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| VirusShare is proud to have played a part in assisting the tireless efforts of the global research community and is thankful to the researchers who have contributed to the project. Below is an incomplete but growing list of publications that have cited VirusShare as a data source for their research. |
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| • 2026 • |
| 葛广凯, 张英杰， 韩龙玺，; (2026). 隐蔽特征推演下的广域物联网 APT 攻击本征立体识别算法. 微电子学与计算机. |
| Abdumalik, H; Hou, W; Zhuang, Y; Ali, MM; (2026). A two-stage deep learning approach for multiclass DGA-based malware communication detection and classification in DoH network traffic. Scientific Reports. |
| Abusabha, Omar; Uhm, Jiyong; Abuhmed, Tamer; Koo, Hyungjoon; (2026). A Deep Dive into Function Inlining and its Security Implications for ML-based Binary Analysis. Network and Distributed System Security Symposium. |
| Agarwal, G; Vasanth, N; Cho, S; (2026). VERA: A dual-validated, behaviorally-verified repository of 15,000+ raw windows ransomware binaries. 2026 35th. |
| Ahmed, Bakhan Tofiq; Jameel, Noor Ghazi M; Saeed, Bakhtiar Ibrahim; (2026). A Novel Hybrid Opcode Feature Selection Framework for Efficient and Effective IoT Malware Detection. IoT. |
| Akter, S; (2026). EFFICIENT RANSOMWARE DETECTION USING ENSEMBLE CLASSIFIERS. Journal of Integrated Sciences. |
| Al-Bayati, Tabarek K; Jawad, Lahieb Mohammed; (2026). A Multi-stage Android Malware Detection Framework Using GRU and Semi-Supervised Autoencoder. International journal of intelligent engineering and systems. |
| AL-Essa, Malik; Franchini, Felice; Galantucci, Stefano; Imran, Muhammad; Pirlo, Giuseppe; (2026). Improving robustness and explainability of PE malware classifiers using GAN-Generated Synthetic Adversarial examples. Cybersecurity. |
| Al-Obeidat, Feras N; Rashad, Muhammad Saad; Amin, Muhammad; Ali, Waqas; Khan, Bilal; Anwar, Sajid; (2026). Trustworthy and explainable malware threat intelligence through social media analytics and nature-inspired optimization. Array. |
| Alaeiyan; Lakzian; (2026). Malware detection via memory dump images: investigating the role of uneven kernel filters in CNNs with visual explainability. Journal of Computer Virology and Hacking Techniques, 22(1). Springer Science and Business Media LLC. |
| Aldawghan, Huda; Frikha, Mounir; (2026). Designing an Attack-Vector-Based Taxonomy for IoT Malware. International Journal of Advanced Computer Science and Applications. |
| AlMajali, Anas; Dweik, Waleed; Abdelghafur, Yahia; Alhasan, Obada; Tawalbeh, Omar; Amer, Moustafa; (2026). Detecting Ransomware Through Dynamic API Call Monitoring and Machine Learning. Applied Sciences. |
| Alqahtani, Abdullah; Ohemeng, Mordecai Opoku; Sheldon, Frederick T; (2026). An Intelligent Sensing Framework for Early Ransomware Detection Using MHSA-LSTM Machine Learning. Sensors. |
| ALVARADO, VERONICA LUCIA CHAMORRO; (2026). Clasificación de malware con características estáticas mediante algoritmos de aprendizaje automático. O2 - Repositori Institucional (Universitat Oberta de Catalunya). |
| Anand, A; Singh, JP; (2026). Smali Opcode-Based Android Malware Detection and Obfuscation Identification. Proceedings of the National Academy of Sciences. |
| Anas, Taqiyuddin; Ridzuan, Farida Hazwani Mohd; Pitchay, Sakinah Ali; Lim, Charles; (2026). Investigation of 0debug Stealer Using Assisted Static Analysis Method. Malaysian Journal of Science Health & Technology. |
| Andresini, Giuseppina; Appice, Annalisa; Belvedere, Vincenzo; Fiameni, Giuseppe; Malerba, Donato; (2026). Anakin: explainable android malware detection with graph neural networks. Cybersecurity. |
