Malware Classification

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1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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MARS: Malware Analysis with Rule-Based Scoring of LLM Claims
Oct 7, 2026Hyeongjun ChoiMalware Classification LLMs for Cybersecurity
78ResearchLab, 8, Yangpyeong-ro 25-gil, Yeongdeungpo-gu, Seoul, 07207, Republic of Korea
A Structured State Space Sequence Model for Multi-Class Classification of Malware
Oct 1, 2026Emmanuela Andam, Rana Shaaban, Emanuel Grant +1Cybersecurity Malware Classification
Artificial Intelligence Research (AIR) Center · School of Electrical Engineering and Computer Science, College of Engineering & Mines University of North Dakota, Grand Forks, ND 58201, USA
Concept Drift Detection and Adaptive Retraining of Malware Classification Models
Aug 13, 2026Christofer Washington Berruz Chungata, Martin Jurecek, Katerina Potika +2Concept Drift Malware Classification
Department of Computer Science, San Jose State University · Faculty of Information Technology, Czech Technical University in Prague
ShielDroid: A Hybrid Approach Integrating Machine and Deep Learning for Android Malware Detection
Aug 4, 2026Md Faisal Ahmed, Zarin Tasnim Biash, Abu Raihan Shakil +4Android Malware Detection Malware Classification
Department of Computer Science and Engineering Brac University Dhaka, Bangladesh
Latent Stability Analysis of Malware Representations Under Feature-Space Perturbations
Jul 27, 2026Bamidele Ajayi, Ken McGarryMalware Classification Adversarial Perturbations
School of Computer Science and Engineering, University of Sunderland, Sunderland, UK
Agentic SABRE: An Uncertainty-Aware Neuro-Symbolic Multi-Agent Framework for Adaptive Ransomware Detection
Jul 5, 2026Henry Kabuye, Biju Issac, Jeyamohan NeeraMalware Classification Neuro-Symbolic AI
School of Computer Science, Northumbria University, Newcastle Upon Tyne, UK
ThreatVisionAI: A Hybrid CNN-ViT Framework for Image-Based Malware Classification
Jul 4, 2026Allyson Taylor, Prashanth BusiReddyGariHybrid CNN-Transformer Architectures Malware Classification
Department of Mathematics & Computer Science University of North Carolina Pembroke
Hamm-Grams: An Algorithm for Mining Regular Expressions of Bytes
Jul 1, 2026Derek Everett, Edward Raff, James HoltMalware Classification Malware Analysis
Amazon, Baltimore, USA · CrowdStrike, Austin, USA
A Multi-task Mixture of Experts Framework for Malware Classification, Packing Detection, and Family Attribution
Jun 29, 2026Jithin S., Roshin Sleeba C., Anvin Mariya P. B. +4Multi-Task Learning Malware Classification
Department of Computer Applications, Cochin University of Science and Technology, India · Department of Electrical, Computer and Biomedical Engineering, University of Pavia, Italy
DroidBreaker: Practical and Functional Problem-Space Attacks on Machine-Learning Android Malware Detectors
Jun 25, 2026Christian Scano, Diego Soi, Angelo Sotgiu +5Adversarial Attacks Android Malware Detection
University of Cagliari · Sapienza University of Rome · CINI +1
Multi-View Decompilation for LLM-Based Malware Classification
Jun 18, 2026Bercan Turkmen, Vyas RainaSoftware Reverse Engineering LLM Prompting
Independent Researcher · SPARK
ViPER: Vision-based Packing-Aware Encoder for Robust Malware Detection
Jun 11, 2026Fatima Qaiser, Bisma Tahir, Muhammad Abid Mughal +1Malware Classification Malware Analysis
Department of Computer & Information Sciences, Pakistan Institute of Engineering & Applied Sciences (PIEAS), Islamabad, Pakistan
MOLOT System Card: Malicious Operational Logic Observation Transformer
Jun 5, 2026Daniil Lopatkin, Maksim Mitrofanov, Stanislav Rakovsky +1Static Code Analysis Malware Classification
Data Science & ML Dept. Positive Technologies False Positive Community
AMD-FCG: An Enhanced Function Call Graph Dataset with Integrated Topological Features for Malware Detection and Classification
Jun 5, 2026Parthajit Borah, Sakshi Singh, D. K. Bhattacharyya +1Malware Classification Malware Analysis
School of Cyber Security and Digital Forensics, National Forensic Sciences University,Guwahati. · 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.
High-Precision APT Malware Attribution with Out-of-Scope Resilience
Jun 2, 2026Peter Williams, Adam Sobey, Erisa KarafiliSelective Prediction Malware Classification
University of Southampton, Southampton, UK · The Alan Turing Institute, London, UK
A Hybrid Approach For Malware Classification Using Secondary Features Fusion
Jun 2, 2026Raja Khurram Shahzad, Muhammad Mustaqeem, Haroon ElahiMalware Classification Malware Analysis
Department of Computer and System Science, Mid Sweden University, Östersund Campus, Jämtland, Sweden. · Department of Engineering, Arbisoft (Pvt.) Ltd., Lahore, Punjab, Pakistan. · Department of Computing Science and Engineering, Chalmers University of Technology, Gothenburg, Västerbotten, Sweden.
Large Byte Model: Teaching Language Models About Compiled Code
Jun 1, 2026Florian Störtz, Catalin-Andrei Stan, Alexandru Dinu +4Malware Classification Byte-Level Language Model
CrowdStrike · CrowdStrike U.K. · CrowdStrike Romania +1
Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
May 25, 2026David Košťál, Martin JurečekAdversarial Attacks Malware Classification
Department of Information Security, Faculty of Information Technology, Czech Technical University in Prague
SEED: Semi-supervised Continual MalwarE Detection for Tackling ConcEpt Drift on a BuDget
May 24, 2026Suresh Kumar Amalapuram, Bikraj Shresta, Siva Ram murthy Chebiyam +2Concept Drift Continual Learning
Indian Institute of Technology Ropar, India · Indian Institute of Technology Hyderabad, India
Learning to Look Benign: Targeted Evasion of Malware Detectors via API Import Injection
May 18, 2026Juozas Dautartas, Olga Kurasova, Juozapas Rokas Čypas +1Adversarial Attacks Malware Classification
Institute of Data Science and Digital Technologies, Faculty of Mathematics and Informatics, Vilnius University, LT-08412 Vilnius, Lithuania
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
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
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.
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
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

