ConvProtoNet: Deep Prototype Induction towards Better Class Representation for Few-Shot Malware Classification

Traditional malware classification relies on known malware types and significantly large datasets labeled manually which limits its ability to recognize new malware classes. For unknown malware types or new variants of existing malware containing only a few samples each class, common classification methods often fail to work well due to severe overfitting. In this paper, we propose a new neural network structure called ConvProtoNet which employs few-shot learning to address the problem of scarce malware samples while prevent from overfitting. We design a convolutional induction module to replace the insufficient prototype reduction in most few-shot models and generates more appropriate class-level malware prototypes for classification. We also adopt meta-learning scheme to make classifier robust enough to adapt unseen malware classes without fine-tuning. Even in extreme conditions where only 5 samples in each class are provided, ConvProtoNet still achieves more than 70% accuracy on average and outperforms other traditional malware classification methods or existed few-shot models in experiments conducted on several datasets. Extra experiments across datasets illustrate that ConvProtoNet learns general knowledge of malware which is dataset-invariant and careful model analysis proves effectiveness of ConvProtoNet in few-shot malware classification.

MODEL PENGENTASAN KEMISKINAN TERINTEGRASI DI KOTA YOGYAKARTA

This research aim to find model reduction poorness of integrated in Yogyakarta testing having taken steps in three subdistrict that is Kricak, Tegalpanggung and Sorosutan . Research method by using research model and expansion with antecedent study stages; steps, expansion prototype , field test and semination product result of expansion. Antecedent study by doing in-depth interview and observation to poor family , and public figures and studies documents ( former research, institute report) which with reference to reduction poorness. Result of firststep study in the form of information of aspiration of poor family, the Government plan and Non Government Organization and research conclusion before all, utilized isn't it to compile expansion of model prototype reduction poorness. Step of hereinafter test prototype model reduction poorness of three sub-district Kricak, Tegalpanggung and Sorosutan to obtain empiric evidence about elegibility of execution process from model limitedly, either its(the subject and also aspects). Result of his its would in semination to some other sub-districts.Its the target is find enableness model of public in the form of expansion of model reduction poorness integrated, mean from various aspects ( economics, social, education, health, culture) and various element considered and its the execution is really can lessen number of poor families Kata Kunci: kemiskinan, Yogyakarta, integrasi