| Atef, A; Tammam, A; (2026). Comparative study on Ransomware Detection using Machine Learning. Journal of Advanced Research Design. |
| Atlam, HF; Jeevane, SM; (2026). HFS-SVE: A Hybrid Feature Selection and Soft Voting Ensemble for Android Malware Detection. Future Internet. |
| Azman, Aimaan; Firdaus, Ahmad; Darulhisham, Syafiq; Othman, Amir Muhammad Hafiz; Park, Taejune; Razak, Mohd Faizal Ab; (2026). VIRAN: Visual Ransomware Analysis. SSRN Electronic Journal. |
| Banik, Abhinandan; Singh, Jyoti Prakash; (2026). A BERT and PSO framework for Android malware detection using real permissions and API calls. Discover Computing. |
| Barethiya, Deepa; Lanjewar, Kajal; Mondhe, Damini; (2026). Machine Learning-Based Detection of Obfuscated Malware in Secure Computing Environments. Neliti. |
| Beg, Rijvan; Pateriya, Rajesh Kumar; Tomar, Deepak Singh; Solanki, Surendra; (2026). Design of an improved validation model with dynamic attribution and graph-based anomaly analysis for malware forensics. Discover Computing. |
| Beg, Rijvan; Nigam, Nikhil; Sharma, Yogesh Kumar; Patel, Amit; Solanki, Surendra; Sharma, Sukhwinder; Kumar, Lalit; (2026). Design of an integrated evidence-driven few-shot meta-learning for zero-day malware detection and forensic attributions. Scientific Reports. |
| Beg, Rijvan; Pateriya, Rajesh Kumar; Tomar, Deepak Singh; Raghuwanshi, Ghanshyam; Solanki, Surendra; (2026). Multimodal generative cyber forensics network for unified malware intelligence synthesis through temporal evidence alignment, neuro-symbolic hypergraph reasoning, and cognitive threat attributions. Scientific Reports. |
| Bhandary, Prajna; (2026). Behavioral Analysis of Ransomware in Active Directory Environments. |
| Brahimi, Bijied; Cohadon, Vincent; Glazman, Gabriel; Mohaize, Rayan Al; Berjawi, Omran; Khatoun, Rida; (2026). Delphi Scanner: efficient and interpretable static malware detection via API sequence modeling. arXiv (Cornell University). |
| Carlin, Domhnall; (2026). Dynamic analyses of malware. Research Portal (Queen's University Belfast). |
| Chauhan, Kshamta; Gandotra, Ekta; (2026). Android Malware Detection via Hybrid Static-Dynamic Analysis and Metaheuristic Feature Selection. International Journal of Engineering Trends and Technology. |
| Chow, Theo; D’Onghia, Mario; Linhardt, Lorenz; Kan, Zeliang; Arp, Daniel; Cavallaro, Lorenzo; Pierazzi, Fabio; (2026). Beyond the Tesseract: Trustworthy Dataset Curation for Sound Evaluations of Android Malware Classifiers. IEEE Conference on Secure and Trustworthy Machine Learning (SaTML). |
| Ciccaglione, Matteo; Caliandro, Pierciro; Pellegrini, Alessandro; (2026). A Formal Grammar Approach to Metamorphic Binary Transformation. |
| Cristea, EA; Molnes, P; Li, J; (2026). MalCVE: malware detection and CVE association using large language models. Proceedings of the 2026 IEEE/ACM Third. |
| Cvetković, Aleksandar Sandro; Stanišić, Snježana; Adamović, Saša; (2026). A Comparative Analysis of Static Feature Extraction Techniques for PE File Malware Detection using LightGBM and Gradient Boosting Variants. ZBORNIK RADOVA UNIVERZITETA SINERGIJA. |
| Davies, Simon R. H; Macfarlane, Richard; (2026). Comprehensive performance benchmarking and comparative analysis of active ransomware threats. Computers & Electrical Engineering. |
| Dawood, ZS; Abbas, AH; (2026). Adaptive malware detection under concept drift: an evolutionary deep learning framework with GA-based retraining. |
| Dolejš, Jan; Jureček, Martin; Lórencz, Róbert; (2026). Gray-Box Poisoning of Continuous Malware Ingestion Pipelines. Lecture notes in computer science. |
