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PE-MILCon:Multiple-Instance Learning with Contrastive Multi-View Representation for Static Windows PE Malware Detection 认领 引用
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作者 Tuan Nguyen Kim Son Doan Trung Nguyen Minh Nhut Pham 《Computers, Materials & Continua》 SCIE EI 2026年第10期1160-1180,共21页
Static Windows Portable Executable(PE)malware detection remains a significant challenge due to the growing use of packing,obfuscation,and code reuse techniques,which gradually reduce the effectiveness of signature-bas... Static Windows Portable Executable(PE)malware detection remains a significant challenge due to the growing use of packing,obfuscation,and code reuse techniques,which gradually reduce the effectiveness of signature-based and manually engineered feature approaches.Recent deep learning models that operate directly on binary code or static features have achieved encouraging results;however,most still rely on global file-level representations.Such approaches are susceptible to noise introduced by padding or obfuscation and may overlook localized malicious regions.Moreover,many multi-view methods process different feature sources independently,lacking mechanisms to enforce semantic consistency across views.This paper proposes PE-MILCon,a malware detection framework that integrates Multiple-Instance Learning(MIL)with contrastive multi-view representation learning across raw byte segments and structural PE features.In PE-MILCon,each executable file is modeled as a“bag”of byte segments.The attention mechanism within MIL enables the model to focus selectively on suspicious regions rather than treating the entire file uniformly.In parallel,structural and semantic PE features are encoded as a complementary view.A contrastive loss function aligns the two representations within a shared semantic space,enhancing robustness against obfuscation and packing techniques.The proposed framework operates entirely on static analysis and is trained end-to-end.Experiments on large-scale PE datasets under a strict time-based evaluation protocol show that PE-MILCon achieves an ROC-AUC above 0.98 and an F1-score of approximately 0.96,demonstrating competitive performance compared with existing models.In addition,instance-level attention weights provide intuitive indications of important code regions,supporting malware inspection and forensic analysis.These results suggest that PE-MILCon offers an effective,robust,and interpretable approach for static malware detection. 展开更多
关键词 Malware detection multiple-instance learning contrastive learning multi-view learning static analysis portable executable deep learning cybersecurity
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Multiple-Instance Learning with Instance Selection via Constructive Covering Algorithm 认领 引用 被引量：3
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作者 Yanping Zhang Heng Zhang +2 位作者 Huazhen Wei Jie Tang Shu Zhao 《Tsinghua Science and Technology》 SCIE EI CAS 2014年第3期285-292,共8页
Multiple-Instance Learning （MIL） is used to predict the unlabeled bags＇ label by learning the labeled positive training bags and negative training bags.Each bag is made up of several unlabeled instances.A bag is la... Multiple-Instance Learning （MIL） is used to predict the unlabeled bags＇ label by learning the labeled positive training bags and negative training bags.Each bag is made up of several unlabeled instances.A bag is labeled positive if at least one of its instances is positive,otherwise negative.Existing multiple-instance learning methods with instance selection ignore the representative degree of the selected instances.For example,if an instance has many similar instances with the same label around it,the instance should be more representative than others.Based on this idea,in this paper,a multiple-instance learning with instance selection via constructive covering algorithm （MilCa） is proposed.In MilCa,we firstly use maximal Hausdorff to select some initial positive instances from positive bags,then use a Constructive Covering Algorithm （CCA） to restructure the structure of the original instances of negative bags.Then an inverse testing process is employed to exclude the false positive instances from positive bags and to select the high representative degree instances ordered by the number of covered instances from training bags.Finally,a similarity measure function is used to convert the training bag into a single sample and CCA is again used to classification for the converted samples.Experimental results on synthetic data and standard benchmark datasets demonstrate that MilCa can decrease the number of the selected instances and it is competitive with the state-of-the-art MIL algorithms. 展开更多
关键词 multiple-instance learning instance selection constructive covering algorithm maximal Hausdorff
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