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MARS: Malware Analysis with Rule-Based Scoring of LLM Claims

Hyeongjun Choi

78ResearchLab, 8, Yangpyeong-ro 25-gil, Yeongdeungpo-gu, Seoul, 07207, Republic of Korea

Malware Classification LLMs for Cybersecurity Malware Analysis LLM Evaluation

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Large language models can triage malware through direct verdicts or behavioral claims scored by an external policy. We present MARS, a malware triage framework, and compare direct classification with single-pass claim scoring using the same evidence collector and identical static evidence bundles for each model. The evaluation covers 1,195 PE and ELF binaries grouped into 1,001 near-duplicate clusters and six language models, with deterministic rules providing a baseline. Direct classification is more accurate for all six models. On samples with usable outputs from both paths, its accuracy advantage ranges from 3.7 to 20.9 percentage points, with all 95% cluster-bootstrap confidence intervals for the differences above zero. It also achieves higher malicious alert recall in ten of twelve platform and model combinations. Claim mediation provides no consistent reduction in performance variation across models. Separate subset studies find more consistent alert decisions for direct classification and a larger recall loss for the claim path when predefined indicator fields are removed. In a family identification probe, claims yield higher accuracy than verdict labels but lower accuracy than evidence text. Retained claims expose the inputs to verdict computation and permit policy revision without another model call. We reproduce archived verdicts exactly and apply a revised policy to the same records, including outputs from two additional models withdrawn by their provider. Under the evaluated claim taxonomy and additive policy, these results favor direct classification when only a verdict is required, while demonstrating that retained claims support explicit policy inspection and revision.

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