| El-Hajj, M; Zeineddine, MAJ; (2026). Advanced behavioral malware detection: a comprehensive MLOps framework with federated learning and real-time drift detection. Frontiers in Artificial Intelligence. |
| Elgarduh, Anis; Zainal, Anazida; Ghaleb, Fuad Abdulgaleel Abdoh; Qasem, Sultan Noman; Albarrak, Abdullah M; Saeed, Faisal; (2026). FBAM: A Frequency-Based Attention Mechanism for Enhanced Image-Based Malware Detection. Computer Modeling in Engineering & Sciences. |
| Elgarduh, Anis; Zainal, Anazida Binti; Ghaleb, Fuad Abdulgaleel Abdoh; Qasem, Sultan Noman; Albarrak, Abdullah M; Saeed, Faisal; (2026). Hybrid Feature Image Mapping (HFIM): A Multi-Channel Image Approach for Malware Detection Using Static and Dynamic Features. IEEE Access. |
| Eljamal, Abdelrahman; Hendawi, Abdeltawab; (2026). Bytes to Threats: Byte-Sequence Transformer For Malware Detection. IEEE International Conference on AI and Data Analytics (ICAD). |
| Ferdous, J; Islam, R; Mahboubi, A; Islam, MZ; (2026). RanDe: A Realistic Behavioral Dataset for Machine Learning-Based Ransomware Detection. Available at SSRN 7174144. |
| Ferdous, Jannatul; Islam, Rafiqul; Islam, Md Zahidul; (2026). Auditable Machine Unlearning for Privacy-Compliant Ransomware Detection Using Multi-Shard SISA and Deep Reinforcement Learning. arXiv (Cornell University). |
| Ferdous, Jannatul; Islam, Rafiqul; Mahboubi, Arash; Islam, Md Zahidul; (2026). DDQN-MLP: An Explainable and Adversarially Robust DRL-Guided Adaptive Learning Framework for Ransomware Detection. arXiv (Cornell University). |
| Ferdous, Jannatul; Islam, Rafiqul; Islam, Md Tauhidul; (2026). Privacy-Aware Machine Unlearning with SISA for Reinforcement Learning-Based Ransomware Detection. arXiv (Cornell University), 1278-1285. ACM. |
| Ferdous, Jannatul; Islam, Rafiqul; Islam, Md Zahidul; (2026). SA-DRL: Security-Aware Deep Reinforcement Learning for Ransomware Detection with Asymmetric Reward Design. arXiv (Cornell University). |
| Ferdous, Jannatul; Islam, Rafiqul; Mahboubi, Arash; Islam, Md Muhiminul; (2026). TL-RL-FusionNet: An Adaptive and Efficient Reinforcement Learning-Driven Transfer Learning Framework for Detecting Evolving Ransomware Threats. arXiv (Cornell University). |
| Ferdous, Jannatul; Islam, Rafiqul; Mahboubi, Arash; Islam, Md. Shofiqul; (2026). TL-RL-FusionNet: Reinforcement Learning-Guided Residual MLP with Fused CNN Embeddings for Efficient and Adaptive Ransomware Detection. Sensors. |
| Firdos, Rumman; Dangi, Aman; (2026). A Hybrid Multi–Layer Framework for Real–Time Malware and Phishing Detection Integrating Logistic Regression with External Threat Intelligence Validatio. CompSci & AI Advances. |
| Firdos, Rumman; Dangi, Aman; (2026). SecureScan: An AI-Driven Multi-Layer Framework for Malware and Phishing Detection Using Logistic Regression and Threat Intelligence Integration. arXiv (Cornell University). |
| Gao, Y; Xu, J; Zhao, X; Fang, X; (2026). Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method. arXiv preprint arXiv:2607.26634. |
| Giri, Nandakrishna; K.A., Asmitha; Nicolazzo, Serena; Nocera, Antonino; P, Vinod; (2026). Towards Certified Malware Detection: Provable Guarantees Against Evasion Attacks. arXiv (Cornell University). |
| González-Gorrín, Victor Manuel; Prieto, Josep; (2026). Metamorphic Malware Detection via Graph-Augmented Neural Semantics and Adversarial Hardening: A Comprehensive Framework. Journal of Cybersecurity and Privacy. |
| Gu, H; He, D; Zhou, X; (2026). Malicious Domain Detection on Out-of-Distribution Gray Data through Graph Contrastive Learning with Structure Aggregation. Proceedings of the 32nd ACM SIGKDD. |
| Gulmez, S; Kakisim, AG; Sogukpinar, I; (2026). Deep MCRan: Hybrid Analysis-Based Ransomware Detection Using Multi-Channel Data Fusion. IEEE Access. |
