and Classification
May 7, 2026Parthajit Borah, Upasana Sarmah, D. K. Bhattacharyya +1Malware Classification Malware Analysis
Department of Computer Science and Engineering, Tezpur University, Tezpur, 784028, Assam, India. · Computer Science, College of Engineering and Applied Science, University of Colorado,Colorado Springs, CO 80933-7150, Colorado, USA.
Gray-Box Poisoning of Continuous Malware Ingestion Pipelines
May 6, 2026Jan Dolejš, Martin Jureček, Róbert LórenczAdversarial Robustness Malware Analysis
Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, Prague, 16000, Czech Republic
Trident: Improving Malware Detection with LLMs and Behavioral Features
Apr 30, 2026Rebecca Saul, Jingzhi Jiang, Elliott Chia +1Malware Classification LLMs for Cybersecurity
University of California, Berkeley Berkeley, California, USA
eDySec: A Deep Learning-based Explainable Dynamic Analysis Framework for Detecting Malicious Packages in PyPI Ecosystem
Apr 29, 2026Sk Tanzir Mehedi, Raja Jurdak, Chadni Islam +2Malware Classification Software Supply Chain Security
Queensland University of Technology · Edith Cowan University · Research Graduate School, BUBT
SeqShield: A Behavioral Analysis Approach to Uncover Rootkits
Apr 26, 2026Paras Ghodeshwar, Sandeep K Shukla, Anand Handa +1Malware Analysis
Department of Computer Science and Enginerring, Indian Institute of Technology, Kanpur, India · International Institute of Information Technology, Hyderabad, India · C3iHub Center, Indian Institute of Technology, Kanpur, India
Self-Supervised Learning for Android Malware Detection on a Time-Stamped Dataset
Apr 24, 2026Annan Fu, Hao Pei, Maryam TanhaConcept Drift Android Malware Detection
Khoury College of Computer Sciences Northeastern University Vancouver, Canada
Adversarial Malware Generation in Linux ELF Binaries via Semantic-Preserving Transformations
Apr 24, 2026Lukáš Hrdonka, Martin JurečekAdversarial Attacks Malware Classification
Faculty of Information Technology, Czech Technical University in Prague, Thákurova 9, Prague, Czech Republic
Detecting Concept Drift in Evolving Malware Families Using Rule-Based Classifier Representations
Apr 24, 2026Tomáš Kalný, Martin Jureček, Mark StampConcept Drift Malware Classification
Faculty of Information Technology Czech Technical University, Thakurova 9, Prague, Czech · Department of Computer Science, San Jose State University, San Jose, CA, USA
Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective
Apr 24, 2026Olha Jurečková, Martin Jureček, Matouš Kozák +1Adversarial Attacks Adversarial Robustness
Faculty of Information Technology, Czech Technical University in Prague, Th´akurova 9, Prague, 16000, Czech Republic
Generating Synthetic Malware Samples Using Generative AI
Apr 23, 2026Tiffany Bao, Kylie Trousil, Quang Duy Tran +2Class-Imbalanced Learning Synthetic Data Augmentation
Department of Computer Science, Boston University, Boston, MA 02215, USA · Department of Computer Science, University of Wisconsin–La Crosse, La Crosse, WI 54601, USA · Department of Computer Science, San José State University, San Jose, CA 95192, USA +2
Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations
Apr 23, 2026Pawan Acharya, Lan ZhangConcept Drift Adversarial Attacks
Northern Arizona University, Flagstaff, AZ, USA
Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks
Apr 22, 2026Nandakrishna Giri, Asmitha K. A., Serena Nicolazzo +2Randomized Smoothing Adversarial Attacks
Department of Computer Applications, Cochin University of Science and Technology, India · Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Via Adolfo Ferrata 5, 27100, Pavia, Italy
Evaluating Out-of-Distribution Robustness in Graph-Based Android Malware Classification: A New Principled Benchmark
Aug 8, 2025Ngoc N. Tran, Anwar Said, Waseem Abbas +2Distribution Shift Robustness Graph Representation Learning
Vanderbilt University, USA · Highmark Health, USA · University of Texas at Dallas, USA
Trust Under Siege: Label Spoofing Attacks against Machine Learning for Android Malware Detection
Mar 14, 2025Tianwei Lan, Luca Demetrio, Farid Nait-Abdesselam +2Android Malware Detection Malware Classification
1Université Paris Cité, France · University of Genova, Italy · 3INRIA, France +1
TIF: Learning Temporal Invariance in Android Malware Detectors
Feb 7, 2025Xinran Zheng, Shuo Yang, Edith C. H. Ngai +2Concept Drift Android Malware Detection
University College London, London, UK · The University of Hong Kong, Hong Kong SAR, China · Columbia University, New York, USA

May 18, 2026, cs.LGJ/K move · Enter open · S save

Attacking the First-Principle: A Black-Box, Query-Free Targeted Mimicry Attack on Binary Function Classifiers

Gabriel Sauger, Jean-Yves Marion, Sazzadur Rahaman, Victor Matrat+2

UL, CNRS, LORIA, Inria · ∗Universit´e de Lorraine, CNRS, LORIA, F-54000 Nancy, France · CNRS, UL, LORIA, Inria+2

Adversarial Attacks Malware Classification Binary Classification Black-Box Adversarial Attacks Malware Analysis

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Binary function classifiers play a crucial role in maintaining the security and integrity of software systems by detecting malicious code and unauthorized modifications. However, machine learning-based classifiers are vulnerable to adversarial attacks that can evade detection. In this study, we present Kelpie, a novel framework for executing mimicry attacks, a stronger type of targeted evasion attacks, on binary function classifiers in a black-box, zero-query setting. Unlike previous approaches that rely on querying the target classifier to refine untargeted evasion attacks, Kelpie leverages code transformations that preserve the functionality of malicious payloads while causing them to be misclassified as we want. Through extensive experimentation, we demonstrate that Kelpie can successfully execute mimicry attacks against six state-of-the-art binary function classifiers representing different model architectures without requiring direct interaction with them. We further validate our approach with a practical demonstration, involving a keylogger and a wiper concealed within benign-looking functions embedded in an application. This work, to our best knowledge, is the first to demonstrate such a mimicry attack in a black-box, zero-query context, raising important questions about the reliability and security of existing machine learning-based binary function classifiers.

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