English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Identification of the family to which a malware specimen belongs is essentialin understanding the behavior of the malware and developing mitigationstrategies. Solutions proposed by prior work, however, are often notpracticable due to the lack of realistic evaluation factors. These factorsinclude learning under class imbalance, the ability to identify new malware,and the cost of production-quality labeled data. In practice, deployed modelsface prominent, rare, and new malware families. At the same time, obtaining alarge quantity of up-to-date labeled malware for training a model can beexpensive. In this paper, we address these problems and propose a novelhierarchical semi-supervised algorithm, which we call the HNMFk Classifier,that can be used in the early stages of the malware family labeling process.Our method is based on non-negative matrix factorization with automatic modelselection, that is, with an estimation of the number of clusters. With HNMFkClassifier, we exploit the hierarchical structure of the malware data togetherwith a semi-supervised setup, which enables us to classify malware familiesunder conditions of extreme class imbalance. Our solution can performabstaining predictions, or rejection option, which yields promising results inthe identification of novel malware families and helps with maintaining theperformance of the model when a low quantity of labeled data is used. Weperform bulk classification of nearly 2,900 both rare and prominent malwarefamilies, through static analysis, using nearly 388,000 samples from theEMBER-2018 corpus. In our experiments, we surpass both supervised andsemi-supervised baseline models with an F1 score of 0.80.

Semi-supervised Classification of Malware Families Under Extreme Class  Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic  Model Selection