| Harish, Parikshieth; S, Ramesh P; C, Suganthan; (2026). 3D volumetric malware detection using morton curves and multi-channel semantic features. Scientific Reports. |
| Hayat, K; Hina, S; Hashmat, F; Husnain, M; (2026). Base Semantics—A Novel Approach for Minimizing Malware Detection Rules. Security and. |
| Haynie, Kaelyn; (2026). The Texture of a Threat: Adversarial Training, CNNs, and Obfuscated Malware Detection. Scholars Crossing (Liberty University). |
| Ishfaq, Hassan; Shah, Jamal Hussain; Saleem, Rabia; Afzal, Maira; (2026). A distributed framework for zero-day malware detection using federated ensemble models. PLoS ONE. |
| Jawad, M; Yassin, AA; Al-Asadi, HAA; (2026). Using Machine Learning Detection Malware in IoHT System. Vietnam Journal of. |
| Joshi; Hu; Di Troia; (2026). Integrating Opcode N-Grams and Word Embeddings for Enhanced Malware Classification: A Comparative Study with Transformer-Based Representations. Electronics, 15(17), 3969. MDPI AG. |
| Kale, Rahul; Wijayasiri, Thesath; Fok, Kar Wai; Thing, Vrizlynn L. L; (2026). HilEnT: Hilbert, Entropy Transformed Image Based Malware Detection. arXiv (Cornell University). |
| Karbab, ElMouatez Billah; (2026). Quantifiable Uncertainty: A Stochastic Consensus Multi-Agent RAG Framework for Robust Malware Detection. arXiv (Cornell University). |
| Karbab, EMB; (2026). AsmRAG: LLM-Driven Malware Detection by Retrieving Functionally Similar Assembly Code. arXiv preprint arXiv:2604.23196. |
| Karim, Khalid; (2026). Machine Learning-Based Malware Detection Using Behavioral Pattern Analysis for Enhanced Cybersecurity. Jurnal Teknik Informatika C I T Medicom. |
| Kolokythas, V; (2026). GPU-Accelerated Pattern Matching for Open Anti-Virus Engines. |
| Košťál, David; Jureček, Martin; (2026). Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation. arXiv (Cornell University). |
| Kumar, Vikash; Gupta, Yash; Soren, Yash; Kumar, Vikash; Mishra, Ms. Reeta; Tiwari, Vinayak; (2026). Dynamic Ransomware Detection using Behavioral Analysis. Zenodo (CERN European Organization for Nuclear Research). |
| Kurniawan, ED; Ariyanto, L; (2026). Analisis Deteksi Malware Menggunakan Pendekatan Static dan Dynamic pada Mendeley Dataset. Jutisi: Jurnal Ilmiah. |
| Kwee, Vincent Ferdinand; Windarta, Michael Aurelio; Putra, Vito Tantra; Nadia; Maulina, Ayu; (2026). Memetic Algorithm Approach in Code Caves for Detection Evasion. Procedia Computer Science. |
| Lei, T; (2026). LIGHTWEIGHT EXPLAINABLE AND FINE-GRAINED IOT MALWARE CLASSIFICATION. Journal of Nonlinear and Convex Analysis. |
| Liu, Jiahao; Zeng, Jun; Pierazzi, Fabio; Yang, Ziqi; Cavallaro, Lorenzo; Liang, Zhenkai; (2026). Unraveling the Key of Machine Learning-based Android Malware Detection. ACM Transactions on Software Engineering and Methodology. |
| Liu, Yi; Johar, Md Gapar Md; Tham, Jacquline; (2026). MLSTM: A GraphSAGE-based Android Malware Detection Method Integrating Mean Aggregation and LSTM. Informatica. |
| Liu, Yutong; Ning, Jianting; Feng, Qi; Zhang, Yanjun; Huang, Yujin; Zhang, Leo Yu; (2026). GRASP: Hard-Label Black-Box Malware Evasion with Higher Success, Fewer Queries, and Smaller Perturbations. International Joint Conference on Artificial Intelligence. |
| Luka, Kirollos Magdy; Abdelkader, Tamer; Naik, Kshirasagar; (2026). Generating adversarial malware using GANs to evade robust detectors. Engineering Research Express. |
| M., Sacristán, José; González-Tablas, Ana I; (2026). PRISM: PE Relational Inter-Section Matrix. A 2D Section-Aware Dataset for Static PE Malware Detection. arXiv (Cornell University). |
| Ma, M; Zhang, A; Bo¹, Y; (2026). MWC-CIL: A Weighted and Centroid-Based. … Conference on Intelligent Computing, ICIC 2026. |
