Malware Detection

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

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Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data
Oct 7, 2026Boaz Micah, Nadia Milazzo, Maissa Beji +8Quantum Kernel Methods Unsupervised Anomaly Detection
Multiverse Computing, Parque Científico y Tecnológico de Gipuzkoa, Paseo de Miramón 170, Planta 2, 20014 Donostia / San Sebastián, Spain · IQM Quantum Computers, 4 rue Royale, 75008 Paris, France · Multiverse Computing, 7 rue de la Croix Martre, 91120 Palaiseau, Paris, France +3
A Hybrid Approach to Malware Detection: Integrating Few-Shot Model-Agnostic Meta-Learning with Autoencoders
Oct 1, 2026Emmanuela Andam, Yasir Abbas Zaidi, Abdelali Hadir +2Meta-Learning Malware Detection
Artificial Intelligence Research (AIR) Center, University of North Dakota, Grand Forks, ND, USA · School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, ND, USA · National School of Commerce and Management, Hassan II University, Casablanca, Morocco
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
Temporal Generalization and Explanation Stability of Control Flow Graph Neural Networks for Malware Detection
Sep 21, 2026Md. Asif Sajeed, Md. Nazrul Islam Mondal, Md Ashraful Hossen AkashGraph Neural Networks Distribution Shift
Department of Computer Science and Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh
Delphi Scanner: efficient and interpretable static malware detection via API sequence modeling
Sep 17, 2026Bijied Brahimi, Vincent Cohadon, Gabriel Glazman +3Malware Detection
Université Paris Cité, Paris, France · Institut Polytechnique de Paris, Télécom Paris, Palaiseau, France
CAM-Guided Saliency Cutout and Image-Based Malware Classification
Aug 12, 2026Yasaman Ebrahimi, Martin Jurecek, Mark StampData Augmentation Malware Detection
Department of Computer Science, San Jose State University · Faculty of Information Technology, Czech Technical University in Prague
On the Performance of Malware Detection Classifiers Using Hardware Performance Counters
Aug 2, 2026Alireza Abolhasani Zeraatkar, Parnian Shabani Kamran, Inderpreet Kaur +3Malware Detection
ECE Department University of California Davis, United States
Behavioral Grammar: Detecting Adaptive Malware via Tiny Language Model Priors and Second-Order Temporal Analysis
Aug 1, 2026Zihan LuoUnsupervised Anomaly Detection Malware Analysis
Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method
Jul 29, 2026Yinan Gao, Jiarong Xu, Xiaohang Zhao +1Graph Representation Learning Malware Detection
School of Management, Fudan University · School of Information Management and Engineering, Shanghai University of Finance and Economics · Lerner College of Business and Economics, University of Delaware
EXE-Bench: Ranking the Tradeoffs of AI-based Windows Malware Detectors for Real-World Usability
Jul 27, 2026Andrea Ponte, Daniel Gibert, Matous Kozak +5Efficient Inference Adversarial Robustness
Department of Informatics, Bioengineering, Robotics and Systems Engineering, University of Genova, Italy · Artificial Intelligence Research Institute (IIIA-CSIC), Spain · Department of Information Security at Faculty of Information Technology, CTU in Prague, Czech Republic +2
Leveraging Interpretable Tsetlin Machine for PDF Malware Detection
Jul 10, 2026Rahul JaiswalMalware Detection
The Centre for Artificial Intelligence Research (CAIR) · Department of ICT, University of Agder, Norway
Detecting Ladder Logic Bombs in IEC 61131-3 PLC Programs using ESBMC-PLC+: A Formal Verification Approach with Trigger Synthesis
Jul 9, 2026Pierre Dantas, Lucas Cordeiro, Waldir JuniorCybersecurity Backdoor Detection
Electrical Engineering Federal University of Amazonas (UFAM) Manaus, AM, Brazil
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
LLM-Enhanced Hierarchical Heterogeneous Graph Representation Learning for Malicious Python Package Detection
Jul 3, 2026Hang Gao, Xiaoyu Chen, Baoquan Cui +4Heterogeneous GNNs Hierarchical Representation Learning
Institute of Software, Chinese Academy of Sciences · University of Chinese Academy of Sciences
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 Hybrid Framework For Crypto-Ransomware Detection In Enterprise Shared Storage
Jun 29, 2026Gervais Hatungimana, Abdun Naser Mahmood, Mohammad Jabed Morshed ChowdhuryNetwork Intrusion Detection Cybersecurity
Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC, Australia
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
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.
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.
ClawHub Security Signals: When VirusTotal, Static Analysis, and SkillSpector Disagree
May 31, 2026Vincent Koc, Patrick Erichsen, Jacob Tomlinson +3AI Agent Security Agent Skill Security Auditing
OpenClaw Foundation USA · NVIDIA United Kingdom · NVIDIA USA
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
Concept Drift Adaptation Using Self-Supervised and Reinforcement Learning In Android Malware Detection
May 22, 2026Ahmed Sabbah, Mohammad Kharma, Mohammad Alkhanafseh +3Reinforcement Learning Concept Drift
Birzeit University · University of Central Florida
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
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

Oct 7, 2026, quant-phJ/K move · Enter open · S save

Pareto-optimal quantum kernel selection for unsupervised anomaly detection on real malware beaconing data

Boaz Micah, Nadia Milazzo, Maissa Beji, Borja Aizpurua+7

Multiverse Computing, Parque Científico y Tecnológico de Gipuzkoa, Paseo de Miramón 170, Planta 2, 20014 Donostia / San Sebastián, Spain · IQM Quantum Computers, 4 rue Royale, 75008 Paris, France · Multiverse Computing, 7 rue de la Croix Martre, 91120 Palaiseau, Paris, France+3

Quantum Kernel Methods Unsupervised Anomaly Detection Multi-Objective Optimization Malware Detection

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Quantum kernel methods are leading candidates for a practical quantum advantage in machine learning, but assessing that potential requires two quantities usually reported separately: how well a kernel performs on the task, and how far its geometry departs from the classical kernels available for the same problem. We introduce a fully unsupervised, multi-objective protocol that optimises simultaneously the normalised pseudo discrepancy (NPD), a label-free proxy for anomaly detection quality, and the geometric difference (GD) to a tuned classical reference kernel, selecting models from the resulting Pareto front. We apply it to malware beaconing detection in real network traffic, using a one-class support vector machine with fidelity and projected quantum kernels over four data encodings, on simulators and on IQM's 20-qubit Garnet processor. NPD-guided selection alone finds a fidelity kernel that beats the tuned classical baseline, but with a geometric difference too small to certify the gain as quantum. Projected kernels reach far larger geometric differences; the Pareto-selected one only marginally exceeds the baseline (AUC

0.782

versus

0.765

g_{C\to Q}\approx 89>\sqrt{N}

relative to that reference kernel), still below the NPD-selected fidelity kernel (

0.840

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Latest papers 43