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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In recent years, the research on malware variant classification has attracted much more attention. However, there are still many challenges, including the low accuracy of classification of samples of similar malware families, high time, and resource consumption. This paper proposes a new method of malware classification based on multiple visual features of malware and deep learning algorithms. In prior research, visualization techniques and entropy demonstrated exemplary performance in many areas. This paper extracts numerous visual features from the raw bytes and entropy sequence of the malware, which makes it more sensitive to malware samples of similar families and endows it the ability to classify malware variants more accurately. To evaluate the proposed method, this paper conducted a series of experiments on two malware datasets with a total of more than 20,000 samples provided by the Malware Research Lab and Microsoft Research. Through experiments, the method showed its superiority compared with some leading malware visual classification methods, achieving good performance on the accuracy with at least 1% improvement. The accuracy of the method even could reach 99.73% and 99.54%, respectively, on the two datasets.

Binary File’s Visualization and Entropy Features Analysis Combined with Multiple Deep Learning Networks for Malware Classification