| Makarem, Fadi; Chall, Rida El; Samhat, Abed Ellatif; Samrouth, Khouloud; Bakir, Nader; (2026). Exploiting Vision Transformer and Ensemble Learning for Advanced Malware Classification. Engineering Reports. |
| Mamatha, Bondugula; Ramesh, D; (2026). Lightweight Malware Detection In Smartphones Through Predictive Analytics, Feature Engineering, And Hybrid Optimization Technique. International Journal of Artificial Intelligence and Machine Learning. |
| Mammadov, Kanan; Kishiyeva, Nubar; (2026). Malware Detection in Modern Systems. InterConf. |
| mangala, mallikarjuna; Mangapoti, Maneedeep; (2026). Real-Time Ransomware Detection Using Visual Analytics and Adaptive Machine Learning. Research Digest on Engineering Management and Social Innovations. |
| Manh, Vu Minh; Xuan, Cho Do; Van, Nguyen Thi Khanh; (2026). HybFusion: A holistic Android malware detection framework with advanced feature fusion and ensemble learning. PLoS ONE. |
| Mani, A; Bavanitha, S; Anitha, C S; Aishwary, K; Varsha, H Vidhya; (2026). Machine Learning for Analysing Malware and Ransomware. International Journal of Engineering & Extended Technologies Research. |
| Manzoor, D; (2026). A Machine-Learning-Powered Real-Time SIEM Framework with Distributed Endpoint Agents and Swarm-Optimized PE Malware Detection. |
| Maqsood, A; Mirza, HT; Akram, A; (2026). Improving malware detection through enhanced model interpretability in hybrid environment. Chinese Journal of. |
| Masudome, Ayumu; Ban, Tao; Takahashi, Takeshi; Lin, Tsung-Nan; Morikawa, Tomohiro; (2026). Deep-Learning-Powered Stacked Generalization for Robust and Effective Android Malware Detection. SSRN Electronic Journal. |
| McLaren, Peter; (2026). Investigations into Decrypting Live Secure Traffic in Virtual Environments. Edinburgh Napier Research Repository (Edinburgh Napier University). |
| MENON, A GIRISH; Honnavalli, PB; (2026). Reinforcing Aircraft Network Security Using YARA Rules and Machine Learning for Cyber Threat Detection and Prevention. … and Honnavalli, Prasad. |
| Mohammad, RM; (2026). Android malware detection using a novel binary Firefly Bat feature selection algorithm. Information Security Journal: A Global. |
| Mondal, A; Soni, S; Satpute, S; (2026). BitProbe: A Unified Tool for Automated Malware Analysis and Live Forensic Triage on Windows. |
| Montaruli, Biagio; Oliveri, Andrea; Dambra, Savino; Balzarotti, Davide; (2026). The Role of Domain-Specific Features in Malware Detection: A macOS Case Study. ACM Asia Conference on Computer and Communications Security (ASIA CCS). |
| Musiiwa, E; Chindipha, SD; Moyo, M; (2026). CerebRAN: A Dynamic Behavioural Dataset for Ransomware Detection. |
| Muzaffar, Ali; Hassen, Hani Ragab; Zantout, Hind; Lones, Michael A; (2026). Reassessing feature-based Android malware detection in a contemporary context. PLoS ONE. |
| Niranjan, B; Jasmine, KS; (2026). An Autonomous Self-Evolving Malware Detection System for Real-Time Threat Intelligence. |
| PARGI, BHAVIK; Degadwala, Dr. Sheshang; Joshi, Malini; (2026). Hybrid Malware Analysis using Static and Dynamic Techniques with Machine Learning. International Journal of Scientific Research in Science Engineering and Technology. |
| Patel¹, UA; Narayan, P Lakshmi; Goel¹, S; (2026). An Explainable and Robust Machine Learning Framework for Polymorphic Malware Detection. Proceedings of the. |
| Petingola, M; Charland, P; Ding, SHH; (2026). Supplier-Independent Capability Verification of Software and Firmware with AI-Driven ATT&CK Mapping. 2026 18th International. |
| Posada, Ariel; Sepulveda, Juan Nicolás; Alférez, Santiago; López, Daniel Orlando Díaz; (2026). Using Generative Adversarial Networks (GAN) to detect Cyber Threats. Repositorio Institucional E-DocUR (Universidad Del Rosario). |
| Purwadi, Purwadi; Timur, Muhammad Bagus Bintang; Indra, Indra; (2026). Pendekatan Otomatis dalam Pembentukan Signature Metadata Malware untuk Deteksi Ancaman Siber. Dinamik. |
| R, Mrs. Amritha; U., Shashank Gowda; C., Rudresh S; R., Sanketh Kumar K; Darshan, K R; (2026). DroidSentry: A Survey of Android Malware Dynamic Analysis Techniques. International journal of research and scientific innovation. |
| Rahman, Md Mashfiquer; Rahman, Md Mosiur; Nahar, Sharmin; Rahman, Md Mostafijur; Rahman, Md Mostafijur; Hossain, Md Shahadat; (2026). Deep Learning Approach for Malware Classification and Threat Intelligence in Hospital Management. Global Clinical Engineering Journal. |
| Raju, Manthena; M, Krishnakartik; (2026). Enhancing Cyber security through Machine Learning-Based Intrusion Detection and Malware Classification. International Journal For Multidisciplinary Research. |
| Rana, Deepak Singh; Dimri, Sushil Chandra; (2026). A Comparative Evaluation of Malware Families and Machine-Learning Detection Techniques, and an Optimized Stacked-Ensemble Model for Predicting Software Maliciousness. International Journal of Computer Information Systems and Industrial Management Applications. |
| Ren, Ying; Liu, Ziyu; Wang, Junbo; Wang, P; (2026). Leveraging intelligent multimodal fusion for few-shot malware classification. Intelligence & Robotics. |
| Sabbah, A; Kharma, MF; Jarrar, R; Zein, S; (2026). Empirical Evaluation and New Insights of Concept Drift in ML-Based Android Malware Detection. … of Cybersecurity and. |
| Sabbah, A; Jarrar, R; Zein, S; (2026). Understanding concept drift with deprecated permissions in Android malware detection. IEEE Transactions on. |
| Samuel, D. Abhisheik, G. Hema Sri Latha, L; (2026). DYNAMIC ANALYSIS OF MALWARE USING ARTIFICIAL NEURAL NETWORKS WITH MACHINE LEARNING. International Journal of Pharmacy with Medical Sciences. |
| Sang, Anyuan; Yang, Li; Zhou, Lu; Jia, Junbo; Yang, Huipeng; (2026). Flash: Query-Efficient Black-Box Static Malware Evasion through Transferable GAN-Guided Modification Sequences. Proceedings of the ACM on software engineering. |
| Sarfaraj, RA; Gond, BP; Shahnawaz, M; (2026). A Novel Deep Learning Framework with Behavioral Heatmap, for Malware Classification. … on Intelligent and. |
| Saxena, Maheep; Das, Tanurup; (2026). Hierarchical malware detection, family identification, and variant attribution using CNN-based hybrid models on grayscale executable images. Scientific Reports. |
| Saxena, Maheep; Das, Tanurup; (2026). Multi-level malware subfamily attribution from RGB executable images using attention-augmented deep learning. Scientific Reports. |
| Shah, Vedant; Troia, Fabio Di; (2026). Autonomous Online Malware Labelling: Integrating Novelty Detection and Dynamic Clustering for Zero-Day Defense. Research Square. |
| Sharma, A; (2026). Hybrid Machine Learning Framework for Malware Detection using Static and Dynamic Analysis. |
| Sharma, Yashovardhan; (2026). A TTP by TTP Approach: Precise Malware Detection via Malicious TTP Recognition. arXiv (Cornell University). |
| Sharmila, SP; Tiwari, A; (2026). PDFInspect: A unified feature extraction framework for malicious document detection. 2026 18th International Conference on. |
| Shinde, Snehal R; (2026). Predictive Moving Target Defense for Ransomware Using Temporal Transformers and Uncertainty-Aware Risk Estimation. Journal of Propulsion Technology. |
| Shukla, T; Mishra, N; (2026). TLECNN: A Transfer Learning-Enhanced CNN for Malware-Family Classification in IoT-Relevant Security Environment. Electronics. |
| Smyth, Peter William; (2026). A Static, Dynamic and Memory Analysis Process for Outputting Fileless Malware Prevention Software Requirement. University Of Wales Trinity Saint David Research Repository (University of Wales Trinity Saint David). |
| Song, W; Yang, J; (2026). Semantic Weight-Aware Cuckoo Filter. Proceedings of the 2026 9th International Conference. |
| Song, Wenjia; Karanam, Sanjula; Xiao, Ya; Qi, Jingyuan; Dautenhahn, Nathan; Meng, Na; Ferrari, Elena; Yao, Danfeng Daphne; (2026). Crypto-Ransomware and Their Defenses: In-Depth Behavioral Characterization, Discussion of Deployability, and New Insights. ACM Computing Surveys. |
| Srinivas, G; Rajesh, M.V; (2026). Android Malware Detection using Deep Learning based Feature Fusion and Ensemble Learning Models through Image Representations. International Journal of Engineering Trends and Technology. |
| Tan, R; Hu, J; Xue, L; Zhang, X; Zhao, Q; (2026). Shalton: Cross-Runtime Static Analysis for Android Applications. ACM Transactions on. |
| Tegegn, Michael; Rubin, Julia; (2026). The Illusion of Success: Learning-Based Android Malware Detectors (Replicability Study). Proceedings of the ACM on software engineering. |
| Thakur, K; Ali, ML; Schmeelk, S; Debello, J; Rahman, MM; (2026). Real-time ransomware detection using reinforcement learning agents. Information. |
| Touili, T; Zhangeldinov, O; (2026). CARET Model Checking of Self Modifying Code. … Symposium on Theoretical Aspects of Software. |
| Tran, BQ; Tran, QD; Nguyen, AH; Pan, Z; Dang, T; (2026). Fully Quantum Latent Diffusion for Synthetic Malware Records: Effective Denoising, Limited Class Information, and Costly Post-Selection. arXiv preprint arXiv. |
| Uzodinma, V. C; (2026). Leveraging Application Programming Interface (API) Call Patterns for Real-Time Dynamic Malware Detection Using Deep Learning. INTERNATIONAL JOURNAL OF COMPUTER SCIENCE AND MATHEMATICAL THEORY E-ISSN. |
| Wang, Cheng; Wu, Guangjun; Li, Shuhao; Tang, Zixian; Gao, Fei; Qin, Su‐Juan; Tu, Tengfei; Li, Jishuai; (2026). RTCatch: a real-time detection method to combat evolving rust language ransomware. Cybersecurity. |
| Wang, L; Cai, J; (2026). Cross-Platform Malware Fingerprint Identification with a Convolutional KAN–Transformer Hybrid Architecture. Applied Sciences. |
| Xia, Lin; Chen, Yuanhe; Han, Lin; (2026). A deep learning-based IoT malware detection approach for electric vehicle charging stations. Scientific Reports. |
| Yang, Jieshuai; Wang, Zhi; Jia, Yan; Wu, Zhenhua; Tang, Jianfei; Su, Chenbin; Ye, Jingwei; Tian, Jianwen; Li, Wanpeng; (2026). Breaking the Black Box: Byte-Level Boundary Inference of Real-World Antivirus Systems. arXiv (Cornell University). |
| Yang, Jieshuai; Wang, Zhi; Jia, Yan; Wu, Zhenhua; Tang, Jianfei; Su, Chenbin; Ye, Jingwei; Tian, Jianwen; Li, Wanpeng; (2026). Signature Knowledge Shielding: Protecting Antivirus Detection Knowledge through Robust Embedding Matching. arXiv (Cornell University). |
| Yang, Jieshuai; Wang, Zhi; Jia, Yan; Wu, Zhenhua; Tang, Jianfei; Su, Chenbin; Ye, Jingwei; Tian, Jianwen; Li, Wanpeng; (2026). Weaponizing Ground Truth: Data Poisoning Attacks by Exploiting Boundary Misalignment Between Antivirus Software and Learning-Based Detectors. arXiv (Cornell University). |
| Yu, Hailong; Wang, Tiantian; Shar, Lwin Khin; Li, Hanmeng; Lo, David; (2026). Func: reducing the impact of Android framework evolution on malware detection. Empirical Software Engineering. |
| Zainab, Mashal; Dyrmishi, Salijona; Bostani, Hamid; Cavallaro, Lorenzo; Cordy, Maxime; (2026). Breaking Windows Malware Detection: A Comprehensive Evaluation of Problem-Space Adversarial Robustness. arXiv (Cornell University). |
| Zhan, YiLe; Zhao, YueAi; Liu, Meichen; (2026). Dynamic Mask-based Multimodal Fusion Method for PE Malware Detection. Research Square. |
| Zhang, H; Zhang, Y; Shi, Y; (2026). Mal-CTM-TL: Transferable API Behavior Representations via Self-Supervised Pretraining. |
| Zhang, Xiaoling; Sun, Jian; Wang, Dawei; Wang, Chongyu; Chen, Li; Yan, Zhaoteng; Liu, Peipei; Zhang, Lixiao; Li, Dan; (2026). Hieronym: Leveraging Hierarchical Multi-Source Information for Function Renaming in Stripped Binary. arXiv (Cornell University). |
| Zhang, Xuan; Zhang, Aihua; Ma, Maode; Bo, Yuanjie; Zhang, Yiying; Zhang, Yanan; (2026). ProtoMal: Prototype-Guided Dual-Branch Continual Learning for Robust Android Malware Detection. Algorithms. |
| Zhao, Erliang; Zhu, Z G; (2026). Hardware Performance Counter Analysis of Ransomware Behavior: Observed Inverse Correlations Across Heterogeneous x86 Platforms. Applied Sciences. |
| Zhao, Hanqing; Zhang, Yiming; Ying, Lingyun; Zhang, Mingming; Liu, Baojun; Duan, Haixin; You, Zi-Quan; Zhang, Shuhao; (2026). Understanding the Status and Strategies of the Code Signing Abuse Ecosystem. Network and Distributed System Security Symposium. |
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| • 2023 • |
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| • 2014 • |
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| 武田圭史; 露木航平; (2014). 実行ファイルのプロファイリングによる調査対象ファイル特定手法の提案. コンピュータセキュリティシンポジウム 2014 論文集, 2014(2), 519-526. |
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| • 2013 • |
| CERTH, Lead Beneficiary; PU, ITI Dissemination Level; (2013). Enhanced Network Security for Seamless Service Provisioning in the Smart Mobile Ecosystem. |
| Guri, Mordehai; Kedma, Gabi; Sela, Tom; Carmeli, Buky; Rosner, Amit; Elovici, Yuval; (2013). Noninvasive detection of anti-forensic malware. 2013 8th International Conference on Malicious and Unwanted Software:" The Americas"(MALWARE), 1-10. IEEE. |
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| Oktavianto, Digit; Muhardianto, Iqbal; (2013). Cuckoo malware analysis. Packt Publishing Ltd. |
| Pareek, Himanshu; Eswari, PRL; Babu, Sarat Chandra; (2013). APPBACS: AN APPLICATION BEHAVIOR ANALYSIS AND CLASSIFICATION SYSTEM. International Journal of Computer Science & Information Technology, 5(2), 53. Academy & Industry Research Collaboration Center (AIRCC). |
| Pareek, Himanshu; Eswari, PRL; Babu, N Sarat Chandra; (2013). Malicious PDF document detection based on feature extraction and entropy. International Journal of Security, Privacy and Trust Management, 2(5), 31-35. Academy and Industry Research Collaboration Center (AIRCC). |
| • 2012 • |
| Kedziora, Michal; Gawin, Paulina; Szczepanik, Michal; Jozwiak, Ireneusz; (2012). Android Malware Detection Using Machine Learning And Reverse Engineering. World Wide Web, 16(1). |
| Kozák, M; Jureček, M; Lórencz, R; (2012). Generation of adversarial malware and benign examples using reinforcement learning. Artificial Intelligence for Cybersecurity, 3-25. Springer Nature. |
| Published research using VirusShare data? Let us know at [email protected]. |
| Suggested citation: Roberts, J. M. (2011–). VirusShare: A repository of malware samples [Data set]. https://virusshare.com. https://doi.org/10.5281/zenodo.23675927 When possible, please name the samples (by cryptographic hash) or packages (e.g. VirusShare_00123) you used and the date you downloaded them, so that others can reproduce your research. |
@misc{virusshare,
author = {Roberts, J. M.},
title = {{VirusShare}: A Repository of Malware Samples},
year = {2011},
publisher = {Zenodo},
doi = {10.5281/zenodo.23675927},
url = {https://virusshare.